PepSim: T-Cell Cross-Reactivity Prediction via Comparison of Peptide Sequence and Peptide-Hla Structure

Authors

Sarah Hall-Swan
Jared Slone
Mauricio M Rigo
Dinler A Antunes
Gregory Lizée
Lydia E Kavraki

Publication Date

1-1-2023

Journal

Frontiers in Immunology

Abstract

INTRODUCTION: Peptide-HLA class I (pHLA) complexes on the surface of tumor cells can be targeted by cytotoxic T-cells to eliminate tumors, and this is one of the bases for T-cell-based immunotherapies. However, there exist cases where therapeutic T-cells directed towards tumor pHLA complexes may also recognize pHLAs from healthy normal cells. The process where the same T-cell clone recognizes more than one pHLA is referred to as T-cell cross-reactivity and this process is driven mainly by features that make pHLAs similar to each other. T-cell cross-reactivity prediction is critical for designing T-cell-based cancer immunotherapies that are both effective and safe.

METHODS: Here we present PepSim, a novel score to predict T-cell cross-reactivity based on the structural and biochemical similarity of pHLAs.

RESULTS AND DISCUSSION: We show our method can accurately separate cross-reactive from non-crossreactive pHLAs in a diverse set of datasets including cancer, viral, and self-peptides. PepSim can be generalized to work on any dataset of class I peptide-HLAs and is freely available as a web server at pepsim.kavrakilab.org.

Keywords

Amino Acid Sequence, Peptides, T-Lymphocytes, Cytotoxic, Clone Cells, T-cell cross-reactivity, peptide-HLA, immunotherapy, structure comparison, sequence similarity

Comments

Supplementary Material

Data Availability Statement

PMID: 37187737

DOI

10.3389/fimmu.2023.1108303

PMID

37187737

PMCID

PMC10175663

PubMedCentral® Posted Date

April 2023

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes