BigMHC

BigMHC is a deep learning tool for predicting MHC-I (neo)epitope presentation and immunogenicity.

See the article for more information:

• Nature Machine Intelligence article
• free NMI link

All data used in this research can be freely downloaded here.

Installation

pip install git+https://github.com/karchinlab/bigmhc.git

This will take a few minutes and will install all of the required dependencies.

Environment and Dependencies

Execution is OS agnostic and does not require GPUs.

Training models with large batch sizes (e.g. 32768) requires significant GPU memory (about 94 GB total). Transfer learning requires minimal GPU memory and can be reasonably conducted on a CPU.

All methods were tested on Debian 11 using Linux 5.10.0-19-amd64, AMD EPYC 7443P, and four RTX 3090 GPUs.

Optional Dependencies

• cuda (11.7)
  • Required for GPU usage
• magma (magma-cuda117 version 2.6.1)
  • Recommended for GPU usage

Jupyter Notebook Dependencies

• scipy (1.7.3)
• scikit-learn (1.0.2)
• matplotlib (3.5.3)
• seaborn (0.12.1)
• py3dmol (2.0.0.post2)
• logomaker (0.8)
• openpyxl (3.1.1)

Usage

There are two commandline entrypoints: bigmhc_predict and bigmhc_train.

• bigmhc_predict is used for making predictions using BigMHC EL and BigMHC IM
• bigmhc_train allows you to train or retrain (transfer learning) BigMHC on new data

Both scripts, which can be run from any directory, offer help text.

• bigmhc_predict --help
• bigmhc_train --help

Examples

bigmhc_predict -i=../data/example1.csv -m=el -t=2 -d="cpu"
bigmhc_predict -i=../data/example2.csv -m=el -a=HLA-A*02:02 -p=0 -c=0 -d="cpu"

Predictions will be written to example1.csv.prd and example2.csv.prd in the data folder. Execution takes a few seconds. Compare your output with example1.csv.cmp and example2.csv.cmp respectively.

Supported Alleles

BigMHC only supports MHC-I. In order to handle different MHC naming schemes, BigMHC will perform fuzzy string matching to find the nearest MHC by name. For example, HLA-A*02:01, A*02:01, HLAA0201, and A0201 are all considered valid and equivalent allele names. Additionally, synonymous substitutions and noncoding fields are handled, so HLA-A*02:01:01 should be mapped to HLA-A*02:01.

We do not validate allele names. BigMHC will make predictions even if given nonsense or MHC-II input, as it will find the nearest valid MHC name to the provided invalid allele name. The list of alleles used in our multiple sequence alignment, to which input is mapped, can be found in the pseudosequences data file.

Required Arguments

• -i or --input input CSV file
  • Columns are zero-indexed
  • Must have a column of peptides
  • Can also have a column of of MHC-I allele names
• -m or --model BigMHC model to load
  • el or bigmhc_el to load BigMHC EL
  • im or bigmhc_im to load BigMHC IM
  • Can be a path to a BigMHC model directory
  • Optional for bigmhc_train (if a model dir is specified, then transfer learn)

Required Arguments for Training

• -t or --tgtcol column index of target values
  • Elements in this column are considered ground truth values.
• -o or --out output directory
  • Directory to save model parameters for each epoch
  • Optional for transfer learning (defaults to model arg)

Input Formatting Arguments

• -a or --allele allele name or allele column
  • If allele is a column index, then a single MHC-I allele name must be present in each row
• -p or --pepcol peptide column
  • Is the column index of a CSV file containing one peptide sequence per row.
• -c or --hdrcnt header count
  • Skip the first hdrcnt rows before consuming input

Output Arguments

• -o or --out output file or directory
  • If using bigmhc_predict, save CSV data to this file
    • Defaults to input.prd
  • If using bigmhc_train, save the retrained BigMHC model to this directory
    • If transfer learning, defaults to the base model dir
• -z or --saveatt boolean indicating whether to save attention values
  • Only available for bigmhc_predict
  • Use 1 for true and 0 for false

Other Optional Arguments

• -d or --devices devices on which to run BigMHC
  • Set to all to utilize all GPUs
  • To use a subset of available GPUs, provide a comma-separated list of GPU device indices
  • Set to cpu to run on CPU (not recommended for large datasets)
• -v or --verbose toggle verbose printing
  • Use 1 for true and 0 for false
• -j or --jobs Number of workers for parallel data loading
  • These workers are persistent throughout the script execution
• -f or --prefetch Number of batches to prefetch per data loader worker
  • Increasing this number can help prevent GPUs waiting on the CPU, but increases memory usage
• -b or --maxbat Maximum batch size
  • Turn this down if running out of memory
  • If using bigmhc_predict, defaults to a value that is estimated to fully occupy the device with the least memory
  • If using bigmhc_train, defaults to 32
• -s or --pseudoseqs CSV file mapping MHC to one-hot encoding
• -l or --lr AdamW optimizer learning rate
  • Only available for bigmhc_train
• -e or --epochs number of epochs for transfer learning
  • Only available for bigmhc_train

Citation

@Article{Albert2023,
	author={Albert, Benjamin Alexander and Yang, Yunxiao and Shao, Xiaoshan M. and Singh, Dipika and Smith, Kellie N. and Anagnostou, Valsamo and Karchin, Rachel},
	title={Deep neural networks predict class I major histocompatibility complex epitope presentation and transfer learn neoepitope immunogenicity},
	journal={Nature Machine Intelligence},
	year={2023},
	month={Jul},
	day={20},
	issn={2522-5839},
	doi={10.1038/s42256-023-00694-6},
	url={https://doi.org/10.1038/s42256-023-00694-6}
}

License

See the LICENSE file