Understanding the co-operability between T-cell receptor beta and alpha chains

The project focuses on understanding the co-operability between the T-cell receptor (TCR) alpha and beta chains, aiming to improve antigen specificity prediction. TCRs are crucial molecules in adaptive immunology, allowing T-cells to recognise antigen fragments presented by the Major Histocompatibility Complex (MHC). Accurate prediction of TCR-epitope binding is a "Cancer Grand Challenge," especially given the variability and complexity of these interactions. Traditionally, the beta chain (VDJ gene recombination) has been the primary focus due to its theoretical sequence diversity. However, recent evidence suggests that the alpha chain (VJ gene recombination) contributes significantly to specificity, necessitating a shift in how we approach TCR binding prediction.

This research aims to:

1. Investigate the interplay between TCR alpha and beta chains in antigen recognition.

2. Improve models for predicting TCR-epitope binding specificity.

3. Explore practical applications, such as off-target prediction and distinguishing between functional and non-functional binders, through case studies with Immunocore.

The project combines coherence analysis, structural modelling, and metadynamics to examine the relationship between TCR chains. Coherence analysis will be used to quantify how much one chain dictates the sequence of the other in antigen-specific populations. Structural modelling and metadynamics will provide insight into the physical interactions and stability of the TCR-peptide-MHC complex. This interdisciplinary approach, integrating computational techniques with immunological applications, addresses gaps in understanding TCR-epitope specificity and advances the development of predictive models. A particularly novel aspect is the focus on both chains (alpha and beta), rather than solely the beta chain, in determining specificity.

The results of this project could significantly advance our understanding of TCR-epitope recognition, with broad implications for immunotherapy, vaccine development, and autoimmune disease treatment. By improving the accuracy of TCR specificity prediction models, the research has the potential to enhance the precision of T-cell-based therapies, reduce off-target effects, and improve patient outcomes in cancer and infectious diseases. Moreover, the project's computational innovations could be applied to other biological systems, making it relevant to a wide range of scientific and medical fields.

This project falls within the EPSRC "Biological Informatics" research area, which focuses on understanding information processing in biological systems. The research aligns with EPSRC's goals of supporting interdisciplinary approaches that involve novel computational modelling for biological systems, with a particular emphasis on applying machine learning to improve predictions of biological interactions. It also aligns with EPSRC's interest in advancing data science through biological data analysis. By developing new computational tools and models for TCR-epitope binding prediction, this project directly contributes to the EPSRC's focus on data-enabled decision-making in biological systems. Furthermore, the research has relevance to synthetic biology, systems biology, and computational neuroscience, as the findings could inform the design of better immunotherapeutic, neural computing models, and biotechnological applications.

This project is in collaboration with Immunocore, a leading biotechnology company that specialises in the development of T-cell receptor-based therapies. Immunocore's involvement ensures that the research has a direct industrial application, particularly in immunotherapy. This collaboration not only strengthens the practical relevance of the project but also facilitates real-world testing of the models developed, such as distinguishing functional binders from non-functional ones, which is crucial for therapeutic efficacy.