Abstract
Predicting T-cell receptor (TCR) specificity is one of the hardest challenges in immunology owing to difficulties in perturbing this system experimentally and computationally. Successful prediction of TCR specificity can offer a powerful to tool to study T-cell tolerance, antigen recognition, and autoimmunity as well as open doors towards de-novo TCR design and TCR-based Chimeric Antigen Receptor (CAR) T therapies. In this work, we developed a computational model based on experimental insights, molecular dynamics simulations (MD), and machine learning (ML) that can reliably predict TCR specificity towards peptide-class II major histocompatibility (pMHCII) complexes. Our model leverages biophysical information from MD simulations of AlphaFold3 generated TCR-pMHCII complexes to reliably predict T-cell activation outcomes. Inclusion of a novel biophysical parameter termed nonconvex interactions combined with interaction energy calculations, allowed us to improve T-cell activation predictions over an ML model only trained on interaction energies and also offer a compelling theory for TCR specificity encoding via kinetic entrapment.
Committee Chair
Chyi-Song Hsieh
Committee Members
Daved Fremont Michael Vahey
Degree
Master of Science (MS)
Author's Department
Biomedical Engineering
Document Type
Thesis
Date of Award
Spring 5-7-2025
Language
English (en)
DOI
https://doi.org/10.7936/52y2-t347
Author's ORCID
https://orcid.org/0000-0002-0379-4788
Recommended Citation
Wang, Sean J., "Molecular Dynamics and Computational Predictions of T-cell Activation" (2025). McKelvey School of Engineering Theses & Dissertations. 1198.
The definitive version is available at https://doi.org/10.7936/52y2-t347
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