CLEAN-Contact

This repository contains the code and data for the paper "CLEAN-Contact: Contrastive Learning-enabled Enzyme Functional Annotation Prediction with Structural Inference".

Introduction

CLEAN-Contact requires protein sequences and structures as input. The sequence inputs can be CSV or FASTA files. The structure inputs must be PDB files.

Please note that if the proteins in the input CSV or FASTA files already have structures in the Alphafold database, CLEAN-Contact will pull those PDB files for the user. In that case the user will only need to include their protein sequences as input. Otherwise, if the protein structures are not already in the Alphafold database, the user should obtain their PDB files from another method before running CLEAN-Contact.

Installation and Setup

Requirements

Python == 3.10.13, PyTorch == 2.1.1, torchvision == 0.16.1; fair-esm == 2.0.0, pytorch-cuda == 12.1

Installation

1. Clone the code and start setting up the conda environment

   git clone https://github.com/PNNL-CompBio/CLEAN-Contact.git
   cd CLEAN-Contact
   conda create -n clean-contact python=3.10 -y
   conda activate clean-contact
   conda install -c conda-forge biopython biotite matplotlib numpy pandas pyyaml scikit-learn scipy tensorboardx tqdm

2. Install PyTorch and torchvision with CUDA
   • Find your operating system's installation method here: https://pytorch.org/get-started/locally/
3. Install fair-esm

   python -m pip install fair-esm==2.0.0
   python build.py install
   git clone https://github.com/facebookresearch/esm.git
   

Setup

1. Create required folders:

   python
   >>> from src.CLEAN.utils import ensure_dirs
   >>> ensure_dirs()
   

2. Download the precomputed embeddings and distance map for both training and test data from here and put both esm_data and distance_map folders under the data folder.

Pre-inference

Before running the inference step, extract the sequence representations and structure representations for your own data and then merge them.

Sequence inputs can be in a CSV format or FASTA format and should be placed in the data folder. CSVs must have the columns: "Entry", "EC number", and "Sequence", where only "EC number" should be empty.

Structure inputs must be in PDB format. CLEAN-Contact will grab the PDBs from the Alphafold database if the structure is available, otherwise use your own pre-generated PDB files as input. In either case create your PDB folder, such as , in the top level directory of CLEAN-Contact where extract_structure_representation.py is.

Extract sequence and structure representations

Data in CSV format

For example, your <csv-file> is data/split100_reduced.csv. Then run the following commands:

python extract_structure_representation.py \
    --input data/split100_reduced.csv \
    --pdb-dir <pdb-dir> 

python
>>> from src.CLEAN.utils import csv_to_fasta, retrieve_esm2_embedding
>>> csv_to_fasta('data/split100_reduced.csv', 'data/split100_reduced.fasta') # fasta file will be 'data/split100_reduced.fasta'
>>> retrieve_esm2_embedding('split100_reduced')

Data in FASTA format

For example, your <fasta-file> is data/split100_reduced.fasta. Then run the following commands:

python
>>> from src.CLEAN.utils import fasta_to_csv, retrieve_esm2_embedding
>>> fasta_to_csv('split100_reduced') # output will be 'data/split100_reduced.csv'
>>> retrieve_esm2_embedding('split100_reduced')

python extract_structure_representation.py \
    --input data/split100_reduced.csv \
    --pdb-dir <pdb-dir> 

Merge representations and compute distance map

Run the following commands to merge the sequence and structure representations:

python
>>> from src.CLEAN.utils import merge_sequence_structure_emb
>>> merge_sequence_structure_emb(<csv-file>)

If your data will be used as training data, run the following commands to compute distance map:

python
>>> from src.CLEAN.utils import compute_esm_distance
>>> compute_esm_distance(<csv-file>)

Inference

If your dataset is in csv format, you can use the following command to inference the model:

python inference.py \
    --train-data split100_reduced \
    --test-data <test-data> \
    --gmm <gmm> \
    --method <method>

Replace <test-data> with your test data name, <gmm> with the list of fitted Gaussian Mixture Models (GMMs) and <method> with the maxsep or pvalue.

If you provide <gmm>, the model will use the fitted GMMs to compute prediction confidence.

Run python extract_confidence_result.py and python print_prediction_confidence_results.py to extract and print the prediction confidence results, respectively, to reproduce results in Fig. S4-6.

We provide the fitted GMMs based on maxsep at gmm_test/gmm_lst.pkl.

If your dataset is in fasta format, you can use the following command to inference the model:

python inference_fasta.py \
    --train-data split100_reduced \
    --fasta <fasta-file> \
    --gmm <gmm> \
    --method <method>

Performance metrics measured by Precision, Recall, F-1, and AUROC will be printed out. Per sample predictions will be saved in results folder.

Training

Sequences whose EC number has only one sequence are required to mutated to generate positive samples. We provide the mutated sequences in data/split100_reduced_single_seq_ECs.csv. To get your own mutated sequences, run the following command:

python
>>> from src.CLEAN.utils import mutate_single_seq_ECs
>>> mutate_single_seq_ECs('split100_reduced')

python mutate_conmap_for_single_EC.py \
    --fasta data/split100_reduced_single_seq_ECs.fasta 

python
>>> from src.CLEAN.utils import fasta_to_csv, merge_sequence_structure_emb
>>> fasta_to_csv('split100_reduced_single_seq_ECs')
>>> merge_sequence_structure_emb('split100_reduced_single_seq_ECs')

To train the model mentioned in the main text (addition model), modify arguments in train-split100-reduced-resnet50-esm2-2560-addition-triplet.sh and run the following command:

./train-split100-reduced-resnet50-esm2-2560-addition-triplet.sh

To train models with the other combinations (contact_1 and contact_2), modify arguments in train-split100-reduced-resnet50-esm2-2560-contact_1-triplet.sh and train-split100-reduced-resnet50-esm2-2560-contact_2-triplet.sh, respectively, and run the following command:

./train-split100-reduced-resnet50-esm2-2560-contact_1-triplet.sh
./train-split100-reduced-resnet50-esm2-2560-contact_2-triplet.sh