ArticleComputational and structural biotechnology journal2022
A computational algorithm to assess the physiochemical determinants of T cell receptor dissociation kinetics.
Article in Computational and structural biotechnology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 4 citations in OpenAlex.
- A Computational Strategy for the Rapid Identification and Ranking of Patient-Specific T Cell Receptors Bound to Neoantigens.Macromolecular rapid communications · 2024Article
- AbMelt: Learning antibody thermostability from molecular dynamics.Biophysical journal · 2024Article
- AbLEF: antibody language ensemble fusion for thermodynamically empowered property predictions.Bioinformatics (Oxford, England) · 2024Article
- A dynamic biomimetic model of the membrane-bound CD4-CD3-TCR complex during pMHC disengagement.Biophysical journal · 2023Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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