Assessing the Generalization Capabilities of TCR Binding Predictors via Peptide Distance Analysis

Citation

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Steps to Reproduce

Raw Data

VDJDB

This repository requires you to download VDJdb in .csv format. We select the following settings:

A viral subset of the VDJdb dataset, focusing on human host and MHC class I. We omitted MHC class II due to low samples available. The dataset includes peptides from SARS-CoV-2, Influenza A, Human Immunodeficiency Virus (HIV), Hepatitis C Virus (HCV) and Cytomegalovirus (CMV). We discarded data points which do not include both the CDR3-B and the peptide.

The dataset contains 52 unique MHC A and 1 MHC B alleles, 16,504 unique CDR3-A sequences, 28,831 unique CDR3-B sequences and 757 unique peptides, for a total of 34,415 binding samples.

Note: this code should work irrespectively of the filter chosen.

3D Structures of Epitopes (optional)

Testing the distance of the 3D structure between epitopes requires generating them through folding methods like AlphaFold, OmegaFold, or ESMFold.

We used the ESMFold API to obtain the PDB files:

curl -X POST --data "INSERT_EPITOPE_SEQUENCE" https://api.esmatlas.com/foldSequence/v1/pdb/ > path/to/INSERT_EPITOPE_SEQUENCE.pdb
sleep 60

Distance Data

Install the Repository

You can install the repository with the setup.sh file:

bash setup.sh

This should take care of all the dependencies and NetSolP. Alternatively use conda:

conda env create -f environment.yml

Sequence Distance

Use the file scripts/create_distance_seq.py to create sequence distances (LocalDS, GlobalDS, LocalXX and GlobalXX) . Use as such:

python create_data.py --input_path path/to/viral_vdjdb_full.csv --workers 16

The code also optionally supports NetSolP as a possible metric by providing the path as --netsolp_model PATH

Shape Distance

Use the file scripts/create_distance_rmsd.py to create RMSD shape distances. Use as such:

python create_data.py --input_path path/to/viral_vdjdb_full.csv  --structure_path path/to/structures_folder/ --workers 16

Split Creation

At this point you should have downloaded VDJdb and have one or more distance files. We will use these to create multiple split ranges. In this example we selected the ranges 0-33, 33-66, 66-100. Place a bash script similar to this in your scripts folder

python create_multiple_splits.py --input_path path/to/distance_file.csv --dataset_path path/to/vdjdb.csv --metric_to_split your_metric --tt_fig_out tt_fig_yourmetric_0 --tv_fig_out tv_fig_yourmetric_0 --lowest_percentiles 0 33 66 --highest_percentiles 33 66 100 --seed 0 --split_ratio .90 --max_epitope_count 5000 --min_epitope_count 5 --leeway 0.01
python create_multiple_splits.py --input_path path/to/distance_file.csv --dataset_path path/to/vdjdb.csv --metric_to_split your_metric --tt_fig_out tt_fig_yourmetric_1 --tv_fig_out tv_fig_yourmetric_1 --lowest_percentiles 0 33 66 --highest_percentiles 33 66 100 --seed 1 --split_ratio .90  --max_epitope_count 5000 --min_epitope_count 5 --leeway 0.01
python create_multiple_splits.py --input_path path/to/distance_file.csv --dataset_path path/to/vdjdb.csv --metric_to_split your_metric --tt_fig_out tt_fig_yourmetric_2 --tv_fig_out tv_fig_yourmetric_2 --lowest_percentiles 0 33 66 --highest_percentiles 33 66 100 --seed 2 --split_ratio .90 --max_epitope_count 5000 --min_epitope_count 5 --leeway 0.01
python create_multiple_splits.py --input_path path/to/distance_file.csv --dataset_path path/to/vdjdb.csv --metric_to_split your_metric --tt_fig_out tt_fig_yourmetric_3 --tv_fig_out tv_fig_yourmetric_3 --lowest_percentiles 0 33 66 --highest_percentiles 33 66 100 --seed 3 --split_ratio .90 --max_epitope_count 5000 --min_epitope_count 5 --leeway 0.01
python create_multiple_splits.py --input_path path/to/distance_file.csv --dataset_path path/to/vdjdb.csv --metric_to_split your_metric --tt_fig_out tt_fig_yourmetric_4 --tv_fig_out tv_fig_yourmetric_4 --lowest_percentiles 0 33 66 --highest_percentiles 33 66 100 --seed 4 --split_ratio .90 --max_epitope_count 5000 --min_epitope_count 5 --leeway 0.01

```

A thorough description of all the values is available in the `create_multiple_splits.py` file.