DRN-1D2D_Inter

inter-protein contact prediction from sequences of interacting proteins:

Requirements

• python3.8

  1. pytorch1.9
  2. Biopython
  3. esm
  4. numpy

  Please note: To implement protein language models (ESM-1b and ESM-MSA-1b in this study) in esm, model weights of these protein language models should be downloaded first from the links provided in the "Available Models and Datasets" table of esm github. The paths of these model weights need to be set in predict.py later. Besides, the contact regression parameter files of ESM-1b: esm1b_t33_650M_UR50S-contact-regression.pt and ESM-MSA-1b: esm_msa1b_t12_100M_UR50S-contact-regression.pt should be stored in the same directory with the model parameter files.

• other packages

  1. alnstats (directly download the executable file, and change its mode to be executable)
  2. fasta2aln (directly dowload the executable file, and change its mode to be executable)
  3. hh-suite
  4. CCMpred

Installation

1. Install DRN-1D2D_Inter

git clone https://github.com/ChengfeiYan/DRN-1D2D_Inter.git

2. Modify the path of each tool (CCMpred, alnstats ...) and the paths of the model weights of the protien lanuage models (ESM-1b and EMS-MSA-1b) in predict.py

3. Copy the esm1b_t33_650M_UR50S-contact-regression.pt from /data/regression to the location of ESM-1b's model weights; Copy the esm_msa1b_t12_100M_UR50S-contact-regression.pt from /data/regression to the location of ESM-MSA-1b's model weights;

4. Download the trained models

Download the trained models from trained models, then unzip it into the folder named "model".

Usage

python predict.py sequenceA msaA sequenceB msaB result_path device
1.  sequenceA: fasta file corresponding to target A.
2.  msaA: a3m file corresponding to target A (multiple sequence alignment).
3.  sequenceB: fasta file corresponding to target B (multiple sequence alignment).
4.  msaB: a3m file corresponding to target B.
5.  result_path: [a directory for the output]
6.  device: cpu, cuda:0, cuda:1, ...

Where MSA should be derived from Uniref90 or Uniref100 database.

Example

python predict.py ./example/1GL1_A.fasta ./example/1GL1_A_uniref100.a3m ./example/1GL1_I.fasta ./example/1GL1_I_uniref100.a3m ./example/result cpu

The output of exmaple(1GL1)

It should be noted, we downsampled the MSAs of the example target due to the file size limiation of github. The real performance of DRN-1D2D_Inter for the provided example should be better in real practice.

Train

The script used to train DRN-1D2D_Inter is train.py, which contains all the details of training DRN-1D2D_Inter, including how to choose the best model, how to calculate the loss, etc.

Reference

Please cite: Yunda Si, Chengfei Yan, Improved inter-protein contact prediction using dimensional hybrid residual networks and protein language models, Briefings in Bioinformatics, 2023, bbad039, https://doi.org/10.1093/bib/bbad039

If you meet any problem in installing or running the program, please contact chengfeiyan@hust.edu.cn.