This repository is inspired by the remarkable work of Kevin Kaichuang Yang and their outstanding project Machine-learning-for-proteins. We have established this repository to provide a specialized and focused platform for the field of Deep Learning for Protein Design, a rapidly advancing domain in computational biology.
Contributions and suggestions are warmly welcome! Community Values, Guiding Principles, and Commitments for the Responsible Development of AI for Protein Design: details
Papers last week, updated on 2024.11.23:
- Alphafold2 refinement improves designability of large de novo proteins
- D-Flow: Multi-modality Flow Matching for D-peptide Design
- [arXiv:2411.10618] • [code]
- Multi-purpose controllable protein generation via prompted language models
- Design of facilitated dissociation enables control over cytokine signaling duration
- De novo design of epitope-specific antibodies against soluble and multipass membrane proteins with high specificity, developability,and function
0) Benchmarks and datasets
Sequence dataset/benchmarks •
Structure datasets/benchmarks •
Public database •
Similar list •
Guides
1) Reviews and surveys
De novo design •
Antibody design •
Peptide design •
Binder design •
Enzyme design
2) Model-based design
trRosetta-based •
AlphaFold2-based •
DMPfold2-based •
CM-Align •
MSA transformer-based •
DeepAb-based •
TRFold2-based •
GPT-based •
ESM-based •
Sampling-algorithms
3) Function to Scaffold
GAN-based •
VAE-based •
DAE-based •
MLP-based •
Diffusion-based •
RL-based •
Flow-based •
Score-based
4) Scaffold to Sequence
Review •
MLP-based •
VAE-based •
LSTM-based •
CNN-based •
GNN-based •
GAN-based •
Transformer-based •
ResNet-based •
Diffusion-based •
Bayesian method •
Flow-based
5) Function to Sequence
CNN-based •
VAE-based •
GAN-based •
Transformer-based •
Bayesian method •
Reinforcement Learning •
Flow-based •
RNN-based •
LSTM-based •
Autoregressive •
Boltzmann machine •
Diffusion-based •
GNN-based •
Score-based
6) Function to Structure
LSTM-based •
Diffusion-based •
RoseTTAFold-based •
CNN-based •
GNN-based •
Transformer-based •
MLP-based •
Flow-based •
AlphaFold-based
7) Other
Effects of mutations & Fitness Landscape •
Protein Language Model & Representation Learning •
Molecular Design Model •
Unclassified
FLIP: Benchmark tasks in fitness landscape inference for proteins Christian Dallago, Jody Mou, Kadina E Johnston, Bruce Wittmann, Nick Bhattacharya, Samuel Goldman, Ali Madani, Kevin K Yang NeurIPS 2021 Datasets and Benchmarks Track/bioRxiv 2021 • website • code • Supplementary
A Benchmark Framework for Evaluating Structure-to-Sequence Models for Protein Design Jeffrey Chan, Seyone Chithrananda, David Brookes, Sam Sinai Paper unavailable at Machine Learning in Structural Biology Workshop 2022
PDBench: Evaluating Computational Methods for Protein-Sequence Design Leonardo V Castorina, Rokas Petrenas, Kartic Subr, Christopher W Wood Bioinformatics, 2023;, btad027 • code
Benchmarking deep generative models for diverse antibody sequence design Igor Melnyk, Payel Das, Vijil Chenthamarakshan, Aurelie Lozano arXiv:2111.06801
The Protein Engineering Tournament: An Open Science Benchmark for Protein Modeling and Design Chase Armer, Hassan Kane, Dana Cortade, Dave Estell, Adil Yusuf, Radhakrishna Sanka, Henning Redestig, TJ Brunette, Pete Kelly, Erika DeBenedictis arXiv:2309.09955
Computational Scoring and Experimental Evaluation of Enzymes Generated by Neural Networks Sean R.Johnson, Xiaozhi Fu, Sandra Viknander, Clara Goldin, Sarah Monaco, Aleksej Zelezniak, Kevin K. Yang bioRxiv (2023) • code
FLOP: Tasks for Fitness Landscapes Of Protein Wildtypes Peter Mørch Groth, Richard Michael, Jesper Salomon, Pengfei Tian, Wouter Boomsma bioRxiv 2023.06.21.545880 • code
ProteinGym: Large-Scale Benchmarks for Protein Design and Fitness Prediction Pascal Notin, Aaron W Kollasch, Daniel Ritter, Lood van Niekerk, Steffanie Paul, Hansen Spinner, Nathan Rollins, Ada Shaw, Ruben Weitzman, Jonathan Frazer, Mafalda Dias, Dinko Franceschi, Rose Orenbuch, Yarin Gal, Debora S Marks bioRxiv 2023.12.07.570727 • code
Results of the Protein Engineering Tournament: An Open Science Benchmark for Protein Modeling and Design Chase Armer, Hassan Kane, Dana L. Cortade, Henning Redestig, David A. Estell, Adil Yusuf, Nathan Rollins, Hansen Spinner, Debora Marks, TJ Brunette, Peter J. Kelly, Erika DeBenedictis bioRxiv 2024.08.12.606135 • code • Supplementary
Generative AI Models for the Protein Scaffold Filling Problem Letu Qingge, Kushal Badal, Richard Annan, Jordan Sturtz, Xiaowen Liu, and Binhai Zhu Journal of Computational Biology
AlphaDesign: A graph protein design method and benchmark on AlphaFoldDB Zhangyang Gao, Cheng Tan, Stan Z. Li arxiv (2022)
SidechainNet: An All-Atom Protein Structure Dataset for Machine Learning Jonathan E. King, David Ryan Koes arxiv • github::sidechainnet
TDC maintains a resource list that currently contains 22 tasks (and its datasets) related to small molecules and macromolecules, including PPI, DDI and so on. MoleculeNet published a small molecule related benchmark four years ago.
In terms of datasets and benchmarks, protein design is far less mature than drug discovery (paperwithcode drug discovery benchmarks). (Maybe should add the evaluation of protein design for deep learning method (especially deep generative model)) Difficulties and opportunities always coexist. Happy to see the work of Christian Dallago, Jody Mou, Kadina E. Johnston, Bruce J. Wittmann, Nicholas Bhattacharya, Samuel Goldman, Ali Madani, Kevin K. Yang and Zhangyang Gao, Cheng Tan, Stan Z. Li.
Sampling of structure and sequence space of small protein folds Thomas W. Linsky, Kyle Noble, Autumn R. Tobin, Rachel Crow, Lauren Carter, Jeffrey L. Urbauer, David Baker & Eva-Maria Strauch Nat Commun 13, 7151 (2022) • code • Supplementary
OpenProteinSet: Training data for structural biology at scale Gustaf Ahdritz, Nazim Bouatta, Sachin Kadyan, Lukas Jarosch, Daniel Berenberg, Ian Fisk, Andrew M. Watkins, Stephen Ra, Richard Bonneau, Mohammed AlQuraishi arXiv:2308.05326 • OpenFold
ProteinInvBench: Benchmarking Protein Design on Diverse Tasks, Models, and Metrics Zhangyang Gao, Cheng Tan, Yijie Zhang, Xingran Chen, Stan Z. Li GitHub
PDB-Struct: A Comprehensive Benchmark for Structure-based Protein Design Chuanrui Wang, Bozitao Zhong, Zuobai Zhang, Narendra Chaudhary, Sanchit Misra, Jian Tang arXiv preprint arXiv:2312.00080 (2023) • code
Scaffold-Lab: Critical Evaluation and Ranking of Protein Backbone Generation Methods in A Unified Framework Zhuoqi Zheng, Bo Zhang, Bozitao Zhong, Kexin Liu, Jinyu Yu, Zhengxin Li, JunJie Zhu, Ting Wei, Hai-Feng Chen bioRxiv 2024.02.10.579743 • code • Supplementary
Antibody DomainBed: Out-of-Distribution Generalization in Therapeutic Protein Design Nataša Tagasovska, Ji Won Park, Matthieu Kirchmeyer, Nathan C. Frey, Andrew Martin Watkins, Aya Abdelsalam Ismail, Arian Rokkum Jamasb, Edith Lee, Tyler Bryson, Stephen Ra, Kyunghyun Cho arXiv:2407.21028 • code • dataset
Large protein databases reveal structural complementarity and functional locality Paweł Szczerbiak, Lukasz Szydlowski, Witold Wydmański, P. Douglas Renfrew, Julia Koehler Leman, Tomasz Kosciolek bioRxiv 2024.08.14.607935 • code • Supplementary • website
The Protein Design Archive (PDA): insights from 40 years of protein design Marta Chronowska, Michael J. Stam, Derek N. Woolfson, Luigi F. Di Constanzo, Christopher W. Wood bioRxiv 2024.09.05.611465 • code • Supplementary • website
ProteinBench: A Holistic Evaluation of Protein Foundation Models Fei Ye, Zaixiang Zheng, Dongyu Xue, Yuning Shen, Lihao Wang, Yiming Ma, Yan Wang, Xinyou Wang, Xiangxin Zhou, Quanquan Gu arXiv:2409.06744 • code
Benchmarking Generative Models for Antibody Design & Exploring Log-Likelihood for Sequence Ranking Talip Uçar, Cedric Malherbe, Ferran Gonzalez bioRxiv 2024.10.07.617023 • code
Towards Robust Evaluation of Protein Generative Models: A Systematic Analysis of Metrics Pavel Strashnov, Andrey Shevtsov, Viacheslav Meshchaninov, Maria Ivanova, Fedor Nikolaev, Olga Kardymon, Dmitry Vetrov bioRxiv 2024.10.25.620213
A list of suggested protein databases, more lists at CNCB.
| Database | Description |
|---|---|
| PDB | The Protein Data Bank (PDB) is a database of 3D structural data of large biological molecules, such as proteins and nucleic acids. These data are gathered using experimental methods such as X-ray crystallography, NMR spectroscopy, or cryo-electron microscopy. |
| AlphaFoldDB | AlphaFoldDB is a database of protein structure predictions produced by DeepMind's AlphaFold system. It provides highly accurate predictions of protein 3D structures. |
| PDBbind | PDBbind is a comprehensive collection of the binding data of all types of biomolecular complexes in the PDB database. It is primarily used for the development and validation of computational methods for predicting molecular interactions. |
| AB-Bind | AB-Bind is a database for antibody binding affinity data. It offers a curated set of experimental binding data and corresponding antibody-protein complex structures. |
| AntigenDB | AntigenDB is a manually curated database of experimentally verified antigens that includes detailed information about the antigen, the source organism, and the associated antibodies. |
| CAMEO | CAMEO (Continuous Automated Model EvaluatiOn) is a project for the automated evaluation of methods predicting macromolecular structure. It continuously assesses the performance of automated protein structure prediction servers. |
| CAPRI | The Critical Assessment of PRediction of Interactions (CAPRI) is a community-wide experiment to evaluate protein-protein interaction prediction methods. |
| PIFACE | PIFACE is a web server for the prediction of protein-protein interactions. It identifies potential interaction interfaces on protein surfaces. |
| SAbDab | The Structural Antibody Database (SAbDab) is an automatically updated resource for the structural information of antibodies from the PDB. It allows for easy access to curated, annotated, and classified antibody structures. |
| SKEMPI v2.0 | SKEMPI 2.0 is a database of experimental measurements of the change in binding free energy caused by mutations in protein-protein complexes. |
| ProtCAD | ProtCAD is a suite of tools for the design and engineering of novel protein structures, sequences, and functions. It allows users to build and manipulate complex protein structures, generate and evaluate sequence libraries, and simulate mutational effects. ProtCAD is a suite of tools for the design and engineering of novel protein structures, sequences, and functions. It allows users to build and manipulate complex protein structures, generate and evaluate sequence libraries, and simulate mutational effects. |
Some similar GitHub lists that include papers about protein design using deep learning:
- design_tools
- awesome-AI-based-protein-design
- ProteinStructureWithDL
- List of available bioinformatic tools and services
Guides/Tutorials for beginners on GitHub:
Collection of Protein Design Labs:
Protein design: from computer models to artificial intelligence Antonella Paladino, Filippo Marchetti, Silvia Rinaldi, Giorgio Colombo Wiley Interdisciplinary Reviews: Computational Molecular Science 7.5 (2017): e1318
Advances in protein structure prediction and design Kuhlman B., Bradley P. Nat Rev Mol Cell Biol 20, 681-697 (2019)
Deep learning in protein structural modeling and design Wenhao Gao, Sai Pooja Mahajan, Jeremias Sulam, and Jeffrey J. Gray Patterns 1.9 • 2020
100th anniversary of macromolecular science viewpoint: Data-driven protein design Ferguson, Andrew L., and Rama Ranganathan. ACS Macro Letters 10.3 (2021)
Artificial intelligence in early drug discovery enabling precision medicine Fabio Bonioloa, Emilio Dorigattia, Alexander J. Ohnmachta, Dieter Saurb, Benjamin Schuberta, and Michael P. Menden Expert Opinion on Drug Discovery 16.9 (2021)
Protein design with deep learning Defresne, Marianne, Sophie Barbe, and Thomas Schiex. International Journal of Molecular Sciences 22.21 (2021)
Protein sequence design with deep generative models Zachary Wu, Kadina E. Johnston, Frances H. Arnold, Kevin K. Yang Current Opinion in Chemical Biology 65 • note • 2021
Structure-based protein design with deep learning Ovchinnikov, Sergey, and Po-Ssu Huang. Current opinion in chemical biology 65 • note • 2021
Deep learning techniques have significantly impacted protein structure prediction and protein design Pearce, Robin, and Yang Zhang. Current opinion in structural biology 68 (2021)
Recent advances in de novo protein design: Principles, methods, and applications Pan, Xingjie, and Tanja Kortemme. Journal of Biological Chemistry 296 (2021)
Protein design via deep learning Wenze Ding, Kenta Nakai, Haipeng Gong Briefings in Bioinformatics • 25 March 2022
Deep generative modeling for protein design Strokach, Alexey, and Philip M. Kim. Current Opinion in Structural Biology • 2022
Dawn of a new era for membrane protein design Sowlati-Hashjin, Shahin, Aanshi Gandhi, and Michael Garton BioDesign Research (2022)
Deep learning approaches for conformational flexibility and switching properties in protein design Rudden, Lucas SP, Mahdi Hijazi, and Patrick Barth Frontiers in Molecular Biosciences
Computational protein design with evolutionary-based and physics-inspired modeling: current and future synergies Cyril Malbranke, David Bikard, Simona Cocco, Rémi Monasson, Jérôme Tubiana arXiv:2208.13616v2
From sequence to function through structure: deep learning for protein design Noelia Ferruz, Michael Heinzinger, Mehmet Akdel, Alexander Goncearenco, Luca Naef, Christian Dallago bioRxiv 2022.08.31.505981/Computational and Structural Biotechnology Journal Volume 21, 2023 • Supplementary • accompanying list
Computational protein design with data-driven approaches: Recent developments and perspectives Liu H, Chen Q. WIREs Comput Mol Sci. 2022. e1646
Understanding by design: Implementing deep learning from protein structure prediction to protein design Gao, Yuanxu, Jiangshan Zhan, and Albert CH Yu. MedComm-Future Medicine 1.2 (2022): e22
Diffusion Models in Bioinformatics: A New Wave of Deep Learning Revolution in Action Zhiye Guo, Jian Liu, Yanli Wang, Mengrui Chen, Duolin Wang, Dong Xu, Jianlin Cheng arXiv:2302.10907
Machine learning for evolutionary-based and physicsinspired protein design: Current and future synergies Cyril Malbranke, David Bikard, Simona Cocco, Rémi Monasson, Jérôme Tubiana Current Opinion in Structural Biology
De novo design of polyhedral protein assemblies: before and after the AI revolution Bhoomika Basu Mallik, Jenna Stanislaw, Tharindu Madhusankha Alawathurage, and Alena Khmelinskaia ChemBioChem 2023, e202300117
Research progress of artificial intelligence in protein design CHEN Zhihang, JI Menglin, QI Yifei Synthetic Biology Journal (2023)
A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Material Mengchun Zhang, Maryam Qamar, Taegoo Kang, Yuna Jung, Chenshuang Zhang, Sung-Ho Bae, Chaoning Zhang https://arxiv.org/abs/2304.01565
Exploring the Protein Sequence Space with Global Generative Models Sergio Romero-Romero, Sebastian Lindner, Noelia Ferruz arXiv:2305.01941
The Era of Machine Learning for Protein Design, Summarized in Four Key Methods LucianoSphere Towards Data Science
Is novelty predictable? Clara Fannjiang, Jennifer Listgarten arXiv:2306.00872
Computational protein design - where it goes? Xu Binbin, Chen Yingjun and Xue Weiwei Current Medicinal Chemistry 2023
How can the protein design community best support biologists who want to harness AI tools for protein structure prediction and design? Birte Höcker, Peilong Lu, Anum Glasgow, Debora S. Marks Pranam Chatterjee, Joanna S.G. Slusky, Ora Schueler-Furman, Possu Huang Cell Systems 14.8 (2023)
De novo 設計ナノポアの創製 新津藍 生物工学会誌 101.8 (2023)
Generative artificial intelligence for de novo protein design Adam Winnifrith, Carlos Outeiral, Brian Hie arXiv:2310.09685
Intelligent Protein Design and Molecular Characterization Techniques: A Comprehensive Review Jingjing Wang, Chang Chen, Ge Yao, Junjie Ding, Liangliang Wang and Hui Jiang Molecules 28.23 (2023)
Generative models for protein sequence modeling: recent advances and future directions Mehrsa Mardikoraem, Zirui Wang, Nathaniel Pascual, Daniel Woldring Briefings in Bioinformatics
A new age in protein design empowered by deep learning Hamed Khakzad, Ilia Igashov, Arne Schneuing, Casper Goverde Michael Bronstein, Bruno Correia Cell Systems, Volume 14, Issue 11
Deep learning for protein structure prediction and design—progress and applications Jürgen Jänes and Pedro Beltrao Mol Syst Biol(2024)
De novo protein design—From new structures to programmable functions Kortemme, Tanja. Cell 187.3 (2024)
Generative models for protein structures and sequences Hsu, C., Fannjiang, C. & Listgarten, J. Nat Biotechnol 42, 196–199 (2024)
What does it take for an ‘AlphaFold Moment’ in functional protein engineering and design? Roberto A. Chica & Noelia Ferruz Nat Biotechnol 42, 173–174 (2024)
Protein design: the experts speak Doerr, A. Nat Biotechnol 42, 175–178 (2024)
Machine learning for functional protein design Pascal Notin, Nathan Rollins, Yarin Gal, Chris Sander & Debora Marks Nat Biotechnol 42, 216–228 (2024)
Sparks of function by de novo protein design Chu, A.E., Lu, T. & Huang, PS. Nat Biotechnol 42, 203–215 (2024) • poster
A Survey of Generative AI for De Novo Drug Design: New Frontiers in Molecule and Protein Generation Xiangru Tang, Howard Dai, Elizabeth Knight, Fang Wu, Yunyang Li, Tianxiao Li, Mark Gerstein arXiv:2402.08703
Security challenges by AI-assisted protein design Philip Hunter EMBO Rep(2024)
Opportunities and challenges in design and optimization of protein function Dina Listov, Casper A. Goverde, Bruno E. Correia & Sarel Jacob Fleishman Nat Rev Mol Cell Biol (2024)
The State-of-the-Art Overview to Application of Deep Learning in Accurate Protein Design and Structure Prediction Saber Saharkhiz, Mehrnaz Mostafavi, Amin Birashk, Shiva Karimian, Shayan Khalilollah, Sohrab Jaferian, Yalda Yazdani, Iraj Alipourfard, Yun Suk Huh, Marzieh Ramezani Farani & Reza Akhavan-Sigari Top Curr Chem (Z) 382, 23 (2024)
Computational methods for protein design Noelia Ferruz, Amelie Stein Protein Engineering, Design and Selection, Volume 37, 2024
Structure-based protein and small molecule generation using EGNN and diffusion models: A comprehensive review Farzan Soleymani, Eric Paquet, Herna Lydia Viktor, Wojtek Michalowski Computational and Structural Biotechnology Journal (2024)
Machine learning in biological physics: From biomolecular prediction to design Jonathan Martin, Marcos Lequerica Mateos, José N. Onuchic, and Faruck Morcos Proceedings of the National Academy of Sciences 121.27 (2024)
AI has dreamt up a blizzard of new proteins. Do any of them actually work? Ewen Callaway Nature 634.8034 (2024)
Five protein-design questions that still challenge AI Sara Reardon Nature 635.8037 (2024)
A review of deep learning methods for antibodies Jordan Graves, Jacob Byerly, Eduardo Priego, Naren Makkapati , S. Vince Parish, Brenda Medellin and Monica Berrondo Antibodies 9.2 (2020)
Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies Rahmad Akbar, Habib Bashour, Puneet Rawat, Philippe A. Robert, Eva Smorodina, Tudor-Stefan Cotet, Karine Flem-Karlsen, Robert Frank, Brij Bhushan Mehta, Mai Ha Vu, Talip Zengin, Jose Gutierrez-Marcos, Fridtjof Lund-Johansen, Jan Terje Andersen, and Victor Greif Mabs. Vol. 14. No. 1. Taylor & Francis, 2022
Advances in computational structure-based antibody design Hummer, Alissa M., Brennan Abanades, and Charlotte M. Deane. Current Opinion in Structural Biology 74 (2022)
Computational and artificial intelligence-based methods for antibody development Jisun Kim, Matthew McFee, Qiao Fang, Osama Abdin, Philip M. Kim Trends in Pharmacological Sciences (2023)
Leveraging deep learning to improve vaccine design Hederman AP, Ackerman ME Trends in immunology (2023)
In Silico Approaches to Deliver Better Antibodies by Design: The Past, the Present and the Future Andreas Evers, Shipra Malhotra, Vanita D. Sood arXiv:2305.07488
AI Models for Protein Design are Driving Antibody Engineering Michael Chungyoun, Jeffrey J. Gray Current Opinion in Biomedical Engineering (2023): 100473
Computational Methods in Immunology and Vaccinology: Design and Development of Antibodies and Immunogens Federica Guarra and Giorgio Colombo Journal of Chemical Theory and Computation (2023)
Simplifying complex antibody engineering using machine learning Makowski, Emily K., Hsin-Ting Chen, and Peter M. Tessier. Cell Systems 14.8 (2023)/2022 AIChE Annual Meeting. AIChE, 2022.
AI driven B-cell Immunotherapy Design Bruna Moreira da Silva, David B. Ascher, Nicholas Geard, Douglas E. V. Pires arXiv:2309.01122
Best practices for machine learning in antibody discovery and development Leonard Wossnig, Norbert Furtmann, Andrew Buchanan, Sandeep Kumar, Victor Greiff arXiv:2312.08470/Drug Discovery Today (2024)
Next generation of multispecific antibody engineering Daniel Keri, Matt Walker, Isha Singh, Kyle Nishikawa, Fernando Garces Antibody Therapeutics (2023): tbad027
A primer on ML in antibody engineering ABHISHAIKE MAHAJAN Substack • blog
Antibody design using deep learning: from sequence and structure design to affinity maturation Sara Joubbi, Alessio Micheli, Paolo Milazzo, Giuseppe Maccari, Giorgio Ciano, Dario Cardamone, Duccio Medini Briefings in Bioinformatics, Volume 25, Issue 4, July 2024, bbae307
AI-accelerated therapeutic antibody development: practical insights Luca Santuari, Marianne Bachmann Salvy, Ioannis Xenarios, Bulak Arpat Frontiers in Drug Discovery 4 (2024)
AI-driven antibody design with generative diffusion models: current insights and future directions Xin-heng He, Jun-rui Li, James Xu, Hong Shan, Shi-yi Shen, Si-han Gao & H. Eric Xu Acta Pharmacologica Sinica (2024)
Deep generative models for peptide design Wan, Fangping, Daphne Kontogiorgos-Heintz, and Cesar de la Fuente-Nunez Digital Discovery (2022)
Design of protein segments and peptides for binding to protein targets Gupta, Suchetana, Noora Azadvari, and Parisa Hosseinzadeh. BioDesign Research 2022 (2022)
Revolutionizing peptide-based drug discovery: Advances in the post-AlphaFold era Liwei Chang, Arup Mondal, Bhumika Singh, Yisel Martínez-Noa, Alberto Perez Wiley Interdisciplinary Reviews: Computational Molecular Science
Peptide-based drug discovery through artificial intelligence: towards an autonomous design of therapeutic peptides Montserrat Goles, Anamaría Daza, Gabriel Cabas-Mora, Lindybeth Sarmiento-Varón, Julieta Sepúlveda-Yañez, Hoda Anvari-Kazemabad, Mehdi D Davari, Roberto Uribe-Paredes, Álvaro Olivera-Nappa, Marcelo A Navarrete, David Medina-Ortiz Briefings in Bioinformatics 25.4 (2024)
Improving de novo Protein Binder Design with Deep Learning Nathaniel Bennett, Brian Coventry, Inna Goreshnik, Buwei Huang, Aza Allen, Dionne Vafeados, Ying Po Peng, Justas Dauparas, Minkyung Baek, Lance Stewart, Frank DiMaio, Steven De Munck, Savvas Savvides, David Baker bioRxiv 2022.06.15.495993/Nat Commun 14, 2625 (2023) • code • news
A review of enzyme design in catalytic stability by artificial intelligence Yongfan Ming, Wenkang Wang, Rui Yin, Min Zeng, Li Tang, Shizhe Tang, Min Li Briefings in Bioinformatics, 2023
Application of "foldability" in the intelligent of enzymes engineering and design: take AlphaFold2 for example MENG Qiaozhen, GUO Fei Synthetic Biology Journal (2023)
AlphaFold2 and Deep Learning for Elucidating Enzyme Conformational Flexibility and Its Application for Design Casadevall, Guillem, Cristina Duran, and Sílvia Osuna. JACS Au (2023)
Accelerating Biocatalysis Discovery with Machine Learning: A Paradigm Shift in Enzyme Engineering, Discovery, and Design Braun Markus, Gruber Christian C, Krassnigg Andreas, Kummer Arkadij, Lutz Stefan, Oberdorfer Gustav, Siirola Elina, and Snajdrova Radka ACS Catal. 2023
Building Enzymes through Design and Evolution Hossack, Euan J., Florence J. Hardy, and Anthony P. Green. ACS Catalysis 13.19 (2023)
Advances in generative modeling methods and datasets to design novel enzymes for renewable chemicals and fuels Rana A Barghout, Zhiqing Xu, Siddharth Betala, Radhakrishnan Mahadevan Current Opinion in Biotechnology, Volume 84, 2023
Opportunites and Challenges for Machine Learning-Assisted Enzyme Engineering Jason Yang, Francesca-Zhoufan Li, Frances H. Arnold ACS Central Science (2024)
Navigating the landscape of enzyme design: from molecular simulations to machine learning Jiahui Zhoua, Meilan Huang Chemical Society Reviews (2024)
Invert trained models with optimize algorithms through iterations for sequence design. Inverted structure prediction models are known as Hallucination.
Design of proteins presenting discontinuous functional sites using deep learning Doug Tischer, Sidney Lisanza, Jue Wang, Runze Dong, View ORCID ProfileIvan Anishchenko, Lukas F. Milles, Sergey Ovchinnikov, David Baker bioRxiv (2020)
Fast differentiable DNA and protein sequence optimization for molecular design Linder, Johannes, and Georg Seelig. arXiv preprint arXiv:2005.11275 (2020)
De novo protein design by deep network hallucination Ivan Anishchenko, Samuel J. Pellock, Tamuka M. Chidyausiku, Theresa A. Ramelot, Sergey Ovchinnikov, Jingzhou Hao, Khushboo Bafna, Christoffer Norn, Alex Kang, Asim K. Bera, Frank DiMaio, Lauren Carter, Cameron M. Chow, Gaetano T. Montelione & David Baker Nature (2021) • code • trRosetta
Protein sequence design by conformational landscape optimization Christoffer Norn, Basile I. M. Wicky, David Juergens, and Sergey Ovchinnikov Proceedings of the National Academy of Sciences 118.11 (2021) • code
De novo design of small beta barrel proteins David E. Kim, Davin R. Jensen, David Feldman, Doug Tischer and Ayesha Saleem, Cameron M. Chow, Xinting Li, Lauren Carter, Lukas Milles, Hannah Nguyen, Alex Kang, Asim K. Bera, Francis C. Peterson, Brian F. Volkman, Sergey Ovchinnikov, David Baker PNAS(2023),e2207974120 • code
Exploring "dark matter" protein folds using deep learning Zander Harteveld, Alexandra Van Hall-Beauvais, Irina Morozova, Joshua Southern, Casper Alexander Goverde, Sandrine Georgeon, Stephane Rosset, Andreas Loukas, Pierre Vandergheynst, Michael Bronstein, Bruno Correia bioRxiv 2023.08.30.555621/Cell Systems • Suppplymentary • code
Carving out a Glycoside Hydrolase Active Site for Incorporation into a New Protein Scaffold Using Deep Network Hallucination Anders Lønstrup Hansen, Frederik Friis Theisen, Ramon Crehuet, Enrique Marcos, Nushin Aghajari, and Martin Willemoës ACS Synth. Biol. 2024
End-to-end learning of multiple sequence alignments with differentiable Smith-Waterman Petti, Samantha, Bhattacharya, Nicholas, Rao, Roshan, Dauparas, Justas, Thomas, Neil, Zhou, Juannan, Rush, Alexander M, Koo, Peter K, Ovchinnikov, Sergey bioRxiv (2021)/Bioinformatics, 2022;, btac724 • ColabDesign, SMURF, AF2 back propagation • our notes1, notes2 • lecture1, lecture2 • Discord
AlphaDesign: A de novo protein design framework based on AlphaFold Jendrusch, Michael, Jan O. Korbel, and S. Kashif Sadiq. bioRxiv (2021)
Using AlphaFold for Rapid and Accurate Fixed Backbone Protein Design Moffat, Lewis, Joe G. Greener, and David T. Jones. bioRxiv (2021)
State-of-the-art estimation of protein model accuracy using AlphaFold James P. Roney, Sergey Ovchinnikov bioRxiv 2022.03.11.484043/Physical Review Letters 129.23 (2022) • code
Solubility-aware protein binding peptide design using AlphaFold Takatsugu Kosugi, Masahito Ohue bioRxiv 2022.05.14.491955/Biomedicines 10.7 (2022) • Supplemental Materials • code
Hallucinating protein assemblies Basile I M Wicky, Lukas F Milles, Alexis Courbet, Robert J Ragotte, Justas Dauparas, Elias Kinfu, Sam Tipps, Ryan D Kibler, Minkyung Baek, Frank DiMaio, Xinting Li, Lauren Carter, Alex Kang, Hannah Nguyen, Asim K Bera, David Baker bioRxiv 2022.06.09.493773/Science (2022) • related slides • our notes • news
EvoBind: in silico directed evolution of peptide binders with AlphaFold Patrick Bryant, Arne Elofsson bioRxiv 2022.07.23.501214 • code
Hallucination of closed repeat proteins containing central pockets Linna An, Derrick R Hicks, Dmitri Zorine, Justas Dauparas, Basile I. M. Wicky, Lukas F Milles, Alexis Courbet, Asim K. Bera, Hannah Nguyen, Alex Kang, Lauren Carter, David Baker bioRxiv 2022.09.01.506251/Nat Struct Mol Biol 30, 1755-1760 (2023) • Supplementary data
Predicting the structure of large protein complexes using AlphaFold and Monte Carlo tree search Patrick Bryant, Gabriele Pozzati, Wensi Zhu, Aditi Shenoy, Petras Kundrotas & Arne Elofsson Nature communications 13.1 (2022) • gitlba, github • Supplementary data1, Supplementary data2
De novo protein design by inversion of the AlphaFold structure prediction network Casper Goverde, Benedict Wolf, Hamed Khakzad, Stephane Rosset, Bruno E Correia bioRxiv 2022.12.13.520346 • code • lecture1 • lecture2
Code of OpenComplex Jingcheng, Yu and Zhaoming, Chen and Zhaoqun, Li and Mingliang, Zeng and Wenjun, Lin and He, Huang and Qiwei, Ye code
Efficient and scalable de novo protein design using a relaxed sequence space Christopher Josef Frank, Ali Khoshouei, Yosta de Stigter, Dominik Schiewitz, Shihao Feng, Sergey Ovchinnikov, Hendrik Dietz bioRxiv 2023.02.24.529906 • code
Cyclic peptide structure prediction and design using AlphaFold Stephen A. Rettie, Katelyn V. Campbell, Asim K. Bera, Alex Kang, Simon Kozlov, Joshmyn De La Cruz, Victor Adebomi, Guangfeng Zhou, Frank DiMaio, Sergey Ovchinnikov, Gaurav Bhardwaj bioRxiv • Code • Supplementary
De novo design of luciferases using deep learning Andy Hsien-Wei Yeh, Christoffer Norn, Yakov Kipnis, Doug Tischer, Samuel J. Pellock, Declan Evans, Pengchen Ma, Gyu Rie Lee, Jason Z. Zhang, Ivan Anishchenko, Brian Coventry, Longxing Cao, Justas Dauparas, Samer Halabiya, Michelle DeWitt, Lauren Carter, K. N. Houk & David Baker Nature • Code • Supplementary Materials
In silico evolution of protein binders with deep learning models for structure prediction and sequence design Odessa J Goudy, Amrita Nallathambi, Tomoaki Kinjo, Nicholas Randolph, Brian Kuhlman bioRxiv 2023.05.03.539278 • Supplementary • code
Computational design of soluble analogues of integral membrane protein structures Casper Alexander Goverde, Martin Pacesa, Lars Jeremy Dornfeld, Sandrine Georgeon, Stephane Rosset, Justas Dauparas, Christian Shellhaas, Simon Kozlov, David Baker, Sergey Ovchinnikov, Bruno Correia bioRxiv 2023.05.09.540044/Nature (2024) • code • Supplementary
Antibody Complementarity-Determining Region Sequence Design using AlphaFold2 and Binding Affinity Prediction Model Takafumi Ueki, Masahito Ohue bioRxiv 2023.06.02.543382
Context-Dependent Design of Induced-fit Enzymes using Deep Learning Generates Well Expressed, Thermally Stable and Active Enzymes Lior Zimmerman, Noga Alon, Itay Levin, Anna Koganitsky, Nufar Shpigel, Chen Brestel, Gideon David Lapidoth bioRxiv 2023.07.27.550799 • Supplementary
Highly accurate and robust protein sequence design with CarbonDesign/Accurate and robust protein sequence design with CarbonDesign Milong Ren, Chungong Yu, Dongbo Bu, Haicang Zhang bioRxiv 2023.08.07.552204/Nat Mach Intell 6, 536–547 (2024) • code
Design of Cyclic Peptides Targeting Protein-Protein Interactions using AlphaFold Takatsugu Kosugi, Masahito Ohue bioRxiv 2023.08.20.554056 • Supplementary • code
MetaPPI: In Silico Screen for Novel CRBN-based Substrates neoxbio website • news • masif-based • commercial
AlphaFold Distillation for Protein Design Anonymous ICLR 2024 • code
High-throughput computational discovery of inhibitory protein fragments with AlphaFold Andrew Savinov, Sebastian Swanson, Amy E. Keating, Gene-Wei Li bioRxiv 2023.12.19.572389 • code
An integrative approach to protein sequence design through multiobjective optimization Lu Hong, Tanja Kortemme bioRxiv 2024.03.01.582670/PLOS Computational Biology 20(7) • code • Supplementary
Protein Design Using Structure-Prediction Networks: AlphaFold and RoseTTAFold as Protein Structure Foundation Models Jue Wang, Joseph L. Watson and Sidney L. Lisanza Cold Spring Harbor Perspectives in Biology(2024)
Context-dependent design of induced-fit enzymes using deep learning generates well-expressed, thermally stable and active enzymes Lior Zimmerman, Noga Alon, Itay Levin, and Gideon D. Lapidoth Proceedings of the National Academy of Sciences 121.11(2024)
Design of Repeat Alpha-Beta Proteins with Capping Helices Dmitri Zorine, David Baker bioRxiv 2024.06.15.590358 • code
Design of linear and cyclic peptide binders of different lengths only from a protein target sequence Qiuzhen Li, Efstathios Nikolaos Vlachos, Patrick Bryant bioRxiv 2024.06.20.599739 • code • Supplementary
BindCraft: one-shot design of functional protein binders Martin Pacesa, Lennart Nickel, Joseph Schmidt, Ekaterina Pyatova, Christian Schellhaas, Lucas Kissling, Ana Alcaraz-Serna, Yehlin Cho, Kourosh H. Ghamary, Laura Vinue, Brahm J. Yachnin, Andrew M. Wollacott, Stephen Buckley, Sandrine Georgeon, Casper A. Goverde, Georgios N. Hatzopoulos, Pierre Gonczy, Yannick D. Muller, Gerald Schwank, Sergey Ovchinnikov, Bruno E. Correia bioRxiv 2024.09.30.615802 • code
Design of linear and cyclic peptide binders of different lengths from protein sequence information Qiuzhen Li, Efstathios Nikolaos Vlachos, Patrick Bryant bioRxiv 2024.06.20.599739 • code
Scalable protein design using optimization in a relaxed sequence space Christopher Frank, Ali Khoshouei , Lara Fub , Dominik Schiwietz , Dominik Putz, Lara Weber, Zhixuan Zhao, Motoyuki Hattori, Shihao Feng, Yosta de Stigter, Sergey Ovchinnikov, Hendrik Dietz Science386,439-445(2024) • code
Alphafold2 refinement improves designability of large de novo proteins
Christopher Josef Frank, Dominik Schiwietz, Lara Fuss, Sergey Ovchinnikov, Hendrik Dietz
bioRxiv 2024.11.21.624687 • colab
Design in the DARK: Learning Deep Generative Models for De Novo Protein Design Moffat, Lewis, Shaun M. Kandathil, and David T. Jones. bioRxiv (2022) • DMPfold2
AutoFoldFinder: An Automated Adaptive Optimization Toolkit for De Novo Protein Fold Design Shuhao Zhang, Youjun Xu, Jianfeng Pei, Luhua Lai NeurIPS 2021
Protein language models trained on multiple sequence alignments learn phylogenetic relationships Damiano Sgarbossa, Umberto Lupo, Anne-Florence Bitbol arXiv preprint arXiv:2203.15465 (2022)/bioRxiv 2022.04.14.488405
EvoOpt: an MSA-guided, fully unsupervised sequence optimization pipeline for protein design Hideki Yamaguchi, Yutaka Saito NeurIPS 2022
Generative power of a protein language model trained on multiple sequence alignments Sgarbossa, Damiano, Umberto Lupo, and Anne-Florence Bitbol Elife 12 (2023): e79854 • code
Towards deep learning models for target-specific antibody design Sai Pooja Mahajan, Jeffrey Ruffolo, Rahel Frick, Jeffrey J. Gray Biophysical Journal 121.3 (2022) • DeepAb • lecture
Hallucinating structure-conditioned antibody libraries for target-specific binders Sai Pooja Mahajan, Jeffrey A Ruffolo, Rahel Frick, Jeffrey J. Gray bioRxiv 2022.06.06.494991/Front. Immunol. 13:999034 • Supplementary • code
News of TRDesign TIANRANG XLab paper unavailable • slides • website • commercial • news
Multi-segment preserving sampling for deep manifold sampler Daniel Berenberg, Jae Hyeon Lee, Simon Kelow, Ji Won Park, Andrew Watkins, Vladimir Gligorijević, Richard Bonneau, Stephen Ra, Kyunghyun Cho arXiv preprint arXiv:2205.04259 (2022)
Preference optimization of protein language models as a multi-objective binder design paradigm Pouria Mistani, Venkatesh Mysore arXiv:2403.04187
HMAMP: Hypervolume-Driven Multi-Objective Antimicrobial Peptides Design Li Wang, Yiping Li, Xiangzheng Fu, Xiucai Ye, Junfeng Shi, Gary G. Yen, Xiangxiang Zeng arXiv:2405.00753
Generating novel protein sequences using Gibbs sampling of masked language models Sean R. Johnson, Sarah Monaco, Kenneth Massie, Zaid Syed bioRxiv 2021.01.26.428322 • code
A high-level programming language for generative protein design Brian Hie, Salvatore Candido, Zeming Lin, Ori Kabeli, Roshan Rao, Nikita Smetanin, Tom Sercu, Alexander Rives bioRxiv 2022.12.21.521526
Language models generalize beyond natural proteins Robert Verkuil, Ori Kabeli, Yilun Du, Basile IM Wicky, Lukas F Milles, Justas Dauparas, David Baker, Sergey Ovchinnikov, Tom Sercu, Alexander Rives bioRxiv 2022.12.21.521521
ESMFold Hallucinates Native-Like Protein Sequences Jeliazko R Jeliazkov, Diego del Alamo, Joel D Karpiak bioRxiv 2023.05.23.541774
Protein Language Model Supervised Precise and Efficient Protein Backbone Design Method Bo Zhang, Kexin Liu, Zhuoqi Zheng, Yunfeiyang Liu, Junxi Mu, Ting Wei, Hai-Feng Chen bioRxiv 2023.10.26.564121 • code • Supplementary
Unexplored regions of the protein sequence-structure map revealed at scale by a library of foldtuned language models Arjuna M. Subramanian, Matt Thomson bioRxiv 2023.12.22.573145
Computational scoring and experimental evaluation of enzymes generated by neural networks Sean R. Johnson, Xiaozhi Fu, Sandra Viknander, Clara Goldin, Sarah Monaco, Aleksej Zelezniak & Kevin K. Yang Nature Biotechnology (2024) • code
Exploring Latent Space for Generating Peptide Analogs Using Protein Language Models Po-Yu Liang, Xueting Huang, Tibo Duran, Andrew J. Wiemer, Jun Bai arXiv:2408.08341 • code
Designing diverse and high-performance proteins with a large language model in the loop Carlos A. Gomez-Uribe, Japheth Gado, Meiirbek Islamov bioRxiv 2024.10.25.620340
AdaLead: A simple and robust adaptive greedy search algorithm for sequence design Sam Sinai, Richard Wang, Alexander Whatley, Stewart Slocum, Elina Locane, Eric D. Kelsic arXiv preprint arXiv:2010.02141 (2020) • code
Autofocused oracles for model-based design Fannjiang, Clara, and Jennifer Listgarten. Advances in Neural Information Processing Systems 33 (2020)
An Efficient MCMC Approach to Energy Function Optimization in Protein Structure Prediction Lakshmi A. Ghantasala, Risi Jaiswal, Supriyo Datta arXiv:2211.03193
Plug & Play Directed Evolution of Proteins with Gradient-based Discrete MCMC Patrick Emami, Aidan Perreault, Jeffrey Law, David Biagioni, Peter St. Joh NeurIPS 2022/arXiv:2212.09925
Importance Weighted Expectation-Maximization for Protein Sequence Design Zhenqiao Song, Lei Li arXiv:2305.00386 • Supplementary
Simultaneous enhancement of multiple functional properties using evolution-informed protein design Benjamin Fram, Ian Truebridge, Yang Su, Adam J. Riesselman, John B. Ingraham, Alessandro Passera, Eve Napier, Nicole N. Thadani, Samuel Lim, Kristen Roberts, Gurleen Kaur, Michael Stiffler, Debora S. Marks, Christopher D. Bahl, Amir R. Khan, Chris Sander, Nicholas P. Gauthier bioRxiv (2023): 2023-05
Optimizing protein fitness using Gibbs sampling with Graph-based Smoothing Andrew Kirjner, Jason Yim, Raman Samusevich, Tommi Jaakkola, Regina Barzilay, Ila Fiete arXiv:2307.00494 • code
These models design backbone/scaffold/template in Cartesian coordinates, contact maps, distance maps and φ & ψ angles. Including conditional/unconditional generative models.
Generative modeling for protein structures Anand, Namrata, and Possu Huang. NeurIPS 2018
Fully differentiable full-atom protein backbone generation Anand Namrata, Raphael Eguchi, and Po-Ssu Huang. OpenReview ICLR 2019 workshop DeepGenStruct • without code
RamaNet: Computational de novo helical protein backbone design using a long short-term memory generative neural network Sabban, Sari, and Mikhail Markovsky. F1000Research 9 (2020) • code • pyRosetta • tensorflow • maximizaing the fluorescence of a protein
A Generative Model for Creating Path Delineated Helical Proteins Nicholas B. Woodall, Ryan Kibler, Basile Wicky, Brian Coventry bioRxiv 2023.05.24.542095 • code
Conditioning by adaptive sampling for robust design Brookes, David, Hahnbeom Park, and Jennifer Listgarten. International conference on machine learning. PMLR, 2019 • without code
IG-VAE: generative modeling of immunoglobulin proteins by direct 3D coordinate generation Raphael R. Eguchi, Christian A. Choe, Po-Ssu Huang Biorxiv (2020) • without code
Generating tertiary protein structures via an interpretative variational autoencoder Xiaojie Guo, Yuanqi Du, Sivani Tadepalli, Liang Zhao, Amarda Shehu arXiv preprint arXiv:2004.07119 (2020) • code not available
Deep sharpening of topological features for de novo protein design Zander Harteveld, Joshua Southern, Michaël Defferrard, Andreas Loukas, Pierre Vandergheynst, Micheal Bronstein, Bruno Correia ICLR2022 Machine Learning for Drug Discovery. 2022 • code not available
End-to-End deep structure generative model for protein design Boqiao Lai, matthew McPartlon, Jinbo Xu bioRxiv 2022.07.09.499440
Deep Generative Design of Epitope-Specific Binding Proteins by Latent Conformation Optimization Raphael R Eguchi, Christian A Choe, Udit Parekh, Irene S Khalek, Michael D Ward, Neha Vithani, Gregory R Bowman, Joseph G Jardine, Possu Huang bioRxiv 2022.12.22.521698
Leveraging Deep Generative Model For Computational Protein Design And Optimization Boqiao Lai arXiv:2408.17241 • PhD thesis
Function-guided protein design by deep manifold sampling Vladimir Gligorijevic, Stephen Ra, Daniel Berenberg, Richard Bonneau, Kyunghyun Cho NeurIPS 2021 • without code
A backbone-centred energy function of neural networks for protein design Bin Huang, Yang Xu, Xiuhong Hu, Yongrui Liu, Shanhui Liao, Jiahai Zhang, Chengdong Huang, Jingjun Hong, Quan Chen & Haiyan Liu Nature (2022) • code
De novo Design of Cavity-Containing Proteins with a Backbone-Centered Neural Network Energy Function Yang Xu, Xiuhong Hu, Chenchen Wang, Yongrui Liu, Quan Chen Haiyan Liu Structure (2024)
Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem Brian L. Trippe, Jason Yim, Doug Tischer, Tamara Broderick, David Baker, Regina Barzilay, Tommi Jaakkola arXiv:2206.04119/NeurIPS 2022/ICLR 2023 • poster • Supplementary • code
ProteinSGM: Score-based generative modeling for de novo protein design Jin Sub Lee, Philip M Kim bioRxiv 2022.07.13.499967/Nat Comput Sci (2023) • code
Protein structure generation via folding diffusion Kevin E. Wu, Kevin K. Yang, Rianne van den Berg, James Y. Zou, Alex X. Lu, Ava P. Amini arXiv:2209.15611/Nat Commun 15, 1059 (2024) • code
Generating Novel, Designable, and Diverse Protein Structures by Equivariantly Diffusing Oriented Residue Clouds Yeqing Lin, Mohammed AlQuraishi arXiv:2301.12485v3 • code • news
SE(3) diffusion model with application to protein backbone generation Jason Yim, Brian L. Trippe, Valentin De Bortoli, Emile Mathieu, Arnaud Doucet, Regina Barzilay, Tommi Jaakkola arXiv:2302.02277/ICLR 2023 • code • Supplementary
A Latent Diffusion Model for Protein Structure Generation Cong Fu, Keqiang Yan, Limei Wang, Wing Yee Au, Michael McThrow, Tao Komikado, Koji Maruhashi, Kanji Uchino, Xiaoning Qian, Shuiwang Ji arXiv:2305.04120
Practical and Asymptotically Exact Conditional Sampling in Diffusion Models Luhuan Wu, Brian L. Trippe, Christian A. Naesseth, David M. Blei, John P. Cunningham arXiv:2306.17775 • code
Dynamics-Informed Protein Design with Structure Conditioning Simon V. Mathis, Urszula Julia Komorowska, Mateja Jamnik, Pietro Lió WCBICML2023/ICLR 2024
ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a protein language diffusion model Bo Ni and David L. Kaplan and M. Buehler arXiv:2310.10605/Science Advances 10.6 (2024) • Supplementary • code
DiffSDS: A geometric sequence diffusion model for protein backbone inpainting Anonymous ICLR 2024/arXiv:2301.09642
A framework for conditional diffusion modelling with applications in motif scaffolding for protein design Kieran Didi, Francisco Vargas, Simon V Mathis, Vincent Dutordoir, Emile Mathieu, Urszula J Komorowska, Pietro Lio arXiv:2312.09236
TopoDiff: Improving Protein Backbone Generation with Topology-aware Latent Encoding Yuyang Zhang, Zihui (Zinnia) Ma, Haipeng Gong bioRxiv 2023.12.13.571602
Improved motif-scaffolding with SE(3) flow matching Jason Yim, Andrew Campbell, Emile Mathieu, Andrew Y. K. Foong, Michael Gastegger, José Jiménez-Luna, Sarah Lewis, Victor Garcia Satorras, Bastiaan S. Veeling, Frank Noé, Regina Barzilay, Tommi S. Jaakkola arXiv:2401.04082/TMLR • code1,code2
DiffTopo: Fold exploration using coarse grained protein topology representations Yangyang Miao, Bruno Correia bioRxiv 2024.02.01.578456/ICLR 2024
Diffusion models in protein structure and docking Jason Yim, Hannes Stärk, Gabriele Corso, Bowen Jing, Regina Barzilay, Tommi S. Jaakkola Wiley Interdisciplinary Reviews: Computational Molecular Science 14.2 (2024) • review
De novo antibody design with SE(3) diffusion Daniel Cutting, Frédéric A. Dreyer, David Errington, Constantin Schneider, Charlotte M. Deane arXiv:2405.07622
Out of Many, One: Designing and Scaffolding Proteins at the Scale of the Structural Universe with Genie 2 Yeqing Lin, Minji Lee, Zhao Zhang, Mohammed AlQuraishi arXiv:2405.15489 • code • news
Diffuse StructGen-1 (DSG-1) the Diffuse team technical appendix • commercial
Floating Anchor Diffusion Model for Multi-motif Scaffolding Ke Liu, Weian Mao, Shuaike Shen, Xiaoran Jiao, Zheng Sun, Hao Chen, Chunhua Shen ICML 2024/arXiv:2406.03141 • code • poster
De novo Design of A Fusion Protein Tool for GPCR Research Kaixuan Gao, Xin Zhang, Jia Nie, Hengyu Meng, Weishe Zhang, Boxue Tian, Xiangyu Liu bioRxiv 2024.09.14.613090 • Supplementary • RFdiffusion-based
Text2Protein: A Generative Model for Designated Protein Design on Given Description Ramtin Hosseini, Siyang Zhang, Pengtao Xie PREPRINT (Version 1) available at Research Square • code
Improving diffusion-based protein backbone generation with global-geometry-aware latent encoding Yuyang Zhang, Yuhang Liu, Zinnia Ma, Min Li, Chunfu Xu, Haipeng Gong bioRxiv 2024.10.05.616664 • code
Diffusion Posterior Sampling via Sequential Monte Carlo for Zero-Shot Scaffolding of Protein Motifs Young, James Matthew Uygongco, and Omer Deniz Akyildiz Imperial CollegeofScience, Technology and Medicine, 2024 • code • Master thesis • Genie-based
Protein A-like Peptide Design Based on Diffusion and ESM2 Models Long Zhao, Qiang He, Huijia Song, Huijia Song,Tianqian Zhou, An Luo, Zhenguo Wen,Teng Wang, and Xiaozhu Lin Molecules 29.20 (2024) • code
FoldMark: Protecting Protein Generative Models with Watermarking Zaixi Zhang, Ruofan Jin, Kaidi Fu, Le Cong, Marinka Zitnik, Mengdi Wang arXiv:2410.20354 • code
Top-down design of protein nanomaterials with reinforcement learning Isaac D Lutz, Shunzhi Wang, Christoffer Norn, Andrew J Borst, Yan Ting Zhao, Annie Dosey, Longxing Cao, Zhe Li, Minkyung Baek, Neil P King, Hannele Ruohola-Baker, David Baker bioRxiv 2022.09.25.509419/Science380, 266-273(2023) • code,code2
Model-based reinforcement learning for protein backbone design Frederic Renard, Cyprien Courtot, Alfredo Reichlin, Oliver Bent arXiv:2405.01983
SE(3)-Stochastic Flow Matching for Protein Backbone Generation Avishek Joey Bose, Tara Akhound-Sadegh, Kilian Fatras, Guillaume Huguet, Jarrid Rector-Brooks, Cheng-Hao Liu, Andrei Cristian Nica, Maksym Korablyov, Michael Bronstein, Alexander Tong arXiv:2310.02391/ICLR 2024
Fast protein backbone generation with SE(3) flow matching Jason Yim, Andrew Campbell, Andrew Y. K. Foong, Michael Gastegger, José Jiménez-Luna, Sarah Lewis, Victor Garcia Satorras, Bastiaan S. Veeling, Regina Barzilay, Tommi Jaakkola, Frank Noé arXiv:2310.05297 • code
Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone Generation Guillaume Huguet, James Vuckovic, Kilian Fatras, Eric Thibodeau-Laufer, Pablo Lemos, Riashat Islam, Cheng-Hao Liu, Jarrid Rector-Brooks, Tara Akhound-Sadegh, Michael Bronstein, Alexander Tong, Avishek Joey Bose arXiv:2405.20313 • website
Design of Ligand-Binding Proteins with Atomic Flow Matching Junqi Liu, Shaoning Li, Chence Shi, Zhi Yang, Jian Tang arXiv:2409.12080
Score-Based Generative Models for Designing Binding Peptide Backbones John D Boom, Matthew Greenig, Pietro Sormanni, Pietro Liò arXiv:2310.07051 • code
Identify amino sequence from given backbone/scaffold/template constrains: torsion angles(φ & ψ), backbone angles(θ and τ), backbone dihedrals (φ, ψ & ω), backbone atoms (Cα, N, C, & O), Cα − Cα distance, unit direction vectors of Cα−Cα, Cα−N & Cα−C, etc(aka. inverse folding). Referred from here. Energy-based models are also inculded for task of rotamer conformation(χ angles or atom coordinates) recovery.
Protein sequence design on given backbones with deep learning Yufeng Liu, Haiyan Liu Protein Engineering, Design and Selection, 2023
Multi-indicator comparative evaluation for deep Learning-Based protein sequence design methods Jinyu Yu, Junxi Mu, Ting Wei, Hai-Feng Chen Bioinformatics, 2024;, btae037
Generative AI for Controllable Protein Sequence Design: A Survey Yiheng Zhu, Zitai Kong, Jialu Wu, Weize Liu, Yuqiang Han, Mingze Yin, Hongxia Xu, Chang-Yu Hsieh, Tingjun Hou arXiv:2402.10516
3D representations of amino acids-applications to protein sequence comparison and classification Li, Jie, and Patrice Koehl. Computational and structural biotechnology journal 11.18 (2014) • 2014
Direct prediction of profiles of sequences compatible with a protein structure by neural networks with fragment-based local and energy-based nonlocal profiles Zhixiu Li, Yuedong Yang, Eshel Faraggi, Jian Zhan, Yaoqi Zhou Proteins: Structure, Function, and Bioinformatics 82.10 (2014) • code unavailable
SPIN2: Predicting sequence profiles from protein structures using deep neural networks James O'Connell, Zhixiu Li, Jack Hanson, Rhys Heffernan, James Lyons, Kuldip Paliwal, Abdollah Dehzangi, Yuedong Yang, Yaoqi Zhou Proteins: Structure, Function, and Bioinformatics 86.6 (2018) • code unavailable
Computational protein design with deep learning neural networks Jingxue Wang, Huali Cao, John Z. H. Zhang & Yifei Qi Scientific reports 8.1 (2018) • code unavailable
Ligand-aware protein sequence design using protein self contacts Jody Mou, Benjamin Fry, Chun-Chen Yao, Nicholas Polizzi NeurIPS 2022
SeqPredNN: a neural network that generates protein sequences that fold into specified tertiary structures Lategan, F. Adriaan, Caroline Schreiber, and Hugh G. Patterton. BMC bioinformatics 24.1 (2023) • code
Design of metalloproteins and novel protein folds using variational autoencoders Greener, Joe G., Lewis Moffat, and David T. Jones. Scientific reports 8.1 (2018)
To improve protein sequence profile prediction through image captioning on pairwise residue distance map Sheng Chen, Zhe Sun, Lihua Lin, Zifeng Liu, Xun Liu, Yutian Chong, Yutong Lu, Huiying Zhao, and Yuedong Yang Journal of chemical information and modeling 60.1 (2019) • SPROF
Deep learning of Protein Sequence Design of Protein-protein Interactions Syrlybaeva, Raulia, and Eva-Maria Strauch. bioRxiv (2022)/Bioinformatics, 2022;, btac733 • Supplementary • code
A structure-based deep learning framework for protein engineering Raghav Shroff, Austin W. Cole, Barrett R. Morrow, Daniel J. Diaz, Isaac Donnell, Jimmy Gollihar, Andrew D. Ellington, Ross Thyer bioRxiv (2019)
ProDCoNN: Protein design using a convolutional neural network Yuan Zhang, Yang Chen, Chenran Wang, Chun-Chao Lo, Xiuwen Liu, Wei Wu, Jinfeng Zhang Proteins: Structure, Function, and Bioinformatics 88.7 (2020) • code unavailable
Protein sequence design with a learned potential Namrata Anand, Raphael Eguchi, Irimpan I. Mathews, Carla P. Perez, Alexander Derry, Russ B. Altman & Po-Ssu Huang Nacture Communications (2022) • code
TIMED-Design: Flexible and Accessible Protein Sequence Design with Convolutional Neural Networks Leonardo V Castorina, Suleyman Mert Ünal, Kartic Subr, Christopher W Wood Protein Engineering, Design and Selection, 2024) • code • website
Biosensor and machine learning-aided engineering of an amaryllidaceae enzyme Simon d’Oelsnitz, Daniel J. Diaz, Wantae Kim, Daniel J. Acosta, Tyler L. Dangerfield, Mason W. Schechter, Matthew B. Minus, James R. Howard, Hannah Do, James M. Loy, Hal S. Alper, Y. Jessie Zhang & Andrew D. Ellington Nature Communications 15.1 (2024) • code1, code2
OPUS-Design: Designing Protein Sequence from Backbone Structure with 3DCNN and Protein Language Model Gang Xu, Yulu Yang, Yiqiu Zhang, Qinghua Wang, Jianpeng Ma bioRxiv 2024.08.20.608889 • code
ProBID-Net: A Deep Learning Model for Protein-Protein Binding Interface Design Zhihang Chen, Menglin Ji, Jie Qiana, Zhe Zhang, Xiangying Zhang, Haotian Gao, Haojie Wang, Renxiao Wang, Yifei Qi Chemical Science (2024) • code
Learning from protein structure with geometric vector perceptrons Bowen Jing, Stephan Eismann, Patricia Suriana, Raphael J.L. Townshend, Ron Dror arXiv preprint arXiv:2009.01411 (2020)/ICLR(2021) • GVP
Fast and flexible protein design using deep graph neural networks Alexey Strokach, David Becerra, Carles Corbi-Verge, Albert Perez-Riba, Philip M. Kim Cell Systems (2020) • code::ProteinSolver
Mimetic Neural Networks: A unified framework for Protein Design and Folding Moshe Eliasof, Tue Boesen, Eldad Haber, Chen Keasar, Eran Treister arXiv:2102.03881/Front. Bioinform. 2:715006
TERMinator: A Neural Framework for Structure-Based Protein Design using Tertiary Repeating Motifs Alex J. Li, Vikram Sundar, Gevorg Grigoryan, Amy E. Keating NeurIPS 2021 / arXiv (2022)
A neural network model for prediction of amino-acid probability from a protein backbone structure Shintaro Minami, Koya Sakuma, Naoya Kobayashi Unpublished yet (June 2021)• GCNdesgin
XENet: Using a new graph convolution to accelerate the timeline for protein design on quantum computers Jack B Maguire, Daniele Grattarola, Vikram Khipple Mulligan, Eugene Klyshko, Hans Melo PLoS computational biology 17.9 (2021)
AlphaDesign: A graph protein design method and benchmark on AlphaFoldDB Gao, Zhangyang, Cheng Tan, and Stan Li. arXiv preprint arXiv:2202.01079 (2022) • code
Generative De Novo Protein Design with Global Context Cheng Tan, Zhangyao Gao, Jun Xia and Stan Z. Li arXiv • Apr 2022 • code
Masked inverse folding with sequence transfer for protein representation learning Kevin K Yang, Hugh Yeh, Niccolò Zanichelli bioRxiv 2022.05.25.493516/Protein Engineering, Design and Selection 36 (2023) • code • model
Robust deep learning based protein sequence design using ProteinMPNN Justas Dauparas, Ivan Anishchenko, Nathaniel Bennett, Hua Bai, Robert J. Ragotte, Lukas F. Milles, Basile I. M. Wicky, Alexis Courbet, Robbert J. de Haas, Neville Bethel, Philip J. Y. Leung, Timothy F. Huddy, Sam Pellock, Doug Tischer, Frederick Chan, Brian Koepnick, Hannah Nguyen, Alex Kang, Banumathi Sankaran, Asim Bera, Neil P. King, David Baker bioRxiv 2022.06.03.494563/Science (2022) • code • hugging face • lecture • colab(in_jax) • ProteinMPNN+ESMFold
Antibody-Antigen Docking and Design via Hierarchical Equivariant Refinement Jin, Wengong, Regina Barzilay, and Tommi Jaakkola. arXiv preprint arXiv:2207.06616 (2022)/International Conference on Machine Learning. PMLR, 2022 • code • poster
Neural Network-Derived Potts Models for Structure-Based Protein Design using Backbone Atomic Coordinates and Tertiary Motifs Alex J. Li, Mindren Lu, Israel Desta, Vikram Sundar, Gevorg Grigoryan, and Amy E. Keating bioRxiv 2022.08.02.501736/Protein Science, 32(2)
SE(3) Equivalent Graph Attention Network as an Energy-Based Model for Protein Side Chain Conformation Deqin Liu, Sheng Chen, Shuangjia Zheng, Sen Zhang, Yuedong Yang bioRxiv 2022.09.05.506704 • code
PiFold: Toward effective and efficient protein inverse folding Zhangyang Gao, Cheng Tan, Stan Z. Li arXiv:2209.12643v2/ICLR 2023 • github
Protein Sequence Design by Entropy-based Iterative Refinement Xinyi Zhou, Guangyong Chen, Junjie Ye, Ercheng Wang, Jun Zhang, Cong Mao, Zhanwei Li, Jianye Hao, Xingxu Huang, Jin Tang, Pheng Ann Heng bioRxiv 2023.02.04.527099
Lightweight Contrastive Protein Structure-Sequence Transformation Jiangbin Zheng, Ge Wang, Yufei Huang, Bozhen Hu, Siyuan Li, Cheng Tan, Xinwen Fan, Stan Z. Li arXiv:2303.11783
Modeling Protein Structure Using Geometric Vector Field Networks Weian Mao, Muzhi Zhu, Hao Chen, Chunhua Shen bioRxiv 2023.05.07.539736
Knowledge-Design: Pushing the Limit of Protein Deign via Knowledge Refinement Zhangyang Gao, Cheng Tan, Stan Z. Li arXiv:2305.15151/ICLR • code
SPIN-CGNN: Improved fixed backbone protein design with contact map-based graph construction and contact graph neural network Xing Zhang, Hongmei Yin, Fei Ling, Jian Zhan, Yaoqi Zhou bioRxiv 2023.07.07.548080/PLOS Computational Biology • code
ZetaDesign: an end-to-end deep learning method for protein sequence design and side-chain packing Junyu Yan and others Briefings in Bioinformatics, 2023 • code
Contextual protein encodings from equivariant graph transformers Sai Pooja Mahajan, Jeffrey A. Ruffolo, Jeffrey J. Gray bioRxiv 2023.07.15.549154 • code
Robust Design of Effective Allosteric Activators for Rsp5 E3 Ligase Using the Machine Learning Tool ProteinMPNN Hsi-Wen Kao, Wei-Lin Lu, Meng-Ru Ho, Yu-Fong Lin, Yun-Jung Hsieh, Tzu-Ping Ko, Shang-Te Danny Hsu, and Kuen-Phon Wu ACS Synthetic Biology (2023) • Supplementary
Rapid and automated design of two-component protein nanomaterials using ProteinMPNN Robbert J. de Haas, Natalie Brunette, Alex Goodson, Justas Dauparas, Sue Y. Yi, Erin C. Yang, Quinton Dowling, Hannah Nguyen, Alex Kang, Asim K. Bera, Banumathi Sankaran, Renko de Vries, David Baker, Neil P. King bioRxiv 2023.08.04.551935/Proceedings of the National Academy of Sciences 121.(13) • Supplementary • data
Rationally seeded computational protein design Katherine I. Albanese, Rokas Petrenas, Fabio Pirro, Elise A. Naudin, Ufuk Borucu, William M. Dawson, D. Arne Scott, Graham J. Leggett, Orion D. Weiner, Thomas A. A. Oliver, Derek N. Woolfson bioRxiv 2023.08.25.554789 • code
Computational design of sequence-specific DNA-binding proteins Cameron J Glasscock, Robert Pecoraro, Ryan McHugh, Lindsey A. Doyle, Wei Chen, Olivier Boivin, Beau Lonnquist, Emily Na, Yuliya Politanska, Hugh K Haddox, David Cox, Christoffer Norn, Brian Coventry, Inna Goreshnik, Dionne Vafeados, Gyu Rie Lee, Raluca Gordan, Barry L Stoddard, Frank DiMaio, David Baker bioRxiv 2023.09.20.558720 • Supplementary
Improving protein expression, stability, and function with ProteinMPNN Kiera H. Sumida, Reyes Núñez Franco, Indrek Kalvet, Samuel J. Pellock, Basile I. M. Wicky, Lukas F. Milles, Justas Dauparas, Jue Wang, Yakov Kipnis, Noel Jameson, Alex Kang, Joshmyn De La Cruz, Banumathi Sankaran, Asim K Bera, Gonzalo Jimenez Oses, David Baker bioRxiv 2023.10.03.560713/J. Am. Chem. Soc. 2024 • Supplementary
A Suite of Designed Protein Cages Using Machine Learning Algorithms and Protein Fragment-Based Protocols Kyle Meador, Roger Castells-Graells, Roman Aguirre, Michael R. Sawaya, Mark A. Arbing, Trent Sherman, Chethaka Senarathne, Todd O. Yeates bioRxiv 2023.10.09.561468 • code • colab
PROTEIN DESIGNER BASED ON SEQUENCE PROFILE USING ULTRAFAST SHAPE RECOGNITION Anonymous ICLR 2024
Inverse folding for antibody sequence design using deep learning Frédéric A. Dreyer, Daniel Cutting, Constantin Schneider, Henry Kenlay, Charlotte M. Deane arXiv:2310.19513
De novo design of allosterically switchable protein assemblies Arvind Pillai, Abbas Idris, Annika Philomin, Connor Weidle, Rebecca Skotheim, Philip J. Y. Leung, Adam Broerman, Cullen Demakis, Andrew J. Borst, Florian Praetorius, David Baker bioRxiv 2023.11.01.565167/Nature (2024) • code • data
ProRefiner: an entropy-based refining strategy for inverse protein folding with global graph attention Xinyi Zhou, Guangyong Chen, Junjie Ye, Ercheng Wang, Jun Zhang, Cong Mao, Zhanwei Li, Jianye Hao, Xingxu Huang, Jin Tang, Pheng Ann Heng Nature Communications • Supplementary • code
Engineered immunogens to elicit antibodies against conserved coronavirus epitopes A. Brenda Kapingidza, Daniel J. Marston, Caitlin Harris, Daniel Wrapp, Kaitlyn Winters, Dieter Mielke, Lu Xiaozhi, Qi Yin, Andrew Foulger, Rob Parks, Maggie Barr, Amanda Newman, Alexandra Schäfer, Amanda Eaton, Justine Mae Flores, Austin Harner, Nicholas J. Catanzaro Jr., Michael L. Mallory, Melissa D. Mattocks, Christopher Beverly, Brianna Rhodes, Katayoun Mansouri, Elizabeth Van Itallie, Pranay Vure, Brooke Dunn, Taylor Keyes, Sherry Stanfield-Oakley, Christopher W. Woods, Elizabeth A. Petzold, Emmanuel B. Walter, Kevin Wiehe, Robert J. Edwards, David C. Montefiori, Guido Ferrari, Ralph Baric, Derek W. Cain, Kevin O. Saunders, Barton F. Haynes & Mihai L. Azoitei Nat Commun 14, 7897 (2023) • code
DNDesign: Enhancing Physical Understanding of Protein Inverse Folding Model via Denoising Youhan Lee, Jaehoon Kim bioRxiv 2023.12.05.570298
In vitro validated antibody design against multiple therapeutic antigens using generative inverse folding Amir Shanehsazzadeh, Julian Alverio, George Kasun, Simon Levine, Jibran A Khan, Chelsea Chung, Nicolas Diaz, Breanna K Luton, Ysis Tarter, Cailen McCloskey, Katherine B Bateman, Hayley Carter, Dalton Chapman, Rebecca Consbruck, Alec Jaeger, Christa Kohnert, Gaelin Kopec-Belliveau, John M Sutton, Zheyuan Guo, Gustavo Canales, Kai Ejan, Emily Marsh, Alyssa Ruelos, Rylee Ripley, Brooke Stoddard, Rodante Caguiat, Kyra Chapman, Matthew Saunders, Jared Sharp, Douglas Ganini da Silva, Audree Feltner, Jake Ripley, Megan E Bryant, Danni Castillo, Joshua Meier, Christian M Stegmann, Katherine Moran, Christine Lemke, Shaheed Abdulhaqq, Lillian R Klug, Sharrol Bachas bioRxiv 2023.12.08.570889
SPDesign: protein sequence designer based on structural sequence profile using ultrafast shape recognition Hui Wang, Dong Liu, Kailong Zhao, Yajun Wang, Guijun Zhang bioRxiv 2023.12.14.571651/Briefings in Bioinformatics 25.3 (2024): bbae146 • website
De novo design of diverse small molecule binders and sensors using Shape Complementary Pseudocycles Linna An, Meerit Said, Long Tran, Sagardip Majumder, Inna Goreshnik, Gyu Rie Lee, David Juergens, Justas Dauparas, Ivan Anishchenko, Brian Coventry, Asim K Bera, Alex Kang, Paul M Levine, Valentina Alvarez, Arvindd Pillai, Christoffer Norn, David Feldman, Dmitri Zorine, Derrick R Hicks, Xinting Li, Mariana Garcia Sanchez, Dionne K Vafeados, Patrick J Salveson, Anastassia A Vorobieva, David Baker bioRxiv 2023.12.20.572602/Science385,276-282(2024) • code1, code2, code3
Atomic context-conditioned protein sequence design using LigandMPNN Justas Dauparas, Gyu Rie Lee, Robert Pecoraro, Linna An, Ivan Anishchenko, Cameron Glasscock, D. Baker bioRxiv 2023.12.22.573103 • code
Structure-conditioned masked language models for protein sequence design generalize beyond the native sequence space Deniz Akpinaroglu, Kosuke Seki, Amy Guo, Eleanor Zhu, Mark J. S. Kelly, Tanja Kortemme bioRxiv 2023.12.15.571823 • code
ProteinMPNN Recovers Complex Sequence Properties of Transmembrane β-Barrels Marissa D Dolorfino, Anastassia A Vorobieva bioRxiv 2024.01.16.575764 • code
DIProT: A deep learning based interactive toolkit for efficient and effective Protein design He, Jieling, Wenxu Wu, and Xiaowo Wang. Synthetic and Systems Biotechnology (2024)
Blueprinting extendable nanomaterials with standardized protein blocks Timothy F. Huddy, Yang Hsia, Ryan D. Kibler, Jinwei Xu, Neville Bethel, Deepesh Nagarajan, Rachel Redler, Philip J. Y. Leung, Connor Weidle, Alexis Courbet, Erin C. Yang, Asim K. Bera, Nicolas Coudray, S. John Calise, Fatima A. Davila-Hernandez, Hannah L. Han, Kenneth D. Carr, Zhe Li, Ryan McHugh, Gabriella Reggiano, Alex Kang, Banumathi Sankaran, Miles S. Dickinson, Brian Coventry, T. J. Brunette, Yulai Liu, Justas Dauparas, Andrew J. Borst, Damian Ekiert, Justin M. Kollman, Gira Bhabha & David Baker Nature (2024) • RosettaScripts
All-atom protein sequence design based on geometric deep learning Jiale Liu, Zheng Guo, Changsheng Zhang, Luhua Lai bioRxiv 2024.03.18.585651/Angew. Chem. Int. Ed. 2024 • code
Graphormer supervised de novo protein design method and function validation Junxi Mu, Zhengxin Li, Bo Zhang, Qi Zhang, Jamshed Iqbal, Abdul Wadood, Ting Wei, Yan Feng, Hai-Feng Chen Briefings in Bioinformatics 25.3 (2024): bbae135 • code
The Damietta Server: a comprehensive protein design toolkit Iwan Grin, Kateryna Maksymenko, Tobias Wörtwein, Mohammad ElGamacy Nucleic Acids Research, 2024;, gkae297 • website • ProteinMPNN-based • news, news2
Exploring the Potential of Structure-Based Deep Learning Approaches for T cell Receptor Design Helder V. Ribeiro-Filho, Gabriel E. Jara, João V. S. Guerra, Melyssa Cheung, Nathaniel R. Felbinger, José G. C. Pereira, Brian G. Pierce, Paulo S. Lopes-de-Oliveira bioRxiv 2024.04.19.590222 • code, code2
SurfPro: Functional Protein Design Based on Continuous Surface Zhenqiao Song, Tinglin Huang, Lei Li, Wengong Jin arXiv:2405.06693 • ProteinMPNN-based
Computational Design of Myoglobin-based Carbene Transferases for Monoterpene Derivatization Yiyang Sun, Yinian Tang, Jing Zhou, Bingchen Guo, Feiyan Yuan, Bo Yao, Yang Yu, Chun Li Biochemical and Biophysical Research Communications (2024) • code • LigandMPNN-based
UniIF: Unified Molecule Inverse Folding Zhangyang Gao, Jue Wang, Cheng Tan, Lirong Wu, Yufei Huang, Siyuan Li, Zhirui Ye, Stan Z. Li arXiv:2405.18968
Integrating MHC Class I visibility targets into the ProteinMPNN protein design process Hans-Christof Gasser, Diego A. Oyarzún, Javier Antonio Alfaro, Ajitha Rajan bioRxiv 2024.06.04.597365
A Top-Down Design Approach for Generating a Peptide PROTAC Drug Targeting Androgen Receptor for Androgenetic Alopecia Therapy Bohan Ma, Donghua Liu, Zhe Wang, Dize Zhang, Yanlin Jian, Kun Zhang, Tianyang Zhou, Yibo Gao, Yizeng Fan, Jian Ma, Yang Gao, Yule Chen, Si Chen, Jing Liu, Xiang Li, and Lei Li Journal of Medicinal Chemistry (2024)
Improving Inverse Folding models at Protein Stability Prediction without additional Training or Data Oliver Dutton, Sandro Bottaro, Michele Invernizzi, Istvan Redl, Albert Chung, Carlo Fisicaro, Fabio Airoldi, Stefano Ruschetta, Louie Henderson, Benjamin MJ Owens, Patrik Foerch, Kamil Tamiola bioRxiv 2024.06.15.599145 • ProteinMPNN/ESMIF-based
Kernel-Based Evaluation of Conditional Biological Sequence Models Pierre Glaser, Steffanie Paul, Alissa M Hummer, Charlotte Deane, Debora Susan Marks, Alan Nawzad Amin Proceedings of the 41st International Conference on Machine Learning, PMLR 235:15678-15705, 2024 • ProteinMPNN-based
CodonMPNN for Organism Specific and Codon Optimal Inverse Folding Hannes Stark, Umesh Padia, Julia Balla, Cameron Diao, George Church arXiv:2409.17265 • ProteinMPNN-based • code
Exploring the potential of structure-based deep learning approaches for T cell receptor design Helder V. Ribeiro-Filho, Gabriel E. Jara, João V. S. Guerra, Melyssa Cheung,Nathaniel R. Felbinger, José G. C. Pereira, Brian G. Pierce, Paulo S. Lopes-de-Oliveira PLoS Comput Biol 20(9) • ProteinMPNN-based • ESM-based
ProteusAI: An Open-Source and User-Friendly Platform for Machine Learning-Guided Protein Design and Engineering Jonathan Funk, Laura Machado, Samuel A. Bradley, Marta Napiorkowska, Rodrigo Gallegos-Dextre, Liubov Pashkova, Niklas G. Madsen, Henry Webel, Patrick Victor Phaneuf, Timothy P. Jenkins, Carlos G. Acevedo-Rocha Sr. bioRxiv 2024.10.01.616114 • ProteinMPNN-based • ESM-based
Improving Inverse Folding for Peptide Design with Diversity-regularized Direct Preference Optimization Ryan Park, Darren J. Hsu, C. Brian Roland, Maria Korshunova, Chen Tessler, Shie Mannor, Olivia Viessmann, Bruno Trentini arXiv:2410.19471
Computational design of developable therapeutic antibodies: efficient traversal of binder landscapes and rescue of escape mutations Frédéric A. Dreyer, Constantin Schneider, Aleksandr Kovaltsuk, Daniel Cutting, Matthew J. Byrne, Daniel A. Nissley, Newton Wahome, Henry Kenlay, Claire Marks, David Errington, Richard J. Gildea, David Damerell, Pedro Tizei, Wilawan Bunjobpol, John F. Darby, Ieva Drulyte, Daniel L. Hurdiss, Sachin Surade, Douglas E. V. Pires, Charlotte M. Deane bioRxiv 2024.10.03.616038 • code • AbMPNN-based
De novo protein design for novel folds using guided conditional Wasserstein generative adversarial networks Mostafa Karimi, Shaowen Zhu, Yue Cao, Yang Shen Journal of chemical information and modeling 60.12 (2020) • gcWGAN
HelixGAN: A bidirectional Generative Adversarial Network with search in latent space for generation under constraints Xuezhi Xie, Philip M. Kim Machine Learning for Structural Biology Workshop, NeurIPS 2021/Bioinformatics, 2023;, btad036 • code
Deep learning guided design of dynamic proteins Amy B. Guo, Deniz Akpinaroglu, Mark J.S. Kelly, Tanja Kortemme bioRxiv 2024.07.17.603962 • code • Supplementary
Generative models for graph-based protein design John Ingraham, Vikas K Garg, Dr.Regina Barzilay, Tommi Jaakkola NeurIPS 2019 • GraphTrans
Fold2Seq: A Joint Sequence (1D)-Fold (3D) Embedding-based Generative Model for Protein Design Yue Cao, Payel Das, Vijil Chenthamarakshan, Pin-Yu Chen, Igor Melnyk, Yang Shen International Conference on Machine Learning. PMLR, 2021
Rotamer-Free Protein Sequence Design Based on Deep Learning and Self-Consistency Yufeng Liu, Lu Zhang, Weilun Wang, Min Zhu, Chenchen Wang, Fudong Li, Jiahai Zhang, Houqiang Li, Quan Chen& Haiyan Liu Nature portfolio (2022)/Nature computational science(2022) • Supplementary • Comment • code
A Deep SE(3)-Equivariant Model for Learning Inverse Protein Folding Mmatthew McPartlon, Ben Lai, Jinbo Xu bioRxiv (2022)
Learning inverse folding from millions of predicted structures Chloe Hsu, Robert Verkuil, Jason Liu, Zeming Lin, Brian Hie, Tom Sercu, Adam Lerer, Alexander Rives bioRxiv (2022) • esm
Breaking boundaries in protein design with a new AI model that understands interactions with any kind of molecule LucianoSphere Towards Data Science
Accurate and efficient protein sequence design through learning concise local environment of residues Bin Huang, Tingwen Fan, Kaiyue Wang, Haicang Zhang, Chungong Yu, Shuyu Nie, Yangshuo Qi, Wei-Mou Zheng, Jian Han, Zheng Fan, Shiwei Sun, Sheng Ye, Huaiyi Yang, Dongbo Bu bioRxiv (2022)/Bioinformatics 39.3 (2023) • Supplementary • website • code
PeTriBERT : Augmenting BERT with tridimensional encoding for inverse protein folding and design Baldwin Dumortier, Antoine Liutkus, Clément Carré, Gabriel Krouk bioRxiv 2022.08.10.503344
Evolutionary-scale prediction of atomic level protein structure with a language model Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives bioRxiv 2022.07.20.500902 • blog • github
Structure-informed Language Models Are Protein Designers Zaixiang Zheng, Yifan Deng, Dongyu Xue, Yi Zhou, Fei YE, Quanquan Gu arXiv:2302.01649 • code::ByProt
Incorporating Pre-training Paradigm for Antibody Sequence-Structure Co-design Kaiyuan Gao, Lijun Wu, Jinhua Zhu, Tianbo Peng, Yingce Xia, Liang He, Shufang Xie, Tao Qin, Haiguang Liu, Kun He, Tie-Yan Liu arXiv:2211.08406 • code
A Text-guided Protein Design Framework Shengchao Liu, Yutao Zhu, Jiarui Lu, Zhao Xu, Weili Nie, Anthony Gitter, Chaowei Xiao, Jian Tang, Hongyu Guo, Anima Anandkumar arXiv:2302.04611 • code
An end-to-end deep learning method for protein side-chain packing and inverse folding McPartlon, Matthew, and Jinbo Xu Proceedings of the National Academy of Sciences 120.23 (2023) • code • Supplementary
Context-aware geometric deep learning for protein sequence design Lucien Krapp, Fernado Meireles, Luciano Abriata, Matteo Dal Peraro bioRxiv 2023.06.19.545381/Nature Communications, 2024 • code • News
De Novo Generation and Prioritization of Target-Binding Peptide Motifs from Sequence Alone Suhaas Bhat, Kalyan Palepu, Vivian Yudistyra, Lauren Hong, Venkata Srikar Kavirayuni, Tianlai Chen, Lin Zhao, Tian Wang, Sophia Vincoff, Pranam Chatterjee bioRxiv 2023.06.26.546591 • code • colab • Supplementary
ProstT5: Bilingual Language Model for Protein Sequence and Structure Michael Heinzinger Konstantin Weissenow, Joaquin Gomez Sanchez, Adrian Henkel, Martin Steinegger, Burkhard Rost bioRxiv 2023.07.23.550085 • Supplementary • code
De novo Protein Sequence Design Based on Deep Learning and Validation on CalB Hydrolase Junxi Mu, ZhengXin Li, Bo Zhang, Qi Zhang, Jamshed Iqbal, Abdul Wadood, Ting Wei, Yan Feng, Haifeng Chen bioRxiv 2023.08.01.551444 • code
Invariant point message passing for protein side chain packing and design Nicholas Z Randolph, Brian Kuhlman bioRxiv 2023.08.03.551328 • code
Atom-by-atom protein generation and beyond with language models Daniel Flam-Shepherd, Kevin Zhu, Alán Aspuru-Guzik arXiv:2308.09482
SaProt: Protein Language Modeling with Structure-aware Vocabulary Jin Su, Chenchen Han, Yuyang Zhou, Junjie Shan, Xibin Zhou, Fajie Yuan bioRxiv 2023.10.01.560349 • code
AntiFold: Improved antibody structure design using inverse folding Magnus Høie, Alissa Hummer, Tobias Olsen, Morten Nielsen, Charlotte Deane GenBio@NeurIPS2023 Spotlight • code • colab
MMDesign: Multi-Modality Transfer Learning for Generative Protein Design Jiangbin Zheng, Siyuan Li, Yufei Huang, Zhangyang Gao, Cheng Tan, Bozhen Hu, Jun Xia, Ge Wang, Stan Z. Li arXiv preprint arXiv:2312.06297 (2023)
ShapeProt: Top-down Protein Design with 3D Protein Shape Generative Model Lee, Youhan, and Jaehoon Kim. bioRxiv (2023): 2023-12
X-LoRA: Mixture of Low-Rank Adapter Experts, a Flexible Framework for Large Language Models with Applications in Protein Mechanics and Design Eric L. Buehler, Markus J. Buehler arXiv:2402.07148 • code • Model & weights
AntiFold: Improved antibody structure-based design using inverse folding Magnus Haraldson Høie, Alissa Hummer, Tobias H. Olsen, Broncio Aguilar-Sanjuan, Morten Nielsen, Charlotte M. Deane arXiv:2405.03370 • code • website • ESM-IF-based
Protein Design with StructureGPT: a Deep Learning Model for Protein Structure-to-Sequence Translation Nicanor Zalba Sr., Pablo Ursua-Medrano Sr., Humberto Bustince Sr. bioRxiv 2024.06.03.597105 • code • Supplementary
Adapting protein language models for structure-conditioned design Jeffrey A Ruffolo, Aadyot Bhatnagar, Joel Beazer, Stephen Nayfach, Jordan Russ, Emily Hill, Riffat Hussain, Joseph Gallagher, Ali Madani bioRxiv 2024.08.03.606485 • code • Supplementary • news
EMOCPD: Efficient Attention-based Models for Computational Protein Design Using Amino Acid Microenvironment Xiaoqi Ling, Cheng Cai, Demin Kong, Zhisheng Wei, Jing Wu, Lei Wang, Zhaohong Deng arXiv:2410.21069
DenseCPD: improving the accuracy of neural-network-based computational protein sequence design with DenseNet Qi, Yifei, and John ZH Zhang. Journal of chemical information and modeling 60.3 (2020) • code unavailable
De novo protein backbone generation based on diffusion with structured priors and adversarial training Yufeng Liu, Linghui Chen, Haiyan Liu bioRxiv 2022.12.17.520847/Nat Methods (2024) • code
Generative design of de novo proteins based on secondary-structure constraints using an attention-based diffusion model Bo Ni, David L. Kaplan, Markus J. Buehler Chem,(2023) • code • news
Graph Denoising Diffusion for Inverse Protein Folding Kai Yi, Bingxin Zhou, Yiqing Shen, Pietro Liò, Yu Guang Wang arXiv:2306.16819/NeurIPS 2023 • code
Conditional Protein Denoising Diffusion Generates Programmable Endonucleases Bingxin Zhou, Lirong Zheng, Banghao Wu, Kai Yi, Bozitao Zhong, Pietro Lio, Liang Hong bioRxiv 2023.08.10.552783
Diffusion in a quantized vector space generates non-idealized protein structures and predicts conformational distributions Liu Haiyan, Liu Yufeng, Chen Linghui bioRxiv 2023.11.18.567666
Fast non-autoregressive inverse folding with discrete diffusion John J. Yang, Jason Yim, Regina Barzilay, Tommi Jaakkola arXiv:2312.02447 • code
Diffusion Language Models Are Versatile Protein Learners Xinyou Wang, Zaixiang Zheng, Fei Ye, Dongyu Xue, Shujian Huang, Quanquan Gu arXiv:2402.18567
LéxFusion Levinthal paper not available • news • commercial
A conditional protein diffusion model generates artificial programmable endonuclease sequences with enhanced activity Bingxin Zhou, Lirong Zheng, Banghao Wu, Kai Yi, Bozitao Zhong, Yang Tan, Qian Liu, Pietro Liò, Liang Hong bioRxiv 2023.08.10.552783/Cell Discovery 10.1 (2024) • code
LaGDif: Latent Graph Diffusion Model for Efficient Protein Inverse Folding with Self-Ensemble Taoyu Wu, Yu Guang Wang, Yiqing Shen arXiv:2411.01737 • code
Bridge-IF: Learning Inverse Protein Folding with Markov Bridges Yiheng Zhu, Jialu Wu, Qiuyi Li, Jiahuan Yan, Mingze Yin, Wei Wu, Mingyang Li, Jieping Ye, Zheng Wang, Jian Wu arXiv:2411.02120 • code
Inverse Protein Folding Using Deep Bayesian Optimization Natalie Maus, Yimeng Zeng, Daniel Allen Anderson, Phillip Maffettone, Aaron Solomon, Peyton Greenside, Osbert Bastani, Jacob R. Gardner arXiv:2305.18089 • code
Harmonic Self-Conditioned Flow Matching for Multi-Ligand Docking and Binding Site Design Hannes Stärk, Bowen Jing, Regina Barzilay, Tommi Jaakkola arXiv:2310.05764 • code
Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein Design Chenyu Wang, Masatoshi Uehara, Yichun He, Amy Wang, Tommaso Biancalani, Avantika Lal, Tommi Jaakkola, Sergey Levine, Hanchen Wang, Aviv Regev arXiv:2410.13643 • code
Reinforcement learning on structure-conditioned categorical diffusion for protein inverse folding Yasha Ektefaie, Olivia Viessmann, Siddharth Narayanan, Drew Dresser, J. Mark Kim, Armen Mkrtchyan arXiv:2410.17173 • code
These models generate sequences from expected function.
Antibody complementarity determining region design using high-capacity machine learning Ge Liu, Haoyang Zeng, Jonas Mueller, Brandon Carter, Ziheng Wang, Jonas Schilz, Geraldine Horny, Michael E Birnbaum, Stefan Ewert, David K Gifford Bioinformatics 36.7 (2020): 2126-2133 • code
Protein design and variant prediction using autoregressive generative models Jung-Eun Shin, Adam J. Riesselman, Aaron W. Kollasch, Conor McMahon, Elana Simon, Chris Sander, Aashish Manglik, Andrew C. Kruse & Debora S. Marks Nature communications 12.1 (2021) • code::SeqDesign • mutation effect prediction • sequence generation • April 2021
Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning Derek M. Mason, Simon Friedensohn, Cédric R. Weber, Christian Jordi, Bastian Wagner, Simon M. Meng, Roy A. Ehling, Lucia Bonati, Jan Dahinden, Pablo Gainza, Bruno E. Correia & Sai T. Reddy Nature Biomedical Engineering 5.6 (2021) • code
Accelerated Engineering of ELP‐based Materials through Hybrid Biomimetic‐De Novo Predictive Molecular Design Timo Laakko, Antti Korkealaakso, Burcu Firatligil Yildirir, Piotr Batys, Ville Liljeström, Ari Hokkanen, Nonappa, Merja Penttilä, Anssi Laukkanen, Ali Miserez, Caj Södergård, Pezhman Mohammadi Advanced Materials (2024)
Machine learning-aided design and screening of an emergent protein function in synthetic cells Shunshi Kohyama, Béla P. Frohn, Leon Babl & Petra Schwille Nature Communications 15, 2010 (2024) • code
Variational auto-encoding of protein sequences Sam Sinai, Eric Kelsic, George M. Church, Martin A. Nowak arXiv preprint arXiv:1712.03346 (2017)
Design by adaptive sampling Brookes, David H., and Jennifer Listgarten. arXiv preprint arXiv:1810.03714 (2018)
Pepcvae: Semi-supervised targeted design of antimicrobial peptide sequences Payel Das, Kahini Wadhawan, Oscar Chang, Tom Sercu, Cicero Dos Santos, Matthew Riemer, Vijil Chenthamarakshan, Inkit Padhi, Aleksandra Mojsilovic arXiv preprint arXiv:1810.07743 (2018)
Deep generative models for T cell receptor protein sequences Kristian Davidsen, Branden J Olson, William S DeWitt III, Jean Feng, Elias Harkins, Philip Bradley, Frederick A Matsen IV Elife 8 (2019)
How to hallucinate functional proteins Costello, Zak, and Hector Garcia Martin. arXiv preprint arXiv:1903.00458 (2019)
Convergent selection in antibody repertoires is revealed by deep learning Simon Friedensohn, Daniel Neumeier, Tarik A Khan, Lucia Csepregi, Cristina Parola, Arthur R Gorter de Vries, Lena Erlach, Derek M Mason, Sai T Reddy BioRxiv (2020) • Supplementary • code available after publication
Variational autoencoder for generation of antimicrobial peptides Dean, Scott N., and Scott A. Walper. ACS omega 5.33 (2020)
Generating functional protein variants with variational autoencoders Alex Hawkins-Hooker, Florence Depardieu, Sebastien Baur, Guillaume Couairon, Arthur Chen, David Bikard PLoS computational biology 17.2 (2021)
Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations Payel Das, Tom Sercu, Kahini Wadhawan, Inkit Padhi, Sebastian Gehrmann, Flaviu Cipcigan, Vijil Chenthamarakshan, Hendrik Strobelt, Cicero dos Santos, Pin-Yu Chen, Yi Yan Yang, Jeremy P. K. Tan, James Hedrick, Jason Crain & Aleksandra Mojsilovic Nature Biomedical Engineering 5.6 (2021)
Deep generative models create new and diverse protein structures Zeming, Tom, Yann and Alexander. NeurIPS 2021
PepVAE: variational autoencoder framework for antimicrobial peptide generation and activity prediction Scott N. Dean, Jerome Anthony E. Alvarez, Dan Zabetakis, Scott A. Walper, and Anthony P. Malanoski Frontiers in microbiology 12 (2021) • code • Supplementary
HydrAMP: a deep generative model for antimicrobial peptide discovery Paulina Szymczak, Marcin Możejko, Tomasz Grzegorzek, Marta Bauer, Damian Neubauer, Michał Michalski, Jacek Sroka, Piotr Setny, Wojciech Kamysz, Ewa Szczurek bioRxiv (2022) • code
Therapeutic enzyme engineering using a generative neural network Andrew Giessel, Athanasios Dousis, Kanchana Ravichandran, Kevin Smith, Sreyoshi Sur, Iain McFadyen, Wei Zheng & Stuart Licht Scientific Reports 12.1 (2022)
GM-Pep: A High Efficiency Strategy to De Novo Design Functional Peptide Sequences Qushuo Chen, Changyan Yang, Yihao Xie, Yuqiang Wang, Xiaoxu Li, Kairong Wang, Jinqi Huang, and Wenjin Yan Journal of Chemical Information and Modeling (2022) • code
Mean Dimension of Generative Models for Protein Sequences Christoph Feinauer, Emanuele Borgonovo bioRxiv 2022.12.12.520028 • code
Prediction of designer-recombinases for DNA editing with generative deep learning Lukas Theo Schmitt, Maciej Paszkowski-Rogacz, Florian Jug & Frank Buchholz Nat Commun 13, 7966 (2022) • code • Supplementary
ProT-VAE: Protein Transformer Variational AutoEncoder for Functional Protein Design Emre Sevgen, Joshua Moller, Adrian Lange, John Parker, Sean Quigley, Jeff Mayer, Poonam Srivastava, Sitaram Gayatri, David Hosfield, Maria Korshunova, Micha Livne, Michelle Gill, Rama Ranganathan, Anthony B Costa, Andrew L Ferguson bioRxiv 2023.01.23.525232
Target specific peptide design using latent space approximate trajectory collector Tong Lin, Sijie Chen, Ruchira Basu, Dehu Pei, Xiaolin Cheng, Levent Burak Kara arXiv:2302.01435
Deep-learning generative models enable design of synthetic orthologs of a signaling protein Xinran Lian, Niksa Praljak, Andrew L. Ferguson, Rama Ranganathan Biophysical Journal 122.3 (2023): 311a
Designing a protein with emergent function by combined in silico, in vitro and in vivo screening Shunshi Kohyama, Bela Paul Frohn, Leon Babl, Petra Schwille bioRxiv 2023.02.16.528840 • Supplementary
ProteinVAE: Variational AutoEncoder for Translational Protein Design Suyue Lyu, Shahin Sowlati-Hashjin, Michael Garton bioRxiv 2023.03.04.531110/Nat Mach Intell (2024) • Supplementary • code
ProtWave-VAE: Integrating autoregressive sampling with latent-based inference for data-driven protein design Niksa Praljak, Xinran Lian, Rama Ranganathan, Andrew Ferguson bioRxiv 2023.04.23.537971 • Supplementary • code
Designing meaningful continuous representations of T cell receptor sequences with deep generative models Allen Y. Leary, Darius Scott, Namita T. Gupta, Janelle C. Waite, Dimitris Skokos, Gurinder S. Atwal, Peter G. Hawkins bioRxiv 2023.06.17.545423 • code
Utility of language model and physics-based approaches in modifying MHC Class-I immune-visibility for the design of vaccines and therapeutics Hans-Christof Gasser, Diego Oyarzun, Ajitha Rajan, Javier Alfaro bioRxiv 2023.07.10.548300
Cell-free biosynthesis combined with deep learning accelerates de novo-development of antimicrobial peptides Amir Pandi, David Adam, Amir Zare, Van Tuan Trinh, Stefan L. Schaefer, Marie Burt, Björn Klabunde, Elizaveta Bobkova, Manish Kushwaha, Yeganeh Foroughijabbari, Peter Braun, Christoph Spahn, Christian Preußer, Elke Pogge von Strandmann, Helge B. Bode, Heiner von Buttlar, Wilhelm Bertrams, Anna Lena Jung, Frank Abendroth, Bernd Schmeck, Gerhard Hummer, Olalla Vázquez & Tobias J. Erb Nature Communications 14.1 (2023) • code
Design of target specific peptide inhibitors using generative deep learning and molecular dynamics simulations Sijie Chen, Tong Lin, Ruchira Basu, Jeremy Ritchey, Shen Wang, Yichuan Luo, Xingcan Li, Dehua Pei, Levent Burak Kara & Xiaolin Cheng Nat Commun 15, 1611 (2024) • code
Deep-learning-based design of synthetic orthologs of SH3 signaling domains Xinran Lian, Nikša Praljak, Subu K. Subramanian, Sarah Wasinger, Rama Ranganathan, Andrew L. Ferguson Cell Systems (2024)
Feedback GAN for DNA optimizes protein functions Gupta, Anvita, and James Zou. Nature Machine Intelligence 1.2 (2019) • code
Generating protein sequences from antibiotic resistance genes data using Generative Adversarial Networks Chhibbar, Prabal, and Arpit Joshi. arXiv preprint arXiv:1904.13240 (2019)
ProGAN: Protein solubility generative adversarial nets for data augmentation in DNN framework Xi Han, Liheng Zhang, Kang Zhou, Xiaonan Wang Computers & Chemical Engineering 131 (2019)
GANDALF: Peptide Generation for Drug Design using Sequential and Structural Generative Adversarial Networks Rossetto, Allison, and Wenjin Zhou. Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. 2020
Designing feature-controlled humanoid antibody discovery libraries using generative adversarial networks Tileli Amimeur, Jeremy M. Shaver, Randal R. Ketchem, J. Alex Taylor, Rutilio H. Clark, Josh Smith, Danielle Van Citters, Christine C. Siska, Pauline Smidt, Megan Sprague, Bruce A. Kerwin, Dean Pettit BioRxiv (2020)
Generating ampicillin-level antimicrobial peptides with activity-aware generative adversarial networks Andrejs Tucs, Duy Phuoc Tran, Akiko Yumoto, Yoshihiro Ito, Takanori Uzawa, and Koji Tsuda ACS omega 5.36 (2020) • code
Conditional Generative Modeling for De Novo Protein Design with Hierarchical Functions Kucera, Tim, Matteo Togninalli, and Laetitia Meng-Papaxanthos bioRxiv (2021)/Bioinformatics 38.13 (2022) • code
Expanding functional protein sequence spaces using generative adversarial networks Donatas Repecka, Vykintas Jauniskis, Laurynas Karpus, Elzbieta Rembeza, Irmantas Rokaitis, Jan Zrimec, Simona Poviloniene, Audrius Laurynenas, Sandra Viknander, Wissam Abuajwa, Otto Savolainen, Rolandas Meskys, Martin K. M. Engqvist & Aleksej Zelezniak Nature Machine Intelligence 3.4 (2021) • code
A Generative Approach toward Precision Antimicrobial Peptide Design. Jonathon B. Ferrell, Jacob M. Remington, Colin M. Van Oort, Mona Sharafi, Reem Aboushousha, Yvonne Janssen-Heininger, Severin T. Schneebeli, Matthew J. Wargo, Safwan Wshah, Jianing Li BioRxiv (2021) • code
AMPGAN v2: Machine Learning-Guided Design of Antimicrobial Peptides Colin M. Van Oort, Jonathon B. Ferrell, Jacob M. Remington, Safwan Wshah, and Jianing Li Journal of chemical information and modeling 61.5 (2021)
DeepImmuno: deep learning-empowered prediction and generation of immunogenic peptides for T-cell immunity Guangyuan Li, Balaji Iyer, V B Surya Prasath, Yizhao Ni, Nathan Salomonis Briefings in bioinformatics 22.6 (2021) • code • web
PandoraGAN: Generating antiviral peptides using Generative Adversarial Network Shraddha Surana, Pooja Arora, Divye Singh, Deepti Sahasrabuddhe, Jayaraman Valadi bioRxiv (2021)
Feedback-AVPGAN: Feedback-guided generative adversarial network for generating antiviral peptides Kano Hasegawa, Yoshitaka Moriwaki, Tohru Terada, Cao Wei, and Kentaro Shimizu Journal of Bioinformatics and Computational Biology (2022) • code
Designing antimicrobial peptides using deep learning and molecular dynamic simulations Qiushi Cao, Cheng Ge, Xuejie Wang, Peta J Harvey, Zixuan Zhang, Yuan Ma, Xianghong Wang, Xinying Jia, Mehdi Mobli, David J Craik, Tao Jiang, Jinbo Yang, Zhiqiang Wei, Yan Wang, Shan Chang, Rilei Yu Briefings in Bioinformatics (2023)
Generative β-Hairpin Design Using a Residue-Based Physicochemical Property Landscape Vardhan Satalkar and Gemechis D. Degaga and Wei Li and Yui Tik Pang and Andrew C. McShan and James C. Gumbart and Julie C. Mitchell and Matthew P. Torres Biophysical Journal(2024) • code
De Novo Antimicrobial Peptide Design with Feedback Generative Adversarial Networks Michaela Areti Zervou, Effrosyni Doutsi, Yannis Pantazis, Panagiotis Tsakalides International Journal of Molecular Sciences 25.10 (2024) • code
Including protein large language models(pLLM) and autoregressive language models.
Progen: Language modeling for protein generation / Large language models generate functional protein sequences across diverse families Ali Madani, Bryan McCann, Nikhil Naik, Nitish Shirish Keskar, Namrata Anand, Raphael R. Eguchi, Po-Ssu Huang, Richard Socher arXiv preprint arXiv:2004.03497 (2020)/Nat Biotechnol (2023) • ProGen, CTRL
Signal peptides generated by attention-based neural networks Zachary Wu, Kevin K. Yang, Michael J. Liszka, Alycia Lee, Alina Batzilla, David Wernick, David P. Weiner, and Frances H. Arnold ACS Synthetic Biology 9.8 (2020)
ProtTrans: towards cracking the language of Life's code through self-supervised deep learning and high performance computing Ahmed Elnaggar, Michael Heinzinger, Christian Dallago, Ghalia Rehawi, Yu Wang, Llion Jones, Tom Gibbs, Tamas Feher, Christoph Angerer, Martin Steinegger,Debsindhu Bhowmik, and Burkhard Rost arXiv preprint arXiv:2007.06225 (2020) • code
Generative Language Modeling for Antibody Design Shuai, Richard W., Jeffrey A. Ruffolo, and Jeffrey J. Gray. bioRxiv (2021)/Cell Systems • Supplementary • code
Deep neural language modeling enables functional protein generation across families Ali Madani, Ben Krause, Eric R. Greene, Subu Subramanian, Benjamin P. Mohr, James M. Holton, Jose Luis Olmos Jr., Caiming Xiong, Zachary Z. Sun, Richard Socher, James S. Fraser, Nikhil Naik bioRxiv (2021)
Protein sequence sampling and prediction from structural data Gabriel A. Orellana, Javier Caceres-Delpiano, Roberto Ibañez, Michael P. Dunne, Leonardo Alvarez bioRxiv 2021.09.06.459171
Transformer-based protein generation with regularized latent space optimization Egbert Castro, Abhinav Godavarthi, Julian Rubinfien, Kevin Givechian, Dhananjay Bhaskar & Smita Krishnaswamy Nat Mach Intell (2022)/arXiv:2201.09948 • code
BioPhi: A platform for antibody design, humanization, and humanness evaluation based on natural antibody repertoires and deep learning David Prihoda, Jad Maamary, Andrew Waight, Veronica Juan, Laurence Fayadat-Dilman, Daniel Svozil, Danny A. Bitton mAbs. Vol. 14. No. 1. Taylor & Francis, 2022
Guided Generative Protein Design using Regularized Transformers Egbert Castro, Abhinav Godavarthi, Julian Rubinfien, Kevin B. Givechian, Dhananjay Bhaskar, Smita Krishnaswamy arXiv preprint arXiv:2201.09948 (2022)
Towards Controllable Protein design with Conditional Transformers Noelia Ferruz, Birte Höcker arXiv preprint arXiv:2201.07338 (2022)/Nature Machine Intelligence (2022) • review of Heading 5.4
ProtGPT2 is a deep unsupervised language model for protein design Noelia Ferruz, View ProfileSteffen Schmidt, View ProfileBirte Höcker bioRxiv/Nature Communications • model::huggingface datasets::hugingface • lecture • research highlights • news
Few Shot Protein Generation Ram, Soumya, and Tristan Bepler. arXiv preprint arXiv:2204.01168 (2022)
RITA: a Study on Scaling Up Generative Protein Sequence Models Daniel Hesslow, Niccoló Zanichelli, Pascal Notin, Iacopo Poli, Debora Marks arXiv preprint arXiv:2205.05789 (2022) • code
ProGen2: Exploring the Boundaries of Protein Language Models Erik Nijkamp, Jeffrey Ruffolo, Eli N. Weinstein, Nikhil Naik, Ali Madani arXiv:2206.13517 • code • guide
AbLang: an antibody language model for completing antibody sequences Tobias H Olsen, Iain H Moal, Charlotte M Deane Bioinformatics Advances, Volume 2, Issue 1, 2022, vbac046
Reprogramming Pretrained Language Models for Antibody Sequence Infilling Igor Melnyk, Vijil Chenthamarakshan, Pin-Yu Chen, Payel Das, Amit Dhurandhar, Inkit Padhi, Devleena Das arXiv:2210.07144 • code
AbBERT: Learning Antibody Humanness via Masked Language Modeling Denis Vashchenko, Sam Nguyen, Andre Goncalves, Felipe Leno da Silva, Brenden Petersen, Thomas Desautels, Daniel Faissol bioRxiv 2022.08.02.502236
Accelerating Antibody Design with Active Learning Seung-woo Seo, Min Woo Kwak, Eunji Kang, Chaeun Kim, Eunyoung Park, Tae Hyun Kang, Jinhan Kim bioRxiv 2022.09.12.507690
Reprogramming Large Pretrained Language Models for Antibody Sequence Infilling Igor Melnyk, Vijil Chenthamarakshan, Pin-Yu Chen, Payel Das, Amit Dhurandhar, Inkit Padhi, Devleena Das ICLR 2023/arXiv:2210.07144
Machine Learning Optimization of Candidate Antibodies Yields Highly Diverse Sub-nanomolar Affinity Antibody Libraries Lin Li, Esther Gupta, John Spaeth, Leslie Shing, Rafael Jaimes, Rajmonda Sulo Caceres, Tristan Bepler, Matthew E. Walsh bioRxiv 2022.10.07.502662 • Supplementary • code will be available
ZymCTRL: a conditional language model for the contollable generation of artificial enzymes Noelia Ferruz NeurIPS 2022/bioRxiv 2024.05.03.592223 • hugging face • poster
Generative Antibody Design for Complementary Chain Pairing Sequences through Encoder-Decoder Language Model Chu, Simon, and Kathy Wei. NeurIPS 2023 Generative AI and Biology (GenBio) Workshop. 2023/arXiv:2301.02748
Unlocking de novo antibody design with generative artificial intelligence Amir Shanehsazzadeh, Matt McPartlon, George Kasun, Andrea K. Steiger, John M. Sutton, Edriss Yassine, Cailen McCloskey, Robel Haile, Richard Shuai, Julian Alverio, Goran Rakocevic, Simon Levine, Jovan Cejovic, Jahir M. Gutierrez, Alex Morehead, Oleksii Dubrovskyi, Chelsea Chung, Breanna K. Luton, Nicolas Diaz, Christa Kohnert, Rebecca Consbruck, Hayley Carter, Chase LaCombe, Itti Bist, Phetsamay Vilaychack, Zahra Anderson, Lichen Xiu, Paul Bringas, Kimberly Alarcon, Bailey Knight, Macey Radach, Katherine Bateman, Gaelin Kopec-Belliveau, Dalton Chapman, Joshua Bennett, Abigail B. Ventura, Gustavo M. Canales, Muttappa Gowda, Kerianne A. Jackson, Rodante Caguiat, Amber Brown, Douglas Ganini da Silva, Zheyuan Guo, Shaheed Abdulhaqq, Lillian R. Klug, Miles Gander, Engin Yapici, Joshua Meier, Sharrol Bachas bioRxiv (2023): 2023-01 • data • news • blog • commercial
A universal deep-learning model for zinc finger design enables transcription factor reprogramming David M. Ichikawa, Osama Abdin, Nader Alerasool, Manjunatha Kogenaru, April L. Mueller, Han Wen, David O. Giganti, Gregory W. Goldberg, Samantha Adams, Jeffrey M. Spencer, Rozita Razavi, Satra Nim, Hong Zheng, Courtney Gionco, Finnegan T. Clark, Alexey Strokach, Timothy R. Hughes, Timothee Lionnet, Mikko Taipale, Philip M. Kim & Marcus B. Noyes Nat Biotechnol (2023)
XuperNovo®/ProteinGPT XtalPi news • news2 • website • commercial
Evaluating Prompt Tuning for Conditional Protein Sequence Generation Andrea Nathansen, Kevin Klein, Bernhard Y. Renard, Melania Nowicka, Jakub M. Bartoszewicz bioRxiv 2023.02.28.530492 • code
AB-Gen: Antibody Library Design with Generative Pre-trained Transformer and Deep Reinforcement Learning Xiaopeng Xu, Tiantian Xu, Juexiao Zhou, Xingyu Liao, Ruochi Zhang, Yu Wang, Lu Zhang, Xin Gao bioRxiv 2023.03.17.533102 • code • Supplementary • data
Unsupervised cross-domain translation via deep learning and adversarial attention neural networks and application to music-inspired protein designs Buehler, Markus J. Patterns 4.3 (2023) • code
ProtFIM: Fill-in-Middle Protein Sequence Design via Protein Language Models Lee, Youhan, and Hasun Yu. arXiv preprint arXiv:2303.16452 (2023)/ICLR 2023
REXzyme: A Translation Machine for the Generation of New-to-Nature Enzymes Sebastian Lindner, Michael Heinzinger, Noelia Ferruz paper coming soon • hugging face
MPM4: AI Text2Protein Breakthrough Tackles the Molecule Programming Challenge 310.ai news • repo • commercial
De Novo Design of Peptide Binders to Conformationally Diverse Targets with Contrastive Language Modeling Suhaas Bhat, Kalyan Palepu, Lauren Hong, Joey Mao, Tianzheng Ye, Rema Iyer, Lin Zhao, Tianlai Chen, Sophia Vincoff, Rio Watson, Tian Wang, Divya Srijay, Venkata Srikar Kavirayuni, Kseniia Kholina, Shrey Goel, Pranay Vure, Aniruddha H Desphande, Scott Soderling, Matthew DeLisa, Pranam Chatterjee bioRxiv 2023.06.26.546591 • code • Supplementary
xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of Protein Bo Chen, Xingyi Cheng, Li-ao Gengyang, Shen Li, Xin Zeng, Boyan Wang, Gong Jing, Chiming Liu, Aohan Zeng, Yuxiao Dong, Jie Tang, Le Song bioRxiv 2023.07.05.547496 • news • website • commercial
TULIP - a Transformer based Unsupervised Language model for Interacting Peptides and T-cell receptors that generalizes to unseen epitopes Barthelemy Meynard-Piganeau, Christoph Feinauer, Martin Weigt, Aleksandra M Walczak, Thierry Mora bioRxiv 2023.07.19.549669 • code
Efficient and accurate sequence generation with small-scale protein language models Yaiza Serrano, Sergi Roda, Victor Guallar, Alexis Molina bioRxiv 2023.08.04.551626
IMPROVING ANTIBODY AFFINITY USING LABORATORY DATA WITH LANGUAGE MODEL GUIDED DESIGN Ben Krause, Subu Subramanian, Tom Yuan, Marisa Yang, Aaron Sato, Nikhil Naik bioRxiv 2023.09.13.557505
NL2ProGPT: Taming Large Language Model for Conversational Protein Design Anonymous ICLR 2024
PepMLM: Target Sequence-Conditioned Generation of Peptide Binders via Masked Language Modeling Tianlai Chen, Sarah Pertsemlidis, Rio Watson, Venkata Srikar Kavirayuni, Ashley Hsu, Pranay Vure, Rishab Pulugurta, Sophia Vincoff, Lauren Hong, Tian Wang, Vivian Yudistyra, Elena Haarer, Lin Zhao, Pranam Chatterjee arXiv:2310.03842 • code
De novo generation of antibody CDRH3 with a pre-trained generative large language model HaoHuai He, Bing He, Lei Guan, Yu Zhao, Guanxing Chen, Qingge Zhu, Calvin Yu-Chian Chen, Ting Li, Jianhua Yao bioRxiv 2023.10.17.562827/Nature Communications 15.1 (2024) • code • data
SaLT&PepPr is an interface-predicting language model for designing peptide-guided protein degraders Garyk Brixi, Tianzheng Ye, Lauren Hong, Tian Wang, Connor Monticello, Natalia Lopez-Barbosa, Sophia Vincoff, Vivian Yudistyra, Lin Zhao, Elena Haarer, Tianlai Chen, Sarah Pertsemlidis, Kalyan Palepu, Suhaas Bhat, Jayani Christopher, Xinning Li, Tong Liu, Sue Zhang, Lillian Petersen, Matthew P. DeLisa & Pranam Chatterjee Commun Biol 6, 1081 (2023) • code
Binary Discriminator Facilitates GPT-based Protein Design Zishuo Zeng, Rufang Xu, Jin Guo, Xiaozhou Luo bioRxiv 2023.11.20.567789 • code • Supplementary
ProteinNPT: Improving Protein Property Prediction and Design with Non-Parametric Transformers Pascal Notin, Ruben Weitzman, Debora S Marks, Yarin Gal bioRxiv 2023.12.06.570473 • code
The promises of large language models for protein design and modeling Giorgio Valentini, Dario Malchiodi, Jessica Gliozzo, Marco Mesiti, Mauricio Soto-Gomez, Alberto Cabri, Justin Reese, Elena Casiraghi, and Peter N. Robinson Frontiers in Bioinformatics 3 (2023)
Conversational Drug Editing Using Retrieval and Domain Feedback Shengchao Liu, Jiongxiao Wang, Yijin Yang, Chengpeng Wang, Ling Liu, Hongyu Guo, Chaowei Xiao ICLR (2024) • code • website
ProtAgents: Protein discovery via large language model multi-agent collaborations combining physics and machine learning Alireza Ghafarollahi, Markus J. Buehler arXiv:2402.04268 • code
Designing proteins with language models Ruffolo, J.A., Madani, A. Nat Biotechnol 42, 200–202 (2024) • review
ProLLaMA: A Protein Large Language Model for Multi-Task Protein Language Processing Liuzhenghao Lv, Zongying Lin, Hao Li, Yuyang Liu, Jiaxi Cui, Calvin Yu-Chian Chen, Li Yuan, Yonghong Tian arXiv:2402.16445 • code
Combining machine learning with structure-based protein design to predict and engineer post-translational modifications of proteins Moritz Ertelt, Vikram Khipple Mulligan, Jack B. Maguire, Sergey Lyskov, Rocco Moretti, Torben Schiffner, Jens Meiler, Clara T. Schoeder PLOS Computational Biology 20(3): e1011939 • code
Combining Rosetta Sequence Design with Protein Language Model Predictions Using Evolutionary Scale Modeling (ESM) as Restraint Moritz Ertelt, Jens Meiler, and Clara T. Schoeder ACS Synth. Biol. 2024 • code
Design of Antigen-Specific Antibody CDRH3 Sequences Using AI and Germline-Based Templates Toma M. Marinov, Alexandra A. Abu-Shmais, Alexis K. Janke, Ivelin S. Georgiev bioRxiv 2024.03.22.586241
Design of highly functional genome editors by modeling the universe of CRISPR-Cas sequences Jeffrey A. Ruffolo, Stephen Nayfach, Joseph Gallagher, Aadyot Bhatnagar, Joel Beazer, Riffat Hussain, Jordan Russ, Jennifer Yip, Emily Hill, Martin Pacesa, Alexander J. Meeske, Peter Cameron, Ali Madani bioRxiv 2024.04.22.590591 • code
Functional Protein Design with Local Domain Alignment Chaohao Yuan, Songyou Li, Geyan Ye, Yikun Zhang, Long-Kai Huang, Wenbing Huang, Wei Liu, Jianhua Yao, Yu Rong arXiv:2404.16866
The Continuous Language of Protein Structure Lukas Billera, Anton Oresten, Aron Stålmarck, Kenta Sato, Mateusz Kaduk, Ben Murrell bioRxiv 2024.05.11.593685 • code
Generative Enzyme Design Guided by Functionally Important Sites and Small-Molecule Substrates Zhenqiao Song, Yunlong Zhao, Wenxian Shi, Wengong Jin, Yang Yang, Lei Li arXiv:2405.08205/ICML 2024 • code
A generative foundation model for antibody sequence understanding Justin Barton, Aretas Gaspariunas, David A Yadin, Jorge Dias, Francesca L Nice, Danielle H Minns, Olivia Snudden, Chelsea Povall, Sara Valle Tomas, Harry Dobson, James HR Farmery, Jinwoo Leem, Jacob D Galson bioRxiv 2024.05.22.594943 • huggingface
Decoupled Sequence and Structure Generation for Realistic Antibody Design Nayoung Kim, Minsu Kim, Sungsoo Ahn, Jinkyoo Park arXiv:2402.05982/Under review for TMLR • code
Addressing the antibody germline bias and its effect on language models for improved antibody design Tobias H. Olsen, Iain H. Moal, Charlotte M. Deane bioRxiv 2024.02.02.578678/Bioinformatics (2024): btae618 • code
MoFormer: Multi-objective Antimicrobial Peptide Generation Based on Conditional Transformer Joint Multi-modal Fusion Descriptor Li Wang, Xiangzheng Fu, Jiahao Yang, Xinyi Zhang, Xiucai Ye, Yiping Liu, Tetsuya Sakurai, Xiangxiang Zeng arXiv:2406.00735
HELM-GPT: de novo macrocyclic peptide design using generative pre-trained transformer Xiaopeng Xu, Chencheng Xu, Wenjia He, Lesong Wei, Haoyang Li, Juexiao Zhou, Ruochi Zhang, Yu Wang, Yuanpeng Xiong, Xin Gao Bioinformatics (2024): btae364 • code
Unifying Sequences, Structures, and Descriptions for Any-to-Any Protein Generation with the Large Multimodal Model HelixProtX Zhiyuan Chen, Tianhao Chen, Chenggang Xie, Yang Xue, Xiaonan Zhang, Jingbo Zhou, Xiaomin Fang arXiv:2407.09274 • code
A foundation model approach to guide antimicrobial peptide design in the era of artificial intelligence driven scientific discovery Jike Wang, Jianwen Feng, Yu Kang, Peichen Pan, Jingxuan Ge, Yan Wang, Mingyang Wang, Zhenxing Wu, Xingcai Zhang, Jiameng Yu, Xujun Zhang, Tianyue Wang, Lirong Wen, Guangning Yan, Yafeng Deng, Hui Shi, Chang-Yu Hsieh, Zhihui Jiang, Tingjun Hou arXiv:2407.12296 • code
Conditional Sequence-Structure Integration: A Novel Approach for Precision Antibody Engineering and Affinity Optimization Benyamin Jamialahmadi, Mahmood Chamankhah, Mohammad Kohandel, Ali Ghodsi bioRxiv 2024.07.16.603820 • blog
moPPIt: De Novo Generation of Motif-Specific Binders with Protein Language Models Tong Chen, Yinuo Zhang, Pranam Chatterjee bioRxiv 2024.07.31.606098 • code
Toward De Novo Protein Design from Natural Language Fengyuan Dai, Yuliang Fan, Jin Su, Chentong Wang, Chenchen Han, Xibin Zhou, Jianming Liu, Hui Qian, Shunzhi Wang, Anping Zeng, Yajie Wang, Fajie Yuan bioRxiv 2024.08.01.606258
Design Proteins Using Large Language Models: Enhancements and Comparative Analyses Kamyar Zeinalipour, Neda Jamshidi, Monica Bianchini, Marco Maggini, Marco Gori arXiv:2408.06396 • code
Miniaturizing, Modifying, and Augmenting Nature's Proteins with Raygun Kapil Devkota, Daichi Shonai, Joey Mao, Scott H Soderling, Rohit Singh bioRxiv 2024.08.13.607858 • code
TourSynbio: A Multi-Modal Large Model and Agent Framework to Bridge Text and Protein Sequences for Protein Engineering Yiqing Shen, Zan Chen, Michail Mamalakis, Yungeng Liu, Tianbin Li, Yanzhou Su, Junjun He, Pietro Liò, Yu Guang Wang arXiv:2408.15299 • code • model • website • news • commercial
AbGPT: De Novo Antibody Design via Generative Language Modeling Desmond Kuan, Amir Barati Farimani arXiv:2409.06090 • code
PepINVENT: Generative peptide design beyond the natural amino acids Gökçe Geylan, Jon Paul Janet, Alessandro Tibo, Jiazhen He, Atanas Patronov, Mikhail Kabeshov, Florian David, Werngard Czechtizky, Ola Engkvist, Leonardo De Maria arXiv:2409.14040
Conditional Enzyme Generation Using Protein Language Models with Adapters Jason Yang, Aadyot Bhatnagar, Jeffrey A. Ruffolo, Ali Madani arXiv:2410.03634 • code
Re-examining Metrics for Success in Machine Learning, from Fairness and Interpretability to Protein Design Frances Ding Diss. University of California, Berkeley, 2024 • Phd thesis
Computational design of target-specific linear peptide binders with TransformerBeta Haowen Zhao, Francesco A. Aprile, Barbara Bravi arXiv:2410.16302 • code
Structure Language Models for Protein Conformation Generation Jiarui Lu, Xiaoyin Chen, Stephen Zhewen Lu, Chence Shi, Hongyu Guo, Yoshua Bengio, Jian Tang arXiv:2410.18403 • code
Peptide-GPT: Generative Design of Peptides using Generative Pre-trained Transformers and Bio-informatic Supervision Aayush Shah, Chakradhar Guntuboina, Amir Barati Farimani arXiv:2410.19222 • code
An adaptive autoregressive diffusion approach to design active humanized antibody and nanobody Jian Ma, Fandi Wu, Tingyang Xu, Shaoyong Xu, Wei Liu, Divin Yan, Qifeng Bai, Jianhua Yao bioRxiv 2024.10.22.619416 • code
Concept Bottleneck Language Models For protein design
Aya Abdelsalam Ismail, Tuomas Oikarinen, Amy Wang, Julius Adebayo, Samuel Stanton, Taylor Joren, Joseph Kleinhenz, Allen Goodman, Héctor Corrada Bravo, Kyunghyun Cho, Nathan C. Frey
arXiv:2411.06090
De novo design of triosephosphate isomerases using generative language models
Sergio Romero-Romero, Alexander E. Braun, Timo Kossendey, Noelia Ferruz, Steffen Schmidt, Birte Höcker
bioRxiv 2024.11.10.622869
Natural Language Prompts Guide the Design of Novel Functional Protein Sequences
Nikša Praljak, Hugh Yeh, Miranda Moore, Michael Socolich, Rama Ranganathan, Andrew L. Ferguson
bioRxiv 2024.11.11.622734
Multi-purpose controllable protein generation via prompted language models
Zeyuan Wang, Binbin Chen, Keyan Ding, Jiawen Cao, Ming Qin, Yadan Niu, Xiang Zhuang, Xiaotong Li, Kehua Feng, Tong Xu, Ningyu Zhang, Haoran Yu, Qiang Zhang, Huajun Chen
bioRxiv 2024.11.17.624051
Optimistic Games for Combinatorial Bayesian Optimization with Applications to Protein Design Melis Ilayda Bal, Pier Giuseppe Sessa, Mojmir Mutny, Andreas Krause NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning in the Real World, 2023/arXiv:2409.18582
Discovering de novo peptide substrates for enzymes using machine learning Lorillee Tallorin, JiaLei Wang, Woojoo E. Kim, Swagat Sahu, Nicolas M. Kosa, Pu Yang, Matthew Thompson, Michael K. Gilson, Peter I. Frazier, Michael D. Burkart & Nathan C. Gianneschi Nature communications 9.1 (2018) • code
Biological Sequences Design using Batched Bayesian Optimization David Belanger, Suhani Vora, Zelda Mariet, Ramya Deshpande, David Dohan, Christof Angermueller, Kevin Murphy, Olivier Chapelle, Lucy Colwell Machine Learning and the Physical Sciences Workshop (NeurIPS 2019)
Lattice protein design using Bayesian learning Takahashi, Tomoei, George Chikenji, and Kei Tokita. arXiv:2003.06601/Physical Review E 104.1 (2021): 014404
Now What Sequence? Pre-trained Ensembles for Bayesian Optimization of Protein Sequences Ziyue Yang, Katarina A Milas, Andrew D White bioRxiv 2022.08.05.502972 • code • Supplementary • Colab
AntBO: Towards Real-World Automated Antibody Design with Combinatorial Bayesian Optimisation Asif Khan, Alexander I. Cowen-Rivers, Antoine Grosnit, Derrick-Goh-Xin Deik, Philippe A. Robert, Victor Greiff, Eva Smorodina, Puneet Rawat, Kamil Dreczkowski, Rahmad Akbar, Rasul Tutunov, Dany Bou-Ammar, Jun Wang, Amos Storkey, Haitham Bou-Ammar arXiv preprint (2022)/Cell Reports Methods (2023): 100374
Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders Samuel Stanton, Wesley Maddox, Nate Gruver, Phillip Maffettone, Emily Delaney, Peyton Greenside, Andrew Gordon Wilson ICML 2022 • code
Statistical Mechanics of Protein Design Takahashi, Tomoei, George Chikenji, and Kei Tokita. arXiv preprint arXiv:2205.03696 (2022)
PropertyDAG: Multi-objective Bayesian optimization of partially ordered, mixed-variable properties for biological sequence design Ji Won Park, Samuel Stanton, Saeed Saremi, Andrew Watkins, Henri Dwyer, Vladimir Gligorijevic, Richard Bonneau, Stephen Ra, Kyunghyun Cho arXiv:2210.04096
A probabilistic view of protein stability, conformational specificity, and design Jacob A. Stern, Tyler J. Free, Kimberlee L. Stern, Spencer Gardiner, Nicholas A. Dalley, Bradley C. Bundy, Joshua L. Price, David Wingate, Dennis Della Corte bioRxiv 2022.12.28.521825 • Supplementary
Design of antimicrobial peptides containing non-proteinogenic amino acids using multi-objective Bayesian optimisation Murakami Y, Ishida S, Demizu Y, Terayama K. ChemRxiv. Cambridge: Cambridge Open Engage; 2023 • code
Vaxformer: Antigenicity-controlled Transformer for Vaccine Design Against SARS-CoV-2 Aryo Pradipta Gema, Michał Kobiela, Achille Fraisse, Ajitha Rajan, Diego A. Oyarzún, Javier Antonio Alfaro arXiv:2305.11194 • code
Sample-efficient Antibody Design through Protein Language Model for Risk-aware Batch Bayesian Optimization Yanzheng Wang, Boyue Wang, Tianyu Shi, Jie Fu, Yi Zhou, Zhizhuo Zhang bioRxiv 2023.11.06.565922
Integrating Protein Structure Prediction and Bayesian Optimization for Peptide Design Negin Manshour, Fei He, Duolin Wang, Dong Xu NeurIPS 2023 Generative AI and Biology (GenBio) Workshop. 2023
Model-based reinforcement learning for biological sequence design Christof Angermueller, David Dohan, David Belanger, Ramya Deshpande, Kevin Murphy, Lucy Colwell International conference on learning representations. 2019
Structured Q-learning For Antibody Design Alexander I. Cowen-Rivers, Philip John Gorinski, Aivar Sootla, Asif Khan, Liu Furui, Jun Wang, Jan Peters, Haitham Bou Ammar arXiv preprint arXiv:2209.04698 (2022)
Protein Sequence Design in a Latent Space via Model-based Reinforcement Learning Minji Lee, Luiz Felipe Vecchietti, Hyunkyu Jung, Hyunjoo Ro, Ho Min Kim, Meeyoung Cha ICLR 2023/NeurIPS 2022 • Supplementary
Designing Biological Sequences via Meta-Reinforcement Learning and Bayesian Optimization Leo Feng, Padideh Nouri, Aneri Muni, Yoshua Bengio, Pierre-Luc Bacon arXiv:2209.06259/NeurIPS 2022 • poster
Self-play reinforcement learning guides protein engineering Yi Wang, Hui Tang, Lichao Huang, Lulu Pan, Lixiang Yang, Huanming Yang, Feng Mu & Meng Yang Nature Machine Intelligence (2023) • code
Curiosity Driven Protein Sequence Generation via Reinforcement Learning Anonymous ICLR 2024
Stable Online and Offline Reinforcement Learning for Antibody CDRH3 Design Yannick Vogt, Mehdi Naouar, Maria Kalweit, Christoph Cornelius Miething, Justus Duyster, Roland Mertelsmann, Gabriel Kalweit, Joschka Boedecker arXiv:2401.05341
Peptide Vaccine Design by Evolutionary Multi-Objective Optimization Dan-Xuan Liu, Yi-Heng Xu, Chao Qian arXiv:2406.05743
Reinforcement Learning for Sequence Design Leveraging Protein Language Models Jithendaraa Subramanian, Shivakanth Sujit, Niloy Irtisam, Umong Sain, Derek Nowrouzezahrai, Samira Ebrahimi Kahou, Riashat Islam arXiv:2407.03154
BetterBodies: Reinforcement Learning guided Diffusion for Antibody Sequence Design Yannick Vogt, Mehdi Naouar, Maria Kalweit, Christoph Cornelius Miething, Justus Duyster, Joschka Boedecker, Gabriel Kalweit arXiv:2409.16298
Reinforcement learning-driven exploration of peptide space: accelerating generation of drug-like peptides Qian Wang, Xiaotong Hu, Zhiqiang Wei, Hao Lu, Hao Liu Briefings in Bioinformatics 25.5 (2024): bbae444 • code
Biological Sequence Design with GFlowNets Moksh Jain, Emmanuel Bengio, Alex-Hernandez Garcia, Jarrid Rector-Brooks, Bonaventure F. P. Dossou, Chanakya Ekbote, Jie Fu, Tianyu Zhang, Micheal Kilgour, Dinghuai Zhang, Lena Simine, Payel Das, Yoshua Bengio arXiv preprint arXiv:2203.04115 (2022) • lecture
Deep learning to design nuclear-targeting abiotic miniproteins Carly K. Schissel, Somesh Mohapatra, Justin M. Wolfe, Colin M. Fadzen, Kamela Bellovoda, Chia-Ling Wu, Jenna A. Wood, Annika B. Malmberg, Andrei Loas, Rafael Gómez-Bombarelli & Bradley L. Pentelute Nature Chemistry 13.10 (2021) • code
Recurrent neural network model for constructive peptide design Müller, Alex T., Jan A. Hiss, and Gisbert Schneider. Journal of chemical information and modeling 58.2 (2018)
Machine learning designs non-hemolytic antimicrobial peptides Alice Capecchi, Xingguang Cai, Hippolyte Personne, Thilo Köhler, Christian van Delden, and Jean-Louis Reymond Chemical Science 12.26 (2021)
Using molecular dynamics simulations to prioritize and understand AI-generated cell penetrating peptides Duy Phuoc Tran, Seiichi Tada, Akiko Yumoto, Akio Kitao, Yoshihiro Ito, Takanori Uzawa & Koji Tsuda Scientific reports 11.1 (2021)
De novo antioxidant peptide design via machine learning and DFT studies Parsa Hesamzadeh, Abdolvahab Seif, Kazem Mahmoudzadeh, Mokhtar Ganjali Koli, Amrollah Mostafazadeh, Kosar Nayeri, Zohreh Mirjafary & Hamid Saeidian Scientific Reports 14.1 (2024) • code
Computational antimicrobial peptide design and evaluation against multidrug-resistant clinical isolates of bacteria Deepesh Nagarajan, Tushar Nagarajan, Natasha Roy, Omkar Kulkarni, Sathyabaarathi Ravichandran, Madhulika Mishra Dipshikha Chakravortty, Nagasuma Chandra Journal of Biological Chemistry 293.10 (2018)
Deep learning enables the design of functional de novo antimicrobial proteins Javier Caceres-Delpiano, Roberto Ibañez, Patricio Alegre, Cynthia Sanhueza, Romualdo Paz-Fiblas, Simon Correa, Pedro Retamal, Juan Cristóbal Jiménez, Leonardo Álvarez bioRxiv (2020)
ECNet is an evolutionary context-integrated deep learning framework for protein engineering Yunan Luo, Guangde Jiang, Tianhao Yu, Yang Liu, Lam Vo, Hantian Ding, Yufeng Su, Wesley Wei Qian, Huimin Zhao & Jian Peng Nature communications 12.1 (2021)
Deep learning for novel antimicrobial peptide design Wang, Christina, Sam Garlick, and Mire Zloh. Biomolecules 11.3 (2021)
Antibody design using LSTM based deep generative model from phage display library for affinity maturation Koichiro Saka, Taro Kakuzaki, Shoichi Metsugi, Daiki Kashiwagi, Kenji Yoshida, Manabu Wada, Hiroyuki Tsunoda & Reiji Teramoto Scientific reports 11.1 (2021)
In silico proof of principle of machine learning-based antibody design at unconstrained scale Akbar, Rahmad, et al. Mabs. Vol. 14. No. 1. Taylor & Francis, 2022 • code
Large-scale design and refinement of stable proteins using sequence-only models Jedediah M. Singer , Scott Novotney, Devin Strickland, Hugh K. Haddox, Nicholas Leiby, Gabriel J. Rocklin, Cameron M. Chow, Anindya Roy, Asim K. Bera, Francis C. Motta, Longxing Cao, Eva-Maria Strauch, Tamuka M. Chidyausiku, Alex Ford, Ethan Ho, Alexander Zaitzeff, Craig O. Mackenzie, Hamed Eramian, Frank DiMaio, Gevorg Grigoryan, Matthew Vaughn, Lance J. Stewart, David Baker, Eric Klavins PloS one 17.3 (2022) • code
Deep-learning based bioactive therapeutic peptides generation and screening Haiping Zhang, Konda Mani Saravanan, Yanjie Wei, Yang Jiao, Yang Yang, Yi Pan, Xuli Wu, John Z.H. Zhang bioRxiv 2022.11.14.516530 • code • Supplementary
Deep-learning based bioactive peptides generation and screening against Xanthine oxidase Haiping Zhang, Konda Mani Saravanan, John Z.H. Zhang, Xuli Wu bioRxiv 2023.01.11.523536
Deep Learning-Based Bioactive Therapeutic Peptide Generation and Screening Haiping Zhang, Konda Mani Saravanan, Yanjie Wei, Yang Jiao, Yang Yang, Yi Pan, Xuli Wu, and John Z. H. Zhang Journal of Chemical Information and Modeling 63.3 (2023) • code
Bio-xLSTM: Generative modeling, representation and in-context learning of biological and chemical sequences Niklas Schmidinger, Lisa Schneckenreiter, Philipp Seidl, Johannes Schimunek, Pieter-Jan Hoedt, Johannes Brandstetter, Andreas Mayr, Sohvi Luukkonen, Sepp Hochreiter, Günter Klambauer arXiv:2411.04165
Efficient generative modeling of protein sequences using simple autoregressive models Jeanne Trinquier, Guido Uguzzoni, Andrea Pagnani, Francesco Zamponi & Martin Weigt Nature communications 12.1 (2021): 1-11 • code
Conformal prediction for the design problem Clara Fannjiang, Stephen Bates, Anastasios N. Angelopoulos, Jennifer Listgarten, Michael I. Jordan arXiv:2202.03613v4 • code
How pairwise coevolutionary models capture the collective residue variability in proteins? Figliuzzi, Matteo, Pierre Barrat-Charlaix, and Martin Weigt. Molecular biology and evolution 35.4 (2018): 1018-1027 • code
A Pareto-optimal compositional energy-based model for sampling and optimization of protein sequences Nataša Tagasovska, Nathan C. Frey, Andreas Loukas, Isidro Hötzel, Julien Lafrance-Vanasse, Ryan Lewis Kelly, Yan Wu, Arvind Rajpal, Richard Bonneau, Kyunghyun Cho, Stephen Ra, Vladimir Gligorijević arXiv:2210.10838 • slides
Computational design of novel Cas9 PAM-interacting domains using evolution-based modelling and structural quality assessment Cyril Malbranke, William Rostain, Florence Depardieu, Simona Cocco, Remi Monasson, David Bikard bioRxiv 2023.03.20.533501 • code • Supplementary
Protein Discovery with Discrete Walk-Jump Sampling Nathan C. Frey, Daniel Berenberg, Karina Zadorozhny, Joseph Kleinhenz, Julien Lafrance-Vanasse, Isidro Hotzel, Yan Wu, Stephen Ra, Richard Bonneau, Kyunghyun Cho, Andreas Loukas, Vladimir Gligorijevic, Saeed Saremi arXiv:2306.12360/ICLR 2024 • code • lecture
denoising-diffusion-protein-sequence Zhangzhi Peng Paper unavailable • github
Protein Design with Guided Discrete Diffusion Nate Gruver, Samuel Stanton, Nathan C. Frey, Tim G. J. Rudner, Isidro Hotzel, Julien Lafrance-Vanasse, Arvind Rajpal, Kyunghyun Cho, Andrew Gordon Wilson arXiv:2305.20009/Advances in neural information processing systems, 2024 • code • lecture
PRO-LDM: Protein Sequence Generation with Conditional Latent Diffusion Models Zixuan Jiang, Sitao Zhang, Rundong Huang, Shaoxun Mo, Letao Zhu, Peiheng Li, Ziyi Zhang, Xi Chen, Yunfei Long, Renjing Xu, Rui Qing bioRxiv 2023.08.22.554145 • Supplementary
Protein generation with evolutionary diffusion: sequence is all you need Sarah Alamdari, Nitya Thakkar, Rianne van den Berg, Alex Xijie Lu, Nicolo Fusi, Ava Pardis Amini, Kevin K Yang bioRxiv 2023.09.11.556673 • code • data • lecture, lecture2
AntiBARTy Diffusion for Property Guided Antibody Design Jordan Venderley arXiv:2309.13129
PD-1 Targeted Antibody Discovery Using AI Protein Diffusion Colby T. Ford bioRxiv 2024.01.18.576323 • code
ProT-Diff: A Modularized and Efficient Approach to De Novo Generation of Antimicrobial Peptide Sequences through Integration of Protein Language Model and Diffusion Model Xue-Fei Wang, Jing-Ya Tang, Han Liang, Jing Sun, Sonam Dorje, Bo Peng, Xu-Wo Ji, Zhe Li, Xian-En Zhang, Dian-Bing Wang bioRxiv 2024.02.22.581480 • Supplementary
TaxDiff: Taxonomic-Guided Diffusion Model for Protein Sequence Generation Lin Zongying, Li Hao, Lv Liuzhenghao, Lin Bin, Zhang Junwu, Chen Calvin Yu-Chian, Yuan Li, Tian Yonghong arXiv:2402.17156 • code
Diffusion on language model embeddings for protein sequence generation Viacheslav Meshchaninov, Pavel Strashnov, Andrey Shevtsov, Fedor Nikolaev, Nikita Ivanisenko, Olga Kardymon, Dmitry Vetrov arXiv:2403.03726
AMP-Diffusion: Integrating Latent Diffusion with Protein Language Models for Antimicrobial Peptide Generation Tianlai Chen, Pranay Vure, Rishab Pulugurta, Pranam Chatterjee bioRxiv 2024.03.03.583201
Atomically accurate de novo design of single-domain antibodies Nathaniel R. Bennett, Joseph L. Watson, Robert J. Ragotte, Andrew J. Borst, DeJenae L. See, Connor Weidle, Riti Biswas, Ellen L. Shrock, Philip J. Y. Leung, Buwei Huang, Inna Goreshnik, Russell Ault, Kenneth D. Carr, Benedikt Singer, Cameron Criswell, Dionne Vafeados, Mariana Garcia Sanchez, Ho Min Kim, Susana Vazquez Torres, Sidney Chan, David Baker bioRxiv 2024.03.14.585103 • Supplementary
Complex-based Ligand-Binding Proteins Redesign by Equivariant Diffusion-based Generative Models Viet Thanh Duy Nguyen, Nhan Nguyen, Truong Son Hy bioRxiv 2024.04.17.589997 • code
Cytochrome P450 Enzyme Design by Constraining Catalytic Pocket in Diffusion model Qian Wang, Xiaonan Liu, Hejian Zhang, Huanyu Chu, Chao Shi, Lei Zhang, Jie Bai, Pi Liu, Jing Li, Xiaoxi Zhu, Yuwan Liu, Zhangxin Chen, Rong Huang, Hong Chang, Tian Liu, Zhenzhan Chang , Jian Cheng , and Huifeng Jiang Research (2024) • code
Context-Guided Diffusion for Out-of-Distribution Molecular and Protein Design Leo Klarner, Tim G. J. Rudner, Garrett M. Morris, Charlotte M. Deane, Yee Whye Teh arXiv:2407.11942 • code
Secondary Structure-Guided Novel Protein Sequence Generation with Latent Graph Diffusion Yutong Hu, Yang Tan, Andi Han, Lirong Zheng, Liang Hong, Bingxin Zhou arXiv:2407.07443 • code
AI-generated small binder improves prime editing Ju-Chan Park, Heesoo Uhm, Yong-Woo Kim, Ye Eun Oh, Sangsu Bae bioRxiv 2024.09.11.612443 • Supplementary
MeMDLM: De Novo Membrane Protein Design with Masked Discrete Diffusion Protein Language Models Shrey Goel, Vishrut Thoutam, Edgar Mariano Marroquin, Aaron Gokaslan, Arash Firouzbakht, Sophia Vincoff, Volodymyr Kuleshov, Huong T. Kratochvil, Pranam Chatterjee arXiv:2410.16735
Retrieval Augmented Diffusion Model for Structure-informed Antibody Design and Optimization Zichen Wang, Yaokun Ji, Jianing Tian, Shuangjia Zheng arXiv:2410.15040
ProtDiff: Function-Conditioned Masked Diffusion Models for Robust Directed Protein Generation
Vishrut Thoutam, Yair Schiff, Sergey Ovchinnikov, Pranam Chatterjee
Neurips 2024 Workshop Foundation Models for Science: Progress, Opportunities, and Challenges
Generative Pretrained Autoregressive Transformer Graph Neural Network applied to the Analysis and Discovery of Novel Proteins Markus J. Buehler arXiv:2305.04934 • code
Microdroplet screening rapidly profiles a biocatalyst to enable its AI-assisted engineering Maximilian Gantz, Simon V. Mathis, Friederike E. H. Nintzel, Paul J. Zurek, Tanja Knaus, Elie Patel, Daniel Boros, Friedrich-Maximilian Weberling, Matthew R. A. Kenneth, Oskar J. Klein, Elliot J. Medcalf, Jacob Moss, Michael Herger, Tomasz S. Kaminski, Francesco G. Mutti, Pietro Lio, Florian Hollfelder bioRxiv (2024.04.08)
Bootstrapped Training of Score-Conditioned Generator for Offline Design of Biological Sequences Minsu Kim, Federico Berto, Sungsoo Ahn, Jinkyoo Park arXiv:2306.03111 • code
These models generate protein structures(including side chains) from expected function or recover a part of protein structures(aka. inpainting)
Towards deep learning sequence-structure co-generation for protein design Chentong Wang, Sarah Alamdari, Carles Domingo-Enrich, Ava Amini, Kevin K. Yang arXiv:2410.01773
One-sided design of protein-protein interaction motifs using deep learning Syrlybaeva, Raulia, and Eva-Maria Strauch. bioRxiv (2022) • code • our notes • lecture
Protein Structure and Sequence Generation with Equivariant Denoising Diffusion Probabilistic Models Namrata Anand, Tudor Achim GitHub (2022)/arXiv (2022) • our notes • lecture
Antigen-Specific Antibody Design and Optimization with Diffusion-Based Generative Models for Protein Structures Shitong Luo, Yufeng Su, Xingang Peng, Sheng Wang, Jian Peng, Jianzhu Ma bioRxiv 2022.07.10.499510/ICML (2023) • code • hugging face
Illuminating protein space with a programmable generative model John Ingraham, Max Baranov, Zak Costello, Vincent Frappier, Ahmed Ismail, Shan Tie, Wujie Wang, Vincent Xue, Fritz Obermeyer, Andrew Beam, Gevorg Grigoryan Generate Biomedicines Preprint/bioRxiv 2022.12.01.518682/Nature (2023) • website • news • code • colab • commercial
Physics-Inspired Protein Encoder Pre-Training via Siamese Sequence-Structure Diffusion Trajectory Prediction Zuobai Zhang, Minghao Xu, Aurélie Lozano, Vijil Chenthamarakshan, Payel Das, Jian Tang arXiv:2301.12068 • code
TRDiffusion TIANRANG XLab news • website • commercial
An all-atom protein generative model Alexander E Chu, Lucy Cheng, Gina El Nesr, Minkai Xu, Po-Ssu Huang bioRxiv 2023.05.24.542194/Proceedings of the National Academy of Sciences 121.27 (2024) • code
DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packing Yangtian Zhan, Zuobai Zhang, Bozitao Zhong, Sanchit Misra, Jian Tang arxiv 2023.06.01 • code
AbDiffuser: Full-Atom Generation of In-Vitro Functioning Antibodies Karolis Martinkus, Jan Ludwiczak, Kyunghyun Cho, Wei-Ching Lian, Julien Lafrance-Vanasse, Isidro Hotzel, Arvind Rajpal, Yan Wu, Richard Bonneau, Vladimir Gligorijevic, Andreas Loukas arXiv:2308.05027 • lecture
Generative Diffusion Models for Antibody Design, Docking, and Optimization Zhangzhi Peng, Chenchen Han, Xiaohan Wang, Dapeng Li, Fajiie Yuan bioRxiv 2023.09.25.559190 • code • website
Bridging Sequence and Structure: Latent Diffusion for Conditional Protein Generation Anonymous ICLR 2024
Guiding diffusion models for antibody sequence and structure co-design with developability properties Amelia Villegas-Morcillo, Jana M. Weber, Marcel J.T. Reinders bioRxiv 2023.11.22.568230/NeurIPS 2023 Generative AI and Biology Workshop • code
A Multi-Modal Contrastive Diffusion Model for Therapeutic Peptide Generation Yongkang Wang, Xuan Liu, Feng Huang, Zhankun Xiong, Wen Zhang arXiv:2312.15665 • code
Towards Joint Sequence-Structure Generation of Nucleic Acid and Protein Complexes with SE(3)-Discrete Diffusion Alex Morehead, Jeffrey Ruffolo, Aadyot Bhatnagar, Ali Madani arXiv:2401.06151 • code
Proteus: exploring protein structure generation for enhanced designability and efficiency Chentong Wang, Yannan Qu, Zhangzhi Peng, Yukai Wang, Hongli Zhu, Dachuan Chen, Longxing Cao bioRxiv 2024.02.10.579791 • code
Full-Atom Peptide Design with Geometric Latent Diffusion Xiangzhe Kong, Wenbing Huang, Yang Liu arXiv:2402.13555
A Hybrid Diffusion Model for Stable, Affinity-Driven, Receptor-Aware Peptide Generation R Vishva Saravanan, Soham Choudhuri, Bhaswar Ghosh bioRxiv 2024.03.14.584934 • code • dataset
Antigen-Specific Antibody Design via Direct Energy-based Preference Optimization Xiangxin Zhou, Dongyu Xue, Ruizhe Chen, Zaixiang Zheng, Liang Wang, Quanquan Gu arXiv:2403.16576
HelixDiff, a Score-Based Diffusion Model for Generating All-Atom α-Helical Structures Xuezhi Xie, Pedro A Valiente, Jisun Kim, and Philip M Kim ACS Central Science (2024) • code
Combining transformer and 3DCNN models to achieve co-design of structures and sequences of antibodies in a diffusional manner Yue Hu, Feng Tao, Jun Wen Lan, Jing Zhang bioRxiv 2024.04.25.587828 • code
Target-Specific De Novo Peptide Binder Design with DiffPepBuilder Fanhao Wang, Yuzhe Wang, Laiyi Feng, Changsheng Zhang, Luhua Lai arXiv:2405.00128/J. Chem. Inf. Model. 2024 • code
Improving Antibody Design with Force-Guided Sampling in Diffusion Models Paulina Kulytė, Francisco Vargas, Simon Valentin Mathis, Yu Guang Wang, José Miguel Hernández-Lobato, Pietro Liò arXiv:2406.05832
Antibody Design Using a Score-based Diffusion Model Guided by Evolutionary, Physical and Geometric Constraints Tian Zhu, Milong Ren, Haicang Zhang ICML 2024 • code
Antibody-SGM, a Score-Based Generative Model for Antibody Heavy-Chain Design Xuezhi Xie, Pedro A. Valiente, Jin Sub Lee, Jisun Kim, Philip M. Kim Journal of Chemical Information and Modeling (2024) • code
Hybrid Diffusion Model for Stable, Affinity-Driven, Receptor-Aware Peptide Generation Vishva Saravanan R, Soham Choudhuri, Bhaswar Ghosh J. Chem. Inf. Model. 2024 • code
De novo design of high-affinity protein binders with AlphaProteo Vinicius Zambaldi, David La, Alexander E. Chu, Harshnira Patani, Amy E. Danson, Tristan O. C., Kwan, Thomas Frerix, Rosalia G. Schneider, David Saxton, Ashok Thillaisundaram, Zachary Wu, Isabel Moraes, Oskar Lange, Eliseo Papa, Gabriella Stanton, Victor Martin, Sukhdeep Singh, Lai H. Wong, Russ Bates, Simon A. Kohl, Josh Abramson, Andrew W. Senior, Yilmaz Alguel, Mary Y. Wu, Irene M. Aspalter, Katie Bentley, David L.V. Bauer, Peter Cherepanov, Demis Hassabis, Pushmeet Kohli, Rob Fergus, and Jue Wang DeepMind Preprint/arXiv:2409.08022 • blog
DPLM-2: A Multimodal Diffusion Protein Language Model Xinyou Wang, Zaixiang Zheng, Fei Ye, Dongyu Xue, Shujian Huang, Quanquan Gu arXiv:2410.13782 • code • website
E(3)-invaraint diffusion model for pocket-aware peptide generation Po-Yu Liang, Jun Bai arXiv:2410.21335 • code
Deep learning methods for designing proteins scaffolding functional sites / Scaffolding protein functional sites using deep learning Jue Wang, Sidney Lisanza, David Juergens, Doug Tischer, Ivan Anishchenko, Minkyung Baek, Joseph L. Watson, Jung Ho Chun, Lukas F. Milles, Justas Dauparas, Marc Expòsit, Wei Yang, Amijai Saragovi, Sergey Ovchinnikov, David Baker bioRxiv(2021)/Science(2022) • RFDesign • our notes • lecture • RoseTTAFold • Supplementary, Other Supplementary
Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models / De novo design of protein structure and function with RFdiffusion Joseph L. Watson, David Juergens, Nathaniel R. Bennett, Brian L. Trippe, Jason Yim, Helen E. Eisenach, Woody Ahern, Andrew J. Borst, Robert J. Ragotte, Lukas F. Milles, Basile I. M. Wicky, Nikita Hanikel, Samuel J. Pellock, Alexis Courbet, William Sheffler, Jue Wang, Preetham Venkatesh, Isaac Sappington, Susana Vázquez Torres, Anna Lauko, Valentin De Bortoli, Emile Mathieu, Regina Barzilay, Tommi S. Jaakkola, Frank DiMaio, Minkyung Baek, David Baker Bakerlab Preprint/bioRxiv 2022.12.09.519842/Nature (2023) • news, news2, news3 • Supplementary • lecture, lecture2 • RFdiffusion:code, Colab • blog
De novo design of high-affinity protein binders to bioactive helical peptides Susana Vázquez Torres, Philip J. Y. Leung, Isaac D. Lutz, Preetham Venkatesh, Joseph L Watson, Fabian Hink, Huu-Hien Huynh, Andy Hsien-Wei Yeh, David Juergens, Nathaniel R. Bennett, Andrew N. Hoofnagle, Eric Huang, Michael J. MacCoss, Marc Expòsit, Gyu Rie Lee, Elif Nihal Korkmaz, Jeff Nivala, Lance Stewart, Joseph M. Rodgers, David Baker bioRxiv 2022.12.10.519862/Nature (2023) • Supplementary
Joint Generation of Protein Sequence and Structure with RoseTTAFold Sequence Space Diffusion Sidney Lyayuga Lisanza, Jacob Merle Gershon, Sam Wayne Kenmore Tipps, Lucas Arnoldt, Samuel Hendel, Jeremiah Nelson Sims, Xinting Li, David Baker bioRxiv 2023.05.08.539766/Nat Biotechnol (2024) • code • hugging face • lecture
The structural landscape of the immunoglobulin fold by large-scale de novo design Jorge Roel-Touris, Lourdes Carcelen, Enrique Marcos bioRxiv 2023.10.03.560637/Protein Science (2024) • Supplementary • code • data
Generalized Biomolecular Modeling and Design with RoseTTAFold All-Atom Rohith Krishna, Jue Wang, Woody Ahern, Pascal Sturmfels, Preetham Venkatesh, Indrek Kalvet, Gyu Rie Lee, Felix S Morey-Burrows, Ivan Anishchenko, Ian R Humphreys, Ryan McHugh, Dionne Vafeados, Xinting Li, George A Sutherland, Andrew Hitchcock, C Neil Hunter, Minkyung Baek, Frank DiMaio, David Baker bioRxiv 2023.10.09.561603/Science • Supplementary • code
Amalga: Designable Protein Backbone Generation with Folding and Inverse Folding Guidance Shugao Chen, Ziyao Li, Xiangxiang Zeng, Guolin Ke bioRxiv 2023.11.07.565939
Accurate single domain scaffolding of three non-overlapping protein epitopes using deep learning Karla M Castro, Joseph L Watson, Jue Wang, Joshua Southern, Reyhaneh Ayardulabi, Sandrine Georgeon, Stephane Rosset, David Baker, Bruno E Correia bioRxiv 2024.05.07.592871 • Supplementary
Diversifying de novo TIM barrels by hallucination Beck, Julian, Sooruban Shanmugaratnam, and Birte Höcker. Protein Science 33.6 (2024)
De novo designed proteins neutralize lethal snake venom toxins Susana Vázquez Torres, Melisa Benard Valle, Stephen P. Mackessy, Stefanie K. Menzies, Nicholas R. Casewell, Shirin Ahmadi, Nick J. Burlet, Edin Muratspahić, Isaac Sappington, Max D.Overath, Esperanza Rivera-de-Torre, Jann Ledergerber, Andreas H. Laustsen, Kim Boddum, Asim K.Bera, Alex Kang,Evans Brackenbrough, Iara A. Cardoso, Edouard P. Crittenden, Rebecca J.Edge, Justin Decarreau, Robert J. Ragotte, Arvind S. Pillai, Mohamad Abedi, Hannah L. Han,Stacey R. Gerben, Analisa Murray, Rebecca Skotheim, Lynda Stuart, Lance Stewart, Thomas J.A. Fryer, Timothy P. Jenkins, David Baker PREPRINT (Version 1) available at Research Square
Controlling semiconductor growth with structured de novo protein interfaces Amijai Saragovi, Harley Pyles, Paul Kwon, Nikita Hanikel, Fátima A. Dávila-Hernández, Asim K. Bera, Alex Kang, Evans Brackenbrough, Dionne K. Vafeados, Aza Allen, Lance Stewart, David Baker bioRxiv 2024.06.24.600095 • Supplementary
Diffusing protein binders to intrinsically disordered proteins Caixuan Liu, Kejia Wu, Hojun Choi, Hannah Han, Xueli Zhang, Joseph L Watson, Sara Shijo, Asim K Bera, Alex Kang, Evans Brackenbrough, Brian Coventry, Derrick R Hick, Andrew N Hoofnagle, Ping Zhu, Xingting Li, Justin Decarreau, Stacey R Gerben, Wei Yang, Xinru Wang, Mila Lamp, Analisa Murray, Magnus Bauer, David Baker bioRxiv 2024.07.16.603789 • Supplementary
Parametrically guided design of beta barrels and transmembrane nanopores using deep learning David E. Kim, Joseph L. Watson, David Juergens, Sagardip Majumder, Stacey R. Gerben, Alex Kang, Asim K. Bera, Xinting Li, David Baker bioRxiv 2024.07.22.604663 • Supplementary • code1, code2
Computational design of highly active de novo enzymes Markus Braun, Adrian Tripp, Morakot Chakatok, Sigrid Kaltenbrunner, Massimo G. Totaro, David Stoll, Aleksandar Bijelic, Wael Elaily, Shlomo Yakir Yakir Hoch, Matteo Aleotti, Melanie Hall, Gustav Oberdorfer bioRxiv 2024.08.02.606416 • Supplementary
Computational design of serine hydrolases Anna Lauko, Samuel J Pellock, Ivan Anischanka, Kiera H Sumida, David Juergens, Woody Ahern, Alex Shida, Andrew Hunt, Indrek Kalvet, Christoffer Norn, Ian R Humphreys, Cooper S Jamieson, Alex Kang, Evans Brackenbrough, Banumathi Sankaran, K N Houk, David Baker bioRxiv 2024.08.29.610411
De novo design of Ras isoform selective binders Jason Zhaoxing Zhang, Xinting Li, Caixuan Liu, Hanlun Jiang, Kejia Wu, David Baker bioRxiv 2024.08.29.610300
Improved protein binder design using beta-pairing targeted RFdiffusion Isaac Sappington, Martin Toul, David S. Lee, Stephanie A. Robinson, Inna Goreshnik, Clara McCurdy, Tung Ching Chan, Nic Buchholz, Buwei Huang, Dionne Vafeados, Mariana Garcia-Sanchez, Nicole Roullier, Matthias Glögl, Chris Kim, Joseph L. Watson, Susana Vázquez Torres, Koen H. G. Verschueren, Kenneth Verstraete, Cynthia S. Hinck, Melisa Benard-Valle, Brian Coventry, Jeremiah Nelson Sims, Green Ahn, Xinru Wang, Andrew P. Hinck, Timothy P. Jenkins, Hannele Ruohola-Baker, Steven M. Banik, Savvas N. Savvides, David Baker bioRxiv 2024.10.11.617496 • Supplementary
Afpdb–an efficient structure manipulation package for AI protein design Yingyao Zhou, Jiayi Cox, Bin Zhou, Steven Zhu, Yang Zhong, Glen Spraggon Bioinformatics (2024): btae654 • code • website
GRACE: Generative Redesign in Artificial Computational Enzymology
Ruei-En, HuChi-Hua, Yu I-Son Ng
ACS Synthetic Biology (2024) • code
Computational Design of Metallohydrolases
Donghyo Kim, Seth M. Woodbury, Woody Ahern, Indrek Kalvet, Nikita Hanikel, Saman Salike, Samuel J. Pellock, Anna Lauko, Donald Hilvert, David Baker
bioRxiv 2024.11.13.623507 • Supplementary
Design of facilitated dissociation enables control over cytokine signaling duration
Adam J. Broerman, Christoph Pollmann, Mauriz A. Lichtenstein, Mark D. Jackson, Maxx H. Tessmer, Won Hee Ryu, Mohamad H. Abedi, Danny D. Sahtoe, Aza Allen, Alex Kang, Joshmyn De La Cruz, Evans Brackenbrough, Banumathi Sankaran, Asim K. Bera, Daniel M. Zuckerman, Stefan Stoll, Florian Praetorius, Jacob Piehler, David Baker
bioRxiv 2024.11.15.623900 • Supplementary
Accurate de novo design of high-affinity protein binding macrocycles using deep learning
Stephen A. Rettie, David Juergens, Victor Adebomi, Yensi Flores Bueso, Qinqin Zhao, Alexandria N. Leveille, Andi Liu, Asim K. Bera, Joana A. Wilms, Alina Üffing, Alex Kang, Evans Brackenbrough, Mila Lamb, Stacey R. Gerben, Analisa Murray, Paul M. Levine, Maika Schneider, Vibha Vasireddy, Sergey Ovchinnikov, Oliver H. Weiergräber, Dieter Willbold, Joshua A. Kritzer, Joseph D. Mougous, David Baker, Frank DiMaio, Gaurav Bhardwaj
bioRxiv 2024.11.18.622547 • Supplementary
De Novo Design of Site-specific Protein Binders Using Surface Fingerprints Pablo Gainza, Sarah Wehrle, Alexandra Van Hall-Beauvais, Anthony Marchand, Andreas Scheck, Zander Harteveld, Stephen Buckley, Dongchun Ni, Shuguang Tan, Freyr Sverrisson, Casper Goverde, Priscilla Turelli, Charlène Raclot, Alexandra Teslenko, Martin Pacesa, Stéphane Rosset, Sandrine Georgeon, Jane Marsden, Aaron Petruzzella, Kefang Liu, Zepeng Xu, Yan Chai, Pu Han, George F. Gao, Elisa Oricchio, Beat Fierz, Didier Trono, Henning Stahlberg, Michael Bronstein, Bruno E. Correia Protein Science 30.CONF (2021)/bioRxiv (2022)/Nature (2023) • Supplementary • masif_seed • masif • lecture
Targeting protein-ligand neosurfaces using a generalizable deep learning approach Anthony Marchand, Stephen Buckley, Arne Schneuing, Martin Pacesa, Pablo Gainza, Evgenia Elizarova, Rebecca Manuela Neeser, Pao-Wan Lee, Luc Reymond, Maddalena Elia, Leo Scheller, Sandrine Georgeon, Joseph Schmidt, Philippe Schwaller, Sebastian Josef Maerkl, Michael Bronstein, Bruno Emmanuel Correia bioRxiv 2024.03.25.585721 • Supplementary • code
Target-conditioned diffusion generates potent TNFR superfamily antagonists and agonists Matthias Glögl, Aditya Krishnakumar, Robert J. Ragotte, Inna Goreshnik, Brian Coventry, Asim K. Bera, Alex Kang, Emily Joyce, Green Ahn, Buwei Huang, Wei Yang, Wei Chen, Mariana Garcia Sanchez, Brian Koepnick, David Baker bioRxiv 2024.09.13.612773
Iterative refinement graph neural network for antibody sequence-structure co-design Wengong Jin, Jeremy Wohlwend, Regina Barzilay, Tommi Jaakkola arXiv preprint arXiv:2110.04624 (2021) • RefineGNN • lecture1, lecture2
Antibody Complementarity Determining Regions (CDRs) design using Constrained Energy Model Fu, Tianfan, and Jimeng Sun. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2022 • code
Conditional Antibody Design as 3D Equivariant Graph Translation Xiangzhe Kong, Wenbing Huang, Yang Liu ICLR 2023/arXiv:2208.06073
End-to-End Full-Atom Antibody Design Xiangzhe Kong, Wenbing Huang, Yang Liu arXiv:2302.00203 • code
AbODE: Ab Initio Antibody Design using Conjoined ODEs Yogesh Verma, Markus Heinonen, Vikas Garg arXiv:2306.01005
Joint Design of Protein Sequence and Structure based on Motifs Zhenqiao Song, Yunlong Zhao, Yufei Song, Wenxian Shi, Yang Yang, Lei Li arXiv:2310.02546
De novo protein design using geometric vector field networks Weian Mao, Muzhi Zhu, Zheng Sun, Shuaike Shen, Lin Yuanbo Wu, Hao Chen, Chunhua Shen arXiv:2310.11802/ICLR 2024
A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications Jiaqi Han, Jiacheng Cen, Liming Wu, Zongzhao Li, Xiangzhe Kong, Rui Jiao, Ziyang Yu, Tingyang Xu, Fandi Wu, Zihe Wang, Hongteng Xu, Zhewei Wei, Yang Liu, Yu Rong, Wenbing Huang arXiv:2403.00485 • review
GeoAB: Towards Realistic Antibody Design and Reliable Affinity Maturation Haitao LIN, Lirong Wu, Huang Yufei, Yunfan Liu, Odin Zhang, Yuanqing Zhou, Rui Sun, Stan Z Li bioRxiv 2024.05.15.594274/ICML 2024 • code
Topological Neural Networks go Persistent, Equivariant, and Continuous Yogesh Verma, Amauri H Souza, Vikas Garg arXiv:2406.03164 • code
Protein Sequence and Structure Co-Design with Equivariant Translation Chence Shi, Chuanrui Wang, Jiarui Lu, Bozitao Zhong, Jian Tang arXiv:2210.08761/ICLR 2023 • Supplementary • code
Deep Learning for Flexible and Site-Specific Protein Docking and Design Matt McPartlon, Jinbo Xu bioRxiv 2023.04.01.535079 • code
Full-Atom Protein Pocket Design via Iterative Refinement Zaixi Zhang, Zepu Lu, Zhongkai Hao, Marinka Zitnik, Qi Liu arXiv:2310.02553 • code
Functional Geometry Guided Protein Sequence and Backbone Structure Co-Design Anonymous ICLR 2024
Fast and accurate modeling and design of antibody-antigen complex using tFold Fandi Wu, Yu Zhao, Jiaxiang Wu, Biaobin Jiang, Bing He, Longkai Huang, Chenchen Qin, Fan Yang, Ningqiao Huang, Yang Xiao, Rubo Wang, Huaxian Jia, Yu Rong, Yuyi Liu, Houtim Lai, Tingyang Xu, Wei Liu, Peilin Zhao, Jianhua Yao bioRxiv 2024.02.05.578892 • website
PocketGen: Generating Full-Atom Ligand-Binding Protein Pockets Zhang Zaixi, Wanxiang Shen, Qi Liu, Marinka Zitnik bioRxiv 2024.02.25.581968 • code • website
Simulating 500 million years of evolution with a language model Thomas Hayes, Roshan Rao, Halil Akin, Nicholas James Sofroniew, Deniz Oktay, Zeming Lin, Robert Verkuil, Vincent Quy Tran, Jonathan Deaton, Marius Wiggert, Rohil Badkundri, Irhum Shafkat, Jun Gong, Alexander Derry, Raul Santiago Molina, Neil Thomas, Yousuf Khan, Chetan Mishra, Carolyn Kim, Liam J. Bartie, Patrick D. Hsu, Tom Sercu, Salvatore Candido, Alexander Rives preprint/bioRxiv 2024.07.01.600583 • website • code • colab • news
Protein Complex Invariant Embedding with Cross-Gate MLP is A One-Shot Antibody Designer Cheng Tan, Zhangyang Gao, Stan Z. Li arXiv:2305.09480
Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design Andrew Campbell, Jason Yim, Regina Barzilay, Tom Rainforth, Tommi Jaakkola arXiv:2402.04997 • code • lecture
PPFlow: Target-Aware Peptide Design with Torsional Flow Matching Haitao Lin, Odin Zhang, Huifeng Zhao, Dejun Jiang, Lirong Wu, Zicheng Liu, Yufei Huang, Stan Z. Li bioRxiv 2024.03.07.583831/arXiv:2405.06642 • Supplementary • code
Full-Atom Peptide Design based on Multi-modal Flow Matching Jiahan Li, Chaoran Cheng, Zuofan Wu, Ruihan Guo, Shitong Luo, Zhizhou Ren, Jian Peng, Jianzhu Ma arXiv:2406.00735 • code
AntibodyFlow: Normalizing Flow Model for Designing Antibody Complementarity-Determining Regions Bohao Xu, Yanbo Wang, Wenyu Chen, Shimin Shan arXiv:2406.13162
Generalized Protein Pocket Generation with Prior-Informed Flow Matching Zaixi Zhang, Marinka Zitnik, Qi Liu arXiv:2409.19520
D-Flow: Multi-modality Flow Matching for D-peptide Design
Fang Wu, Tinson Xu, Shuting Jin, Xiangru Tang, Zerui Xu, James Zou, Brian Hie
arXiv:2411.10618 • code
CarbonNovo: Joint Design of Protein Structure and Sequence Using a Unified Energy-based Model Ren, Milong, Tian Zhu, and Haicang Zhang ICML 2024 • code
P(all-atom) Is Unlocking New Path For Protein Design Wei Qu, Jiawei Guan, Rui Ma, Ke Zhai, Weikun Wu, Haobo Wang bioRxiv 2024.08.16.608235 • code • news
EnzymeFlow: Generating Reaction-specific Enzyme Catalytic Pockets through Flow-Matching and Co-Evolutionary Dynamics Chenqing Hua paper not available • code
IgGM: A Generative Model for Functional Antibody and Nanobody Design Rubo Wang, Fandi Wu, Xingyu Gao, Jiaxiang Wu, Peilin Zhao, Jianhua Yao bioRxiv 2024.09.19.613838 • code
Deep generative models of genetic variation capture the effects of mutations Adam J. Riesselman, John B. Ingraham & Debora S. Marks Nature Methods • code::DeepSequence • Oct 2018
Deciphering protein evolution and fitness landscapes with latent space models Xinqiang Ding, Zhengting Zou & Charles L. Brooks III Nature Communications • code::PEVAE • Dec 2019
Is transfer learning necessary for protein landscape prediction? Shanehsazzadeh, Amir, David Belanger, and David Dohan. arXiv preprint arXiv:2011.03443 (2020)
Epistatic Net allows the sparse spectral regularization of deep neural networks for inferring fitness functions Amirali Aghazadeh, Hunter Nisonoff, Orhan Ocal, David H. Brookes, Yijie Huang, O. Ozan Koyluoglu, Jennifer Listgarten & Kannan Ramchandran Nature Communications • code • Sep 2021
The generative capacity of probabilistic protein sequence models Francisco McGee, Sandro Hauri, Quentin Novinger, Slobodan Vucetic, Ronald M. Levy, Vincenzo Carnevale & Allan Haldane Nature Communications • code::generation_capacity_metrics • code::sVAE • Nov 2021
Learning the local landscape of protein structures with convolutional neural networks Anastasiya V. Kulikova, Daniel J. Diaz, James M. Loy, Andrew D. Ellington & Claus O. Wilke Journal of Biological Physics 47.4 (2021)
Neural networks to learn protein sequence-function relationships from deep mutational scanning data Sam Gelman, Sarah A. Fahlberg, Pete Heinzelman, Philip A. Romero & Anthony Gitter Proceedings of the National Academy of Sciences • code • Nov 2021
Learning Protein Fitness Models from Evolutionary and Assay-labeled Data Chloe Hsu, Hunter Nisonoff, Clara Fannjiang, Jennifer Listgarten Nature Biotechnology (2022) • Supplementary Information • code
Proximal Exploration for Model-guided Protein Sequence Design Zhizhou Ren, Jiahan Li, Fan Ding, Yuan Zhou, Jianzhu Ma, Jian Peng BioRxiv (2022) • code • commercial
Efficient evolution of human antibodies from general protein language models and sequence information alone Brian L. Hie, Duo Xu, Varun R. Shanker, Theodora U.J. Bruun, Payton A. Weidenbacher, Shaogeng Tang, Peter S. Kim bioRxiv (2022) • code
Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A., Marks, D.S., Gal, Y. ICML (2022)/arXiv:2205.13760 • code • hugging face
Protein engineering via Bayesian optimization-guided evolutionary algorithm and robotic experiments Ruyun Hu, Lihao Fu, Yongcan Chen, Junyu Chen, Yu Qiao, Tong Si bioRxiv 2022.08.11.503535
Antibody optimization enabled by artificial intelligence predictions of binding affinity and naturalness Sharrol Bachas, Goran Rakocevic, David Spencer, Anand V. Sastry, Robel Haile, John M. Sutton, George Kasun, Andrew Stachyra, Jahir M. Gutierrez, Edriss Yassine, Borka Medjo, Vincent Blay, Christa Kohnert, Jennifer T. Stanton, Alexander Brown, Nebojsa Tijanic, Cailen McCloskey, Rebecca Viazzo, Rebecca Consbruck, Hayley Carter, Simon Levine, Shaheed Abdulhaqq, Jacob Shaul, Abigail B. Ventura, Randal S. Olson, Engin Yapici, Joshua Meier, Sean McClain, Matthew Weinstock, Gregory Hannum, Ariel Schwartz, Miles Gander, Roberto Spreafico bioRxiv 2022.08.16.504181 • poster
Construction of a Deep Neural Network Energy Function for Protein Physics Yang, Huan, Zhaoping Xiong, and Francesco Zonta Journal of Chemical Theory and Computation (2022)
Inferring protein fitness landscapes from laboratory evolution experiments Sameer D’Costa, Emily C. Hinds, Chase R. Freschlin, Hyebin Song, Philip A. Romero bioRxiv 2022.09.01.506224 • Supplementary
BayeStab: Predicting Effects of Mutations on Protein Stability with Uncertainty Quantification Shuyu Wang, Hongzhou Tang, Yuliang Zhao, Lei Zuo Protein Science (2022) • code • website
Tuned Fitness Landscapes for Benchmarking Model-Guided Protein Design Neil Thomas, Atish Agarwala, David Belanger, Yun S. Song, Lucy Colwell bioRxiv 2022.10.28.514293 • code
Protein design using structure-based residue preferences David Ding, Ada Y Shaw, Sam Sinai, Nathan J Rollins, Noam Prywes, David Savage, Michael T Laub, Debora S Marks bioRxiv 2022.10.31.514613 • code
Accurate Mutation Effect Prediction using RoseTTAFold Sanaa Mansoor, Minkyung Baek, David Juergens, Joseph L Watson, David Baker bioRxiv 2022.11.04.515218
Learning the shape of protein micro-environments with a holographic convolutional neural network Michael N. Pun, Andrew Ivanov, Quinn Bellamy, Zachary Montague, Colin LaMont, Philip Bradley, Jakub Otwinowski, Armita Nourmohammad bioRxiv (2022) • code
Infer global, predict local: quantity-quality trade-off in protein fitness predictions from sequence data Lorenzo Posani, Francesca Rizzato, Rémi Monasson, Simona Cocco bioRxiv 2022.12.12.520004
Validation of de novo designed water-soluble and transmembrane proteins by in silico folding and melting Alvaro Martin, Carolin Berner, Sergey Ovchinnikov, Anastassia Andreevna Vorobieva bioRxiv 2023.06.06.543955 • colab
PoET: A generative model of protein families as sequences-of-sequences Timothy F. Truong Jr, Tristan Bepler arXiv:2306.06156 • code
Rapid protein stability prediction using deep learning representations Lasse M BlaabjergMaher M KassemLydia L GoodNicolas JonssonMatteo CagiadaKristoffer E JohanssonWouter BoomsmaAmelie SteinKresten Lindorff-Larsen eLife 12:e82593 • code
A general Temperature-Guided Language model to engineer enhanced Stability and Activity in Proteins Pan Tan, Mingchen Li, Yuanxi Yu, Fan Jiang, Lirong Zheng, Banghao Wu, Xinyu Sun, Liqi Kang, Jie Song, Liang Zhang, Yi Xiong, Wanli Ouyang, Zhiqiang Hu, Guisheng Fan, Yufeng Pei, Liang Hong arXiv:2307.12682
Transfer learning to leverage larger datasets for improved prediction of protein stability changes Henry Dieckhaus, Michael Brocidiacono, Nicholas Randolph, Brian Kuhlman bioRxiv 2023.07.27.550881 • code • Supplementary
Structure-based self-supervised learning enables ultrafast prediction of stability changes upon mutation at the protein universe scale Jinyuan Sun, Tong Zhu, Yinglu Cui, Bian Wu bioRxiv 2023.08.09.552725 • code
Boosting AND/OR-Based Computational Protein Design: Dynamic Heuristics and Generalizable UFO Bobak Pezeshki, Radu Marinescu, Alexander Ihler, Rina Dechter arXiv:2309.00408
Zero-shot Mutation Effect Prediction on Protein Stability and Function using RoseTTAFold Sanaa Mansoor, Minkyung Baek, David Juergens, Joseph L. Watson, David Baker Protein Science • dissertation
Accurate proteome-wide missense variant effect prediction with AlphaMissense Jun Cheng, Guido Novati, Joshua Pan, Clare Bycroft, Akvile Žemgulyte, Taylor Applebaum, Alexander Pritzel, Lai Hong Wong, Michal Zielinski, Tobias Sargeant, Rosalia G. Schneider, Andrew W. Senior, John Jumper, Demis Hassabis, Pushmeet Kohli, Žiga Avsec Science0,eadg7492DOI:10.1126/science.adg7492 • code • data
Enzyme structure correlates with variant effect predictability Floris Julian van der Flier, Dave Estell, Sina Pricelius, Lydia Dankmeyer, Sander van Stigt Thans, Harm Mulder, Rei Otsuka, Frits Goedegebuur, Laurens Lammerts, Diego Staphorst, Aalt D.J. van Dijk, Dick de Ridder, Henning Redestig bioRxiv 2023.09.25.559319/Computational and Structural Biotechnology Journal (2024) • code
Fast, accurate ranking of engineered proteins by target binding propensity using structure modeling Xiaozhe Ding, Xinhong Chen, Erin E. Sullivan, Timothy F. Shay, Viviana Gradinaru bioRxiv 2023.01.11.523680/Molecular Therapy (2024) • code • colab
Neural network extrapolation to distant regions of the protein fitness landscape Sarah A Fahlberg, Chase R Freschlin, Pete Heinzelman, Philip A Romero bioRxiv 2023.11.08.566287 • Supplementary
Accelerating protein engineering with fitness landscape modeling and reinforcement learning Haoran Sun, Liang He, Pan Deng, Guoqing Liu, Haiguang Liu, Chuan Cao, Fusong Ju, Lijun Wu, Tao Qin, Tie-Yan Liu bioRxiv 2023.11.16.565910
Protein Design by Directed Evolution Guided by Large Language Models Trong Thanh Tran, Truong Son Hy bioRxiv 2023.11.29.568945 • Supplementary • code
High-throughput ML-guided design of diverse single-domain antibodies against SARS-CoV-2 Christof Angermueller, Zelda Marie, Benjamin Jester, Emily Engelhart, Ryan Emerson, Babak Alipanahi, Zachary Ryan McCaw, Jim Roberts, Randolph M Lopez, David Younger, Lucy Colwell bioRxiv 2023.12.01.569227
Efficiently Predicting Protein Stability Changes Upon Single-point Mutation with Large Language Models Yijie Zhang, Zhangyang Gao, Cheng Tan, Stan Z.Li arXiv preprint arXiv:2312.04019 (2023)
DSMBind: SE(3) denoising score matching for unsupervised binding energy prediction and nanobody design Wengong Jin, Xun Chen, Amrita Vetticaden, Siranush Sarzikova, Raktima Raychowdhury, Caroline Uhler, Nir Hacohen bioRxiv 2023.12.10.570461 • Supplementary1 • Supplementary2
Inverse folding of protein complexes with a structure-informed language model enables unsupervised antibody evolution Varun R. Shanker, Theodora U.J. Bruun, Brian L. Hie, Peter S. Kim bioRxiv 2023.12.19.572475
EvolMPNN: Predicting Mutational Effect on Homologous Proteins by Evolution Encoding Zhiqiang Zhong, Davide Mottin arXiv:2402.13418
Generating mutants of monotone affinity towards stronger protein complexes through adversarial learning Tian Lan, Shuquan Su, Pengyao Ping, Gyorgy Hutvagner, Tao Liu, Yi Pan & Jinyan Li Nat Mach Intell 6, 315–325 (2024) • code
Biophysics-based protein language models for protein engineering Sam Gelman, Bryce Johnson, Chase Freschlin, Sameer D'Costa, Anthony Gitter & Philip A. Romero bioRxiv 2024.03.15.585128 • code
Latent-based Directed Evolution accelerated by Gradient Ascent for Protein Sequence Design Nhat Khang Ngo, Thanh V. T. Tran, Viet Thanh Duy Nguyen, Truong Son Hy bioRxiv 2024.04.13.589381/NeurIPS 2024 • code
AAVDiff: Experimental Validation of Enhanced Viability and Diversity in Recombinant Adeno-Associated Virus (AAV) Capsids through Diffusion Generation Lijun Liu, Jiali Yang, Jianfei Song, Xinglin Yang, Lele Niu, Zeqi Cai, Hui Shi, Tingjun Hou, Chang-yu Hsieh, Weiran Shen, Yafeng Deng arXiv:2404.10573
Protein engineering with lightweight graph denoising neural networks Bingxin Zhou, Lirong Zheng, Banghao Wu, Yang Tan, Outongyi Lv, Kai Yi, Guisheng Fan, and Liang Hong Journal of Chemical Information and Modeling (2024) • code
VespaG: Expert-guided protein Language Models enable accurate and blazingly fast fitness prediction Celine Marquet, Julius Schlensok, Marina Abakarova, Burkhard Rost, Elodie Laine bioRxiv 2024.04.24.590982 • code
Interface design of SARS-CoV-2 symmetrical nsp7 dimer and machine learning-guided nsp7 sequence prediction reveals physicochemical properties and hotspots for nsp7 stability, adaptation, and therapeutic design Amar Jeet Yadav, Shivank Kumar, Shweata Maurya, Khushboo Bhagat, and Aditya K. Padhi Physical Chemistry Chemical Physics (2024)
Aligning protein generative models with experimental fitness via Direct Preference Optimization Talal Widatalla, Rafael Rafailov, Brian Hie bioRxiv 2024.05.20.595026 • code
ProBASS – a language model with sequence and structural features for predicting the effect of mutations on binding affinity Sagara N.S. Gurusinghe, Yibing Wu, William DeGrado, Julia M. Shifman bioRxiv 2024.06.21.600041 • code
Unsupervised evolution of protein and antibody complexes with a structure-informed language model Varun R. Shanker, Theodora U. J. Bruun, Brian L. Hie, Peter S. Kim Science385,46-53(2024) • code
Enhancing efficiency of protein language models with minimal wet-lab data through few-shot learning Ziyi Zhou, Liang Zhang, Yuanxi Yu, Banghao Wu, Mingchen Li, Liang Hong & Pan Tan Nat Commun 15, 5566 (2024) • code
Rapid protein evolution by few-shot learning with a protein language model Kaiyi Jiang, Zhaoqing Yan, Matteo Di Bernardo, Samantha R. Sgrizzi, Lukas Villiger, Alisan Kayabolen, Byungji Kim, Josephine K. Carscadden, Masahiro Hiraizumi, Hiroshi Nishimasu, Jonathan S. Gootenberg, Omar O. Abudayyeh bioRxiv 2024.07.17.604015 • code1,code2
Zero-shot prediction of mutation effects with multimodal deep representation learning guides protein engineering Peng Cheng, Cong Mao, Jin Tang, Sen Yang, Yu Cheng, Wuke Wang, Qiuxi Gu, Wei Han, Hao Chen, Sihan Li, Yaofeng Chen, Jianglin Zhou, Wuju Li, Aimin Pan, Suwen Zhao, Xingxu Huang, Shiqiang Zhu, Jun Zhang, Wenjie Shu & Shengqi Wang Cell Research (2024) • code
Machine learning-guided co-optimization of fitness and diversity facilitates combinatorial library design in enzyme engineering Kerr Ding, Michael Chin, Yunlong Zhao, Wei Huang, Binh Khanh Mai, Huanan Wang, Peng Liu, Yang Yang & Yunan Luo Nature Communications 15.1 (2024) • code, model
Dirichlet latent modelling enables effective learning and sampling of the functional protein design space Evgenii Lobzaev, Giovanni Stracquadanio Nat Commun 15, 9309 (2024) • code
MProt-DPO: Breaking the ExaFLOPS Barrier for Multimodal Protein Design Workflows with Direct Preference Optimization
Gautham Dharuman, Kyle Hippe, Alexander Brace, Sam Foreman, Väinö Hatanpää, Varuni K. Sastry, Huihuo Zheng, Logan Ward, Servesh Muralidharan, Archit Vasan, Bharat Kale, Carla M. Mann, Heng Ma, Yun-Hsuan Cheng, Yuliana Zamora, Shengchao Liu, Chaowei Xiao, Murali Emani, Tom Gibbs, Mahidhar Tatineni, Deepak Canchi, Jerome Mitchell, Koichi Yamada, Maria Garzaran, Michael E. Papka, Ian Foster, Rick Stevens, Anima Anandkumar, Venkatram Vishwanath, Arvind Ramanathan
International Conference for High Performance Computing, Networking, Storage and Analysis SC. IEEE Computer Society, 2024
More detailed protein representation learning list: Lirong Wu's awesome-protein-representation-learning
Unified rational protein engineering with sequence-based deep representation learning Ethan C. Alley, Grigory Khimulya, Surojit Biswas, Mohammed AlQuraishi & George M. Church Nature methods 16.12 (2019)
Protein Structure Representation Learning by Geometric Pretraining Zuobai Zhang, Minghao Xu, Arian Jamasb, Vijil Chenthamarakshan, Aurelie Lozano, Payel Das, Jian Tang arXiv • Jan 2022
Evolutionary velocity with protein language models Brian L. Hie, Kevin K. Yang, and Peter S. Kim bioRxiv
Advancing protein language models with linguistics: a roadmap for improved interpretability Mai Ha Vu, Rahmad Akbar, Philippe A. Robert, Bartlomiej Swiatczak, Victor Greiff, Geir Kjetil Sandve, Dag Trygve Truslew Haug arXiv:2207.00982
Deciphering the language of antibodies using self-supervised learning Jinwoo Leem, Laura S. Mitchell, James H.R. Farmery, Justin Barton, Jacob D. Galson Patterns (2022): 100513 • code
On Pre-training Language Model for Antibody Anonymous(Paper under double-blind review) ICLR 2023 • Supplementary
Antibody Representation Learning for Drug Discovery Lin Li, Esther Gupta, John Spaeth, Leslie Shing, Tristan Bepler, Rajmonda Sulo Caceres arXiv:2210.02881
Learning Complete Protein Representation by Deep Coupling of Sequence and Structure Bozhen Hu, Cheng Tan, Jun Xia, Jiangbin Zheng, Yufei Huang, Lirong Wu, Yue Liu, Yongjie Xu, Stan Z. Li bioRxiv 2023.07.05.547769
Leveraging Ancestral Sequence Reconstruction for Protein Representation Learning D. S. Matthews, M. A. Spence, A. C. Mater, J. Nichols, S. B. Pulsford, M. Sandhu, J. A. Kaczmarski, C. M. Miton, N. Tokuriki, C. J. Jackson bioRxiv 2023.12.20.572683 • code
Protein language models are biased by unequal sequence sampling across the tree of life Frances Ding, Jacob Steinhardt bioRxiv 2024.03.07.584001
InstructPLM: Aligning Protein Language Models to Follow Protein Structure Instructions Jiezhong Qiu, Junde Xu, Jie Hu, Hanqun Cao, Liya Hou, Zijun Gao, Xinyi Zhou, Anni Li, Xiujuan Li, Bin Cui, Fei Yang, Shuang Peng, Ning Sun, Fangyu Wang, Aimin Pan, Jie Tang, Jieping Ye, Junyang Lin, Jin Tang, Xingxu Huang, Pheng Ann Heng, Guangyong Chen bioRxiv 2024.04.17.589642
Unlike function-scaffold-sequence paradigm in protein design, major molecular design models based on paradigm form DL from 3 kinds of level: atom-based, fragment-based, reaction-based, and they can be categorized as Gradient optimization or Optimized sampling(gradient-free). Click here for detail reviewIn consideration of learning more various of generative models for design, these recommended latest models from Molecular Design might be helpful and even be able to be transplanted to protein design. More paper list at :
Differentiable scaffolding tree for molecular optimization Fu, T., Gao, W., Xiao, C., Yasonik, J., Coley, C. W., & Sun, J. arXiv preprint arXiv:2109.10469 • code • Sept 21
Equivariant Energy-Guided SDE for Inverse Molecular Design Fan Bao, Min Zhao, Zhongkai Hao, Peiyao Li, Chongxuan Li, Jun Zhu arXiv:2209.15408
Equivariant Shape-Conditioned Generation of 3D Molecules for Ligand-Based Drug Design Keir Adams, Connor W. Coley arXiv:2210.04893 • code
Structure-based Drug Design with Equivariant Diffusion Models Arne Schneuing, Yuanqi Du, Charles Harris, Arian Jamasb, Ilia Igashov, Weitao Du, Tom Blundell, Pietro Lió, Carla Gomes, Max Welling, Michael Bronstein, Bruno Correia NeurIPS 2022/arXiv:2210.13695 • code
Generating 3D Molecules for Target Protein Binding Meng Liu, Youzhi Luo, Kanji Uchino, Koji Maruhashi, Shuiwang Ji International Conference on Machine Learning 39 (2022) • GraphBP
Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets Peng, Xingang, et al. International Conference on Machine Learning 39 (2022) • code
Reinforced Genetic Algorithm for Structure-based Drug Design Fu, Tianfan, et al. arXiv preprint arXiv:2211.16508 (2022)/ICML22 • code • website
Molecule Generation For Target Protein Binding with Structural Motifs Zhang, Zaixi, et al. International Conference on Learning Representations 11 (2023) • code
3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction Guan, Jiaqi, et al. International Conference on Learning Representations 11 (2023) • code
De novo design of epitope-specific antibodies against soluble and multipass membrane proteins with high specificity, developability,and function
Nabla Bio
preprint • blog • commercial
