Biology subjects

Ghoreyshi, Z. S.

Publications and source records attributed to Ghoreyshi, Z. S..

4 recordsLinked to original sources

bioRxiv2026

A biophysical framework for accurately identifying antigen single-amino acid escape variants and corresponding variant-specific compensatory TCR sequences

The impact of single amino acid substitution on T-cell receptor (TCR) recognition is central to understanding the molecular determinants of TCR specificity and degeneracy during viral mutational escape, cancer recognition, and autoimmunity. In this study, we developed a biophysics-informed computational approach integrating experimental alanine-scan mutagenesis data from the autoimmune-associated ALWGPDPAAA peptide bound to HLA-A*02:01 together with coarse-grained structural modeling. Our approach reconstructs the energetics and structural determinants underpinning the observed loss of recognition by the diabetogenic 1E6 TCR upon single-point mutations, specifically at the critical Pro5 and Asp6 residues. Leveraging the computational models ability to incorporate multiple structural templates into binding predictions, this approach quantitatively reproduces experimentally measured affinity disruptions. Additionally, we apply our approach to identify potential compensatory interactions capable of restoring binding affinity through alternative residue interactions. This integrative computational framework contributes a strategy for inferring TCR-peptide binding energetics at the single amino acid level, guiding the rational design of peptide-based immunotherapeutics, and predicting the functional impacts of clinically relevant peptide variants.

biophysics↗

bioRxiv2025

Biophysical modeling for accurate T cell specificity prediction of viral and tumor antigens

Accurate predictions of T cell receptor (TCR) specificity remain an important open problem in immunology, with broad implications for vaccine design, optimal immunotherapy, and improved management of autoimmune diseases. However, diversity in peptide antigens and TCR sequences at the level of individual patient repertoires remains a formidable computational challenge. Here, we develop a joint experimental and computational approach for predicting the antigen specificity of clinically-derived TCR sequences. Our model is trained on a combination of experimentally pre-identified and in silico-predicted TCR-pMHC structures using AlphaFold3. We apply our structural model in the clinical setting of hematopoietic stem cell transplant (HSCT) and demonstrate that our model is able to effectively discern the specificity of previously unseen donor and patient-derived TCR sequences against tumor associated and viral antigens. Model performance was further enhanced through the integration of sequence-based clustering and structurally diverse training templates. Our results highlight the predictive capabilities of structurally guided machine learning frameworks, trained on a minority test dataset, for antigen specificity prediction on unseen TCR sequences and their potential impact on a wide range of immunological applications.

immunology↗

bioRxiv2024

Feature Selection Enhances Peptide Binding Predictions for TCR-Specific Interactions

T-cell receptors (TCRs) play a critical role in the immune response by recognizing specific ligand peptides presented by major histocompatibility complex (MHC) molecules. Accurate prediction of peptide binding to TCRs is essential for advancing immunotherapy, vaccine design, and understanding mechanisms of autoimmune disorders. This study presents a novel theoretical method that explores the impact of feature selection techniques on enhancing the predictive accuracy of peptide binding models tailored for specific TCRs. To evaluate the universality of our approach across different TCR systems, we utilized a dataset that includes peptide libraries tested against three distinct murine TCRs. A broad range of physicochemical properties, including amino acid composition, dipeptide composition, and tripeptide features, were integrated into the machine learning-based feature selection framework to identify key features contributing to binding affinity. Our analysis reveals that leveraging optimized feature subsets not only simplifies the model complexity but also enhances predictive performance, enabling more precise identification of TCR-peptide interactions. The results of our feature selection method are consistent with findings from hybrid approaches that utilize both sequence and structural data as input as well as experimental data. Our theoretical approach highlights the role of feature selection in peptide-TCR interactions, providing a powerful tool for uncovering the molecular mechanisms of the T-cell response and assisting in the design of more advanced targeted therapeutics.

biophysics↗

bioRxiv2024

Integration of Kinetic Data into Affinity-Driven Models for Improved T Cell-Antigen Specificity Prediction

T cell receptor (TCR) and peptide-major histocompatibility complex (pMHC) interactions that result in T cell activation are complex and have been distinguished by their equilibrium affinity and kinetic profiles. While prior affinity-based models can successfully predict meaningful TCR-pMHC interactions in many cases, they occasionally fail at identifying TCR-pMHC interactions with low binding affinity. This study analyzes TCR-pMHC systems for which empirical kinetic and affinity data exist and prior affinity-based predictions fail. We identify a criteria for TCR-pMHC systems with available kinetic information where the introduction of a correction factor improves energybased model predictions. This kinetic correction factor offers a means to refine existing models with additional data and offers molecular insights to help reconcile previously conflicting reports concerning the influence of TCR-pMHC binding kinetics and affinity on T cell activation.

biophysics↗