Biology subjects

Ilievski, I.

Publications and source records attributed to Ilievski, I..

2 recordsLinked to original sources

bioRxiv2026

Beyond Better Models: Barriers and a Path Forward in Generalisable TCR Antigen Specificity Prediction

T cells are at the centre of adaptive immunity, protecting the organism against infection and cancer. T cell receptors (TCRs), expressed on the surface of T cells, enable the recognition of target cells through interactions with peptides presented by major histocompatibility complex (pMHC) molecules. Reliable, large-scale mapping of TCRs to their cognate pMHCs remains a holy grail of modern systems immunology, with far-reaching implications for cancer immunotherapy, vaccine development, and the understanding and treatment of autoimmune diseases. While exhaustive experimental mapping remains intractable due to the enormous combinatorial sequence space of TCRs and pMHCs, computational approaches offer a promising alternative. Consequently, we have witnessed increasingly sophisticated computational models of TCR antigen specificity, from small neural networks to deep generative models and large protein language models. However, several independent benchmarks have shown that these models, despite their size and sophistication, largely fail to generalise to TCRs or pMHCs not present in the data available during model development. Here, we investigate possible reasons for this and suggest a path forward to generalisable TCR:pMHC prediction. First, our analysis of the largest public repositories of TCR:pMHC data reveals significant biases towards a small number of antigens and MHC molecules, sparse sampling of TCR sequence space per pMHC, as well as inconsistencies and inaccuracies in data collation. Second, we show the importance of carefully setting up model evaluation to measure discrimination based on antigen specificity instead of confounding factors present in the evaluation data. Finally, we highlight the need for and propose how best to match the evaluation metric with the specific downstream application of the predictions. Our investigation suggests that we, as a community, need more representative and better curated data, and a robust set of metrics and benchmarks to guide future model development towards generalisable TCR:pMHC specificity prediction.

immunology↗

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↗