Introduction

Source code for Seq2Symm: Rapid and accurate prediction of protein homo-oligomer symmetry

Rapid prediction of homo-oligomer symmetries using a single sequence as input

Getting Started

Dependencies are in the yaml file esm2_finetune.yaml

conda env create --name esm2 --file=esm2_finetune.yaml

Downloads

1. the predictions from the model on various datasets, predictions on proteomes http://files.ipd.uw.edu/pub/seq2symm/predictions.zip

2. the trained model http://files.ipd.uw.edu/pub/seq2symm/ESM2_model.ckpt

Training the model

python src/finetune.py --meta_data_file ../datasets/homomer_pdbids_hash_clusterid_labels_sampled.csv --data_dir ../datasets/ --model_dir models/ --output_model seq2symm --output_dir outputs/seq2symm --bs 16 --data_splits_file ../datasets/train_val_test_splits.pkl --limit 65536 --granularity 3 --n_classes 20 --n_epoch 100 --weighted_sampler 1

Predicting using the model

A jupyter notebook is available at src/load_chkpt_and_predict.ipynb that shows examples of how this is done for two different file formats

Predicting via command line

The src/predict_oligmerization.py script predicts the oligomerization states of protein sequences provided in a FASTA file, outputting the probabilities for each symmetry state with a probability >= 1%.

python predict_oligomerization.py -input_file <input_fasta> -chkpt_file <model_checkpoint> [-output_file <results.csv>] [-batch_size <n>]

• -input_file: Path to the input FASTA file containing the sequences for prediction (required).
• -chkpt_file: Path to the model checkpoint file (required).
• -output_file: Path to save the prediction results as a CSV file (default: ./results.csv).
• -batch_size: Batch size for processing sequences (default: 1).

Example output:

fasta_id, predicted_oligomerization_state
ID1, "{'C1': 0.9, 'C2': 0.1, 'Other': 0.001}"
ID2, "{'C4': 0.85, 'C6': 0.15, 'D3': 0.01}"

Which you can load with pandas like this:

import pandas as pd
import ast
df = pd.read_csv("results.csv")
df['predicted_oligomerization_state'] = df['predicted_oligomerization_state'].apply(ast.literal_eval)