Dan Hudson is a 3rd year DPhil candidate on the prestigious BBSRC Interdisciplinary Bioscience Doctoral Training Programme at the University of Oxford. Dan holds first-class degrees in Biochemistry (M.Biochem, Oxford, 2010) and artificial intelligence (MSc., Queen Mary University of London, 2020), returning to full-time academic research in 2021 after more than ten years in the pharmaceutical industry.
Inspired by his studies at QMUL, Dan left a senior position at GSK Vaccines R&D to pursue independent research in the field of computational immunology. This latest challenge allows him to combine postgraduate studies in T cell biology (Davis group, MRC Weatherall Institute of Molecular Medicine, 2009) and machine learning (Gong group, QMUL 2018-20) with considerable industry expertise.
In order to fight off attacks by the microscopic bugs that cause disease, and to find and eliminate cancers, specialist cells of the immune system need to be able to recognise these invaders as foreign or abnormal. This is achieved using molecules on their outward facing cell membranes, known as receptors.
Dan's PhD research will rely on the use of powerful algorithms to understand the types of immune cell receptors found in the human body during health and disease. The hope is that this will help identify new immune signatures of disease, and inform the use of these receptors for new drug development.
In particular, Dan is interested in the application of machine learning techniques to dissect the binding preferences of human T cell receptors (TCRs) implicated in human disease, and in the development of novel TCR therapies.
A comparison of clustering models for inference of T cell receptor antigen specificity
Hudson D. et al, (2023)
Can we predict T cell specificity with digital biology and machine learning?
Hudson D. et al, (2023), Nature Reviews Immunology, 23, 511 - 521
TMQuery: a database of precomputed template modeling scores for assessment of protein structural similarity
Price S. et al, (2022), Bioinformatics, 38, 2062 - 2063
Transfer Learning for Protein Structure Classification at Low Resolution
Hudson A. and Gong S., (2020)
Remarkably low affinity of CD4/peptide-major histocompatibility complex class II protein interactions
Jönsson P. et al, (2016), Proceedings of the National Academy of Sciences, 113, 5682 - 5687