A computational algorithm to assess the physiochemical determinants of T cell receptor dissociation kinetics

Zachary A Rollins; Jun Huang; Ilias Tagkopoulos; Roland Faller; Steven C George

doi:10.1016/j.csbj.2022.06.048

A computational algorithm to assess the physiochemical determinants of T cell receptor dissociation kinetics

Comput Struct Biotechnol J. 2022 Jun 25:20:3473-3481. doi: 10.1016/j.csbj.2022.06.048. eCollection 2022.

Authors

Zachary A Rollins¹, Jun Huang², Ilias Tagkopoulos³, Roland Faller¹, Steven C George⁴

Affiliations

¹ Department of Chemical Engineering.
² University of California, Davis, Davis, California, Pritzker School of Molecular Engineering, University of Chicago, Chicago, IL.
³ Department of Computer Science.
⁴ Department of Biomedical Engineering.

Abstract

The rational design of T Cell Receptors (TCRs) for immunotherapy has stagnated due to a limited understanding of the dynamic physiochemical features of the TCR that elicit an immunogenic response. The physiochemical features of the TCR-peptide major histocompatibility complex (pMHC) bond dictate bond lifetime which, in turn, correlates with immunogenicity. Here, we: i) characterize the force-dependent dissociation kinetics of the bond between a TCR and a set of pMHC ligands using Steered Molecular Dynamics (SMD); and ii) implement a machine learning algorithm to identify which physiochemical features of the TCR govern dissociation kinetics. Our results demonstrate that the total number of hydrogen bonds between the CDR2β-MHC⍺(β), CDR1α-Peptide, and CDR3β-Peptide are critical features that determine bond lifetime.

Keywords: Immunogenicity; Machine learning; Peptide major histocompatibility complex; Steered molecular dynamics; T cell receptor.