Biology subjects

Kelley, E. J.

Publications and source records attributed to Kelley, E. J..

3 recordsLinked to original sources

bioRxiv2025

Learned Geometry, Predicted Binding: Structurally-BasedPrediction of Peptide:MHC Binding Using AlphaFold 3 EnablesCD4 T Cell Epitope Prediction

The accurate prediction of T cell epitope peptides within proteins of interest has a wide range of applications, but is complicated by the multiple determinants of antigenicity, the polymorphism of the Major Histocompatibility Complex (MHC) locus, and the great diversity of possible peptide antigens. Leading in silico methods use a variety of statistical approaches to learn from sequences identified through both in vitro MHC binding and peptide elution studies, but their performance remains imperfect, particularly for MHC II-restricted responses. Here we present MHCIIFold-GNN, an entirely orthogonal solution to this problem that combines three new elements: (i) a highly-multiplexed peptide:MHCII binding assay, (ii) generalizable structural modeling using AlphaFold3, and (iii) transfer learning with a Graph-based Neural Network. Trained exclusively on newly-generated in vitro binders, we show that MHCIIFold-GNN enables state-of-the-art prediction of CD4 T cell epitopes presented by diverse MHC II proteins, on par with non-structural methods that rely on much larger datasets including naturally-processed ligands. Moreover, when MHCIIFold-GNN and a leading non-structural method are combined, we observe unparalleled performance on a held-out test set (11 % boost), underscoring the orthogonality of the methods. These results highlight the power of a new class of structure-informed approaches to the CD4 T cell epitope prediction problem.

bioinformatics↗

bioRxiv2024

Integrated, high-dimensional analysis of CD4 T cell epitope specificities and phenotypes reveals unexpected diversity in the response to Mycobacterium tuberculosis

Immunity to Mycobacterium tuberculosis (Mtb), like many pathogens, is encoded jointly by the antigen specificities and functions of responding CD4 T cells. However, these features span a large two-dimensional possibility space - defined on one axis by the Mtb proteome, and on the other by the T cell transcriptome - that exceeds the dimensionality of existing technologies. Here we present an approach ("CRESTA") that combines highly-multiplexed DNA-barcoded epitope probes, single cell sequencing, and clonal analysis of T Cell Receptors (TCRs) to robustly detect rare antigen-specific CD4 T cells across hundreds of epitopes simultaneously and reveal their transcriptome-wide phenotypes. By comprehensively assaying known epitopes in Mtb-infected participants, we reveal polyclonal and multi-epitope responses across a spectrum of differentiation states, uncover previously-unobserved phenotypic diversity within and between epitopes, and increase the total number of known Mtb epitope-mapped TCR:{beta}s by [~]8-fold. We expect CRESTA to enable high-dimensional analyses of CD4 T cell responses in various settings, including infection, cancer, autoimmunity and allergy.

immunology↗

bioRxiv2021

A framework to identify antigen-expanded T Cell Receptor (TCR) clusters within complex repertoires

Common approaches for monitoring T cell responses are limited in their multiplexity and sensitivity. In contrast, deep sequencing of the T Cell Receptor (TCR) repertoire offers a global view whose theoretical sensitivity is limited only by the depth of available sampling. However, assignment of antigen specificities within TCR repertoires has become a bottleneck. Here, we combine antigen-driven expansion, deep TCR sequencing and a novel analysis framework to show that homologous Clusters of Expanded TCRs (CETs) can be confidently identified without cell isolation, and assigned to antigen against a background of non-specific clones. We show that clonotypes within each CET respond to the same epitope, and that protein antigens stimulate multiple CETs reactive to constituent peptides. Finally, we demonstrate the personalized assignment of antigen-specificity to rare clones within fully-diverse unexpanded repertoires. The method presented here may be used to monitor T cell responses to vaccination and immunotherapy with high fidelity.

immunology↗