DProQ: A Gated-Graph Transformer for Protein Complex Structure Assessment

DProQ, is a Gated-Graph Transformer model for end-to-end protein complex structure's quality evaluation. DProQ achieves significant speed-ups and better quality compared to current baseline method. If you have any questions or suggestions, please contact us by xcbh6@umsystem.edu . We are happy to help!

Citation

If you think our work is helpful, please cite our work by:

@article {Chen2022.05.19.492741,
    author = {Chen, Xiao and Morehead, Alex and Liu, Jian and Cheng, Jianlin},
    title = {DProQ: A Gated-Graph Transformer for Protein Complex Structure Assessment},
    elocation-id = {2022.05.19.492741},
    year = {2022},
    doi = {10.1101/2022.05.19.492741},
    publisher = {Cold Spring Harbor Laboratory},
    URL = {https://www.biorxiv.org/content/early/2022/05/20/2022.05.19.492741},
    eprint = {https://www.biorxiv.org/content/early/2022/05/20/2022.05.19.492741.full.pdf},
    journal = {bioRxiv}
}

Dataset

Benchmark sets

We provide our benchmark tests HAF2 and DBM55-AF2 for download by:

wget https://zenodo.org/record/6569837/files/DproQ_benchmark.tgz

Each dataset contains:

1. decoy folder: decoys files
2. native folder: native structure files
3. label_info.csv: DockQ scores and CAPRI class label

Installation

1. Download this repository

   git clone https://github.com/BioinfoMachineLearning/DProQ.git

2. Set up conda environment locally

   cd DProQ
   conda env create --name DProQ -f environment.yml

3. Activate conda environment

   conda activate DPRoQ

Usage

Here is the inference.py script parameters' introduction.

python inference.py
-c --complex_folder     Raw protien complex complex_folder
-w --work_dir           Working directory to save all intermedia files and folders, it will created if it is not exits
-r --result_folder      Result folder to save two ranking results, it will created if it is not exits
-r --threads            Number of threads for parallel feature generation and dataloader, default=10
-s --delete_tmp         Set False to save work_dir and intermedia files, otherwise set True, default=False

Use provided model weights to predict protein complex structures' quality

DProQ requires GPU. We provide few protein complexes in example folder for test. The evaluation result Ranking.csv is stored in result_folder.

python ./inference.py -c ./examples/6AL0/ -w ./examples/work/ -r ./examples/result

You can build you onw dataset for evaluation, the data folder should look like:

customer_data_folder
├── decoy_1.pdb
├── decoy_2.pdb
├── decoy_3.pdb
├── decoy_4.pdb
└── decoy_5.pdb

Main results

Following four tables show DProQ's consistent best result on HAF2 and DBM55-AF2 test sets in terms of hit rate and ranking loss. The best result is highlighted on bold.

HAF2 test set

Table 1: Hit rate performance on the HAF2 dataset. The BEST column represents each target’s best-possible Top-10 result. The SUMMARY row lists the results when all targets are taken into consideration.

ID	DPROQ	DPROQ_GT	DPROQ_GTE	DPROQ_GTN	GNN_DOVE	BEST
7AOH	10/10/10	10/10/10	10/10/10	10/10/10	9/9/0	10/10/10
7D7F	0/0/0	0/0/0	0/0/0	0/0/0	0/0/0	5/0/0
7AMV	10/10/10	10/10/10	10/10/10	10/10/10	10/10/6	10/10/10
7OEL	10/10/0	10/9/0	10/10/0	10/10/0	10/10/0	10/10/0
7O28	10/10/0	10/10/0	10/10/0	10/10/0	10/10/0	10/10/0
7ALA	0/0/0	0/0/0	0/0/0	0/0/0	0/0/0	1/0/0
7MRW	5/4/0	0/0/0	0/0/0	0/0/0	0/0/0	10/10/0
7OZN	0/0/0	0/0/0	0/0/0	0/0/0	0/0/0	10/2/0
7D3Y	2/0/0	5/0/0	6/0/0	8/0/0	0/0/0	10/0/0
7NKZ	10/10/2	10/10/1	10/10/1	10/010/4	10/9/9	10/10/10
7LXT	1/1/0	0/0/0	0/0/0	0/0/0	1/0/0	10/10/0
7KBR	10/10/10	10/10/10	10/10/10	10/10/10	10/10/9	10/10/10
7O27	10/10/0	10/10/0	10/10/0	10/10/0	10/4/0	10/10/0
SUMMARY	10/9/4	8/7/4	8/7/4	8/7/4	8/7/3	13/10/4

Table 2: Ranking loss performance on the HAF2 dataset. The BEST row represents the mean and standard deviation of the ranking losses for all targets.

Target	DPROQ	DProQ_GT	DPROQ_GTE	DPROQ_GTN	GNN_DOVE
7AOH	0.066	0.026	0.026	0.058	0.928
7D7F	0.471	0.471	0.47	0.471	0.003
7AMV	0.01	0.021	0.017	0.019	0.342
7OEL	0.062	0.063	0.135	0.135	0.21
7O28	0.029	0.021	0.027	0.034	0.244
7ALA	0.232	0.226	0.226	0.226	0.226
7MRW	0.085	0.603	0.555	0.555	0.598
7OZN	0.409	0.409	0.49	0.281	0.457
7D3Y	0.326	0.33	0.012	0.326	0.295
7NKZ	0.164	0.175	0.175	0.164	0.459
7LXT	0.586	0.586	0.586	0.586	0.295
7KBR	0.068	0.152	0.152	0.17	0.068
7O27	0.03	0.079	0.079	0.079	0.334
BEST	0.195 ± 0.185	0.243 ± 0.206	0.227 ± 0.21	0.239 ± 0.187	0.343 ± 0.228

DBM55-AF2 test set

Table 3: Hit rate performance on DBM55-AF2 dataset. The BEST column represents each target’s best-possible Top-10 result. The SUMMARY row lists the results when all targets are taken into consideration.

Target	DPROQ	DPROQ_GT	DPROQ_GTE	DPROQ_GTN	GNN_DOVE	BEST
6AL0	9/2/0	10/0/0	10/0/0	10/2/0	6/0/0	10/2/0
3SE8	8/8/0	9/9/0	8/8/0	8/8/0	3/0/0	10/10/0
5GRJ	10/10/0	9/9/0	10/10/0	9/9/0	3/2/0	10/10/0
6A77	7/7/0	7/7/0	8/8/0	8/8/0	0/0/0	8/8/0
4M5Z	10/10/1	10/10/0	10/10/0	10/10/0	10/10/0	10/10/1
4ETQ	1/1/0	1/1/0	1/1/0	1/1/0	0/0/0	1/1/0
5CBA	10/10/1	10/10/0	10/10/0	10/10/1	10/10/3	10/10/6
5WK3	0/0/0	0/0/0	0/0/0	0/0/0	1/0/0	3/0/0
5Y9J	4/0/0	6/0/0	5/0/0	4/0/0	0/0/0	8/0/0
6BOS	10/10/0	10/10/0	10/10/0	10/10/0	10/10/0	10/10/0
5HGG	8/0/0	8/0/0	8/0/0	8/0/0	8/0/0	10/0/0
6A0Z	0/0/0	0/0/0	0/0/0	0/0/0	2/0/0	3/0/0
3U7Y	2/2/1	2/2/1	2/2/1	2/1/0	2/2/1	2/2/1
3WD5	10/8/0	9/8/0	9/8/0	9/8/0	0/0/0	10/10/0
5KOV	0/0/0	0/0/0	0/0/0	0/0/0	1/0/0	2/0/0
SUMMARY	12/10/3	12/9/1	12/9/1	12/10/1	10/4/1	15/10/3

Table 4: Ranking loss performance on the DBM55-AF2 dataset. The BEST row represents the mean and standard deviation of the ranking losses for all targets.

Target	DPROQ	DPROQ_GT	DPROQ_GTE	DPROQ_GTN	GNN_DOVE
6AL0	0.0	0.156	0.156	0.0	0.424
3SE8	0.079	0.041	0.041	0.079	0.735
5GRJ	0.024	0.012	0.095	0.012	0.776
6A77	0.037	0.062	0.0	0.037	0.591
4M5Z	0.015	0.026	0.026	0.015	0.221
4ETQ	0.0	0.76	0.0	0.748	0.759
5CBA	0.052	0.038	0.052	0.058	0.019
5WK3	0.114	0.114	0.114	0.186	0.087
5Y9J	0.0	0.0	0.0	0.0	0.382
6BOS	0.081	0.081	0.0	0.0	0.081
5HGG	0.051	0.051	0.121	0.051	0.121
6A0Z	0.207	0.207	0.207	0.207	0.062
3U7Y	0.0	0.021	0.0	0.0	0.756
3WD5	0.011	0.011	0.011	0.0	0.672
5KOV	0.065	0.08	0.085	0.087	0.0
BEST	0.049 ± 0.054	0.111 ± 0.182	0.061 ± 0.064	0.099 ± 0.185	0.379 ± 0.298