Academic interests
My PhD research mainly focuses on modelling the complex sequence dependencies that govern immune receptor binding to antigen.
Summary
The adaptive immune system can recognize (bind to) virtually any antigen (viruses, bacteria, cancer, autoantigens) via an immense array of antigen-binding antibodies and T-cell receptors, the immune receptor repertoire. Therefore, immune receptors are of fundamental importance for public health: vaccine-driven immunity relies almost entirely on an antibody-mediated response and therapeutic antibodies and T-cell receptors have garnered an irreplaceable position in anti-cancer and anti-autoimmunity therapy. Immune receptor-antigen binding occurs via the molecular interaction on an interface that is generally only 15 amino acids long. So far, however, it remains a challenge to predict the antigen target given an immune receptor or vice-versa, hindering the rule-based implementation of in silico engineering of immune receptor therapeutics and diagnostics. Given the combination of the facts that the interface between immune receptor and antigen is short and immune receptor binding capacity is virtually infinite, there must exist higher-order dependencies that govern binding between amino acids in the immune-receptor-antigen interface. In this thesis, we will develop statistical methods to uncover these dependencies and use them to improve the predictive performance of immune-receptor-antigen binding.