SPOT-Contact-Single: Improving Single-Sequence-Based Prediction of Protein Contact Map using a Transformer Language Model.

The standalone version / CLI-tool of SPOT-Contact-Single available for public use for research purposes.

Contents

• Introduction
• Results
• System Requirments

SPOT-Contact-Single

Introduction

Accurate prediction of protein contact-map is essential for accurate protein structure and function prediction. As a result, many methods have been developed for protein contact map prediction. However, most methods rely on protein-sequence-evolutionary information, which may not exist for many proteins due to lack of naturally occurring homologous sequences. Moreover, generating evolutionary profiles is computationally intensive. Here, we developed a contact-map predictor utilizing the output of a pre-trained language model ESM-1B as an input along with a large training set and an ensemble of residual neural networks.

Results

We showed that the proposed method makes a significant improvement over a single-sequence-based predictor SSCpred with 15% improvement in the F1-score for the independent CASP14-FM test set. It also outperforms evolutionary-profile-based methods TrRosetta and SPOT-Contact with 48.7% and 48.5% respective improvement in the F1-score on the proteins without homologs (Neff=1) in the independent SPOT-2018 set. The new method provides a much faster and reasonably accurate alternative to evolution-based methods, useful for large-scale prediction.

System Requirments

Hardware Requirments: SPOT-Contact-single predictor has been tested on standard ubuntu 18 computer with approximately 32 GB RAM to support the in-memory operations.

• Python3.7
• Anaconda
• CUDA 10.1 (Optional if using GPU)
• cuDNN (>= 7.4.1) (Optional if using GPU)

Installation

To install SPOT-Contact-Single and it's dependencies following commands can be used in terminal:

1. git clone https://github.com/jas-preet/SPOT-Contact-Single.git
2. cd SPOT-Contact-Single

To download the model check points from the dropbox use the following commands in the terminal:

3. wget https://apisz.sparks-lab.org:8443/downloads/Resource/Protein/2_Protein_local_structure_prediction/contact_jits.tar.xz
4. tar -xvf contact_jits.tar.xz

To install the dependencies and create a conda environment use the following commands

5. conda create -n spot_contact_sgl python=3.7
6. conda activate spot_contact_sgl

if GPU computer: 7. conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.1 -c pytorch

for CPU only 7. conda install pytorch==1.6.0 torchvision==0.7.0 cpuonly -c pytorch

8. pip install fair-esm

9. conda install pandas=1.1.1

10. conda install tqdm

Input dependency

SPOT-Contact-Single uses the output of SPOT-1D-Single. Please generate the prediction of SPOT-1D-Single from the Stand-alone version available at https://github.com/jas-preet/SPOT-1D-Single. Copy the prediction for the required proteins to data_files/out_spot_1d_sgl/

Execute

To run SPOT-Contact-Single use the following command

python -W ignore spot_contact_single.py --file_list file_lists/file_list_casp14.txt --save_path results/ --device cuda:0 --esm_device cuda:0

or

python -W ignore spot_contact_single.py --file_list file_lists/file_list_casp14.txt --save_path results/ --device cpu --esm_device cpu

License

MIT License

for more details on this work refer the manuscript

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