/TEIM

TEIM: TCR-Epitope Interaction Modeling. It's the code of my first meeting.

Primary LanguagePythonMIT LicenseMIT

TEIM: TCR-Epitope Interaction Modeling

This repository contains the codes of TEIM (TCR-Epitope Interaction Modeling). TEIM is a deep learning-based model to predict the TCR-epitope interactions, including two submodels TEIM-Res (TEIM at Residue level) and TEIM-Samp (TEIM at Sequence level).

Both models only takes the primary sequences of CDR3βs and the epitopes as input. TEIM-Res predicts the distances and the contact probabilities between all residue pairs of CDR3βs and epitopes. TEIM-Seq predicts whether the CDR3βs and epitopes can bind to each other.

Dependency

  1. Install Python>=3.8
  2. Install basic packages using: 
    pip install -r requirements.txt
  3. Install ANARCI for CDR3 numbering on the new environment. 
    conda install -c bioconda anarci

We also provided a docker file to facilitate the installation of environment. You can build the docker by runing

docker build -t teim:v1 .

Inference

Predict the residue-level interactions of TCR-epitope pairs

  1. Put your input TCR-epitope sequence pairs in the inputs/inputs.csv file. The TCRs are represented by their CDR3β sequences and the epitopes are represented by their sequences in the following format:

    cdr3 epitope
    CASAPGLAGGRPEQYF LLFGYPVYV
    CASRGAAGGRPQYF MLWGYLQYV
    CASRPGLAGGRAEQYF FTDSSVWA

  2. Run

    python scripts/inference_res.py

  3. The predicted distance matrices and contact site matrices are in the outputs directory:

    • The predicted distance matrix and contact matrix are in the files names as dist_<cdr3>_<epitope>.csv and site_<cdr3>_<epitope>.csv, respectively.
    • The rows and columns of the matrices represent the CDR3βs and epitopes, respectively.
    • The values in the distance matrix stand for the distances of residue pairs (unit: angstrom) and the values in the contact matrix stand for the predicted contact scores (probabilities) of residue pairs (range from 0 to 1).

Predict the seqence-level interactions of TCR-epitope pairs

  1. Put your input TCR-epitope sequence pairs in the inputs/inputs_bd.csv file. The format is the same as inputs/inputs.csv (residue-level input file).
  2. Run 
    python scripts/inference_seq.py
  3. The predicted sequence-level binding scores are in the outputs/sequence_level_binding.csv. The binding column in the file represent the predicted sequence-level binding scores (probabilities) of the TCR-epitope pair.