公开(公告)号：US20230129568A1

公开(公告)日：2023-04-27

申请号：US17969883

申请日：2022-10-20

Applicant: NEC Laboratories America, Inc.

Inventor： Renqiang Min ,  Hans Peter Graf ,  Erik Kruus ,  Yiren Jian

IPC: G16B15/00 ,  G06N3/08 ,  G16B30/10 ,  G16B40/20

Abstract: Systems and methods for predicting T-Cell receptor (TCR)-peptide interaction, including training a deep learning model for the prediction of TCR-peptide interaction by determining a multiple sequence alignment (MSA) for TCR-peptide pair sequences from a dataset of TCR-peptide pair sequences using a sequence analyzer, building TCR structures and peptide structures using the MSA and corresponding structures from a Protein Data Bank (PDB) using a MODELLER, and generating an extended TCR-peptide training dataset based on docking energy scores determined by docking peptides to TCRs using physical modeling based on the TCR structures and peptide structures built using the MODELLER. TCR-peptide pairs are classified and labeled as positive or negative pairs using pseudo-labels based on the docking energy scores, and the deep learning model is iteratively retrained based on the extended TCR-peptide training dataset and the pseudo-labels until convergence.