Biology subjects

Thorpe, C. J.

Publications and source records attributed to Thorpe, C. J..

3 recordsLinked to original sources

bioRxiv2026

Characterising AlphaFold 3s ability to predict T cellantigen specificity

T cells are a key part of the adaptive immune system. Using their surface-bound T cell antigen receptors (TCRs), these cells scan peptides and other antigens presented to them by major histocompatibility complex molecules (MHCs) on the surface of cells, searching for abnormalities. Although determining the map between TCRs and their target antigens is of vital importance for the design of safe and effective T cell-based vaccines and therapeutics, decoding these interactions is challenging. Experimental methods are not scalable, and sequence-based computational methods have issues generalising to new antigens. The IMMREP25 benchmark of methods for predicting T cell antigen specificity showed that AlphaFold-based methods promise improved generalisation to novel antigens. However, the ability of structure prediction models to predict T cell antigen specificity has not been robustly evaluated previously. In this work, we characterise AlphaFolds ability to predict T cell antigen specificity. We created a pipeline for high-throughput prediction of TCR:peptide-MHC (pMHC) structures using AlphaFold that is > 100 fold faster than the default implementation and used it to benchmark AlphaFold 3 (AF3) and similar models at predicting T cell antigen specificity. We investigated the underlying correlates of AlphaFold-derived binding scores and found that the models predictive power is related to the positioning of TCRs over the pMHC and not chemical interactions. Furthermore, we refine the AlphaFold-derived binding scores by training a machine learning model we call the PAE Aggregator. We then investigate AF3s ability to uncover the clustering rules of TCR repertoires and recapitulate mutational scanning experiments. These analyses show that AlphaFold3 clusters sequence-similar TCRs according to their binding mode and detects disrupting point mutations accurately. Our results highlight both the promise and the current limitations of structure-based approaches for predicting TCR specificity, guiding the development of more reliable immunological prediction methods.

systems biology↗

bioRxiv2026

Reverse engineering the fatally cross-reactive A3A TCR to decouple potency and specificity

T cell receptor (TCR) affinity enhancement can introduce off-target cross-reactivity with life-threatening consequences, as illustrated by the MAGE-A3-specific A3A TCR, which caused fatal cardiotoxicity through recognition of a Titin-derived peptide. Here, we reconstructed the cross-reactivity landscape by reverse-engineering A3A toward its wild-type precursor, generating intermediate variants in which engineered CDR2 residues are systematically reverted to the wild-type sequence. Reverting just two engineered residues yields a receptor, v9, that retains MAGE-A3 cytotoxicity comparable to A3A while eliminating Titin and other acquired cross-reactivities. Structurally, these substitutions reduce CDR2-MHC contacts and disrupt an intra-TCR CDR2-CDR3{beta} interaction, propagating conformational changes across CDR3 loops that reshape peptide engagement without altering docking geometry. These results demonstrate that mutations outside the peptide-contacting CDR3 loops can allosterically reconfigure antigen specificity and establish simple stepwise reverse engineering to wild-type as a strategy for correcting TCR cross-reactivity.

immunology↗

bioRxiv2022

Infection dynamics of co-transmitted reproductive symbionts are mediated by sex, tissue, and development

One of the most prevalent intracellular infections on earth is with Wolbachia: a bacterium in the Rickettsiales that infects a range of insects, crustaceans, chelicerates, and nematodes. Wolbachia is maternally transmitted to offspring and has profound effects on the reproduction and physiology of its hosts, which can result in reproductive isolation, altered vectorial capacity, mitochondrial sweeps, and even host speciation. Some populations stably harbor multiple Wolbachia strains, which can further contribute to reproductive isolation and altered host physiology. However, almost nothing is known about the requirements for multiple intracellular microbes to be stably maintained across generations while they likely compete for space and resources. Here we use a coinfection of two Wolbachia strains ("wHa" and "wNo") in Drosophila simulans to define the infection and transmission dynamics of an evolutionarily stable double infection. We find that a combination of sex, tissue, and host development contribute to the infection dynamics of the two microbes and that these infections exhibit a degree of niche partitioning across host tissues. wHa is present at a significantly higher titer than wNo in most tissues and developmental stages, but wNo is uniquely dominant in ovaries. Unexpectedly, the ratio of wHa to wNo in embryos does not reflect those observed in the ovaries, indicative of strain-specific transmission dynamics. Understanding how Wolbachia strains interact to establish and maintain stable infections has important implications for the development and effective implementation of Wolbachia-based vector biocontrol strategies, as well as more broadly defining how cooperation and conflict shape intracellular communities. IMPORTANCEWolbachia are maternally transmitted intracellular bacteria that manipulate the reproduction and physiology of arthropods, resulting in drastic effects on the fitness, evolution, and even speciation of their hosts. Some hosts naturally harbor multiple strains of Wolbachia that are stably transmitted across generations, but almost nothing is known about the factors that limit or promote these co-infections which can have profound effects on the hosts biology and evolution, and are under consideration as an insect-management tool. Here we define the infection dynamics of a known stably transmitted double infection in Drosophila simulans with an eye towards understanding the patterns of infection that might facilitate compatibility between the two microbes. We find that a combination of sex, tissue, and development all contribute how the coinfection establishes.

microbiology↗