Investigating B-cell repertoire data using deep learning approaches to aid in the development of antibody therapeutics

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Antibodies are important proteins of the immune system. They recognize potentially harmful molecules, binding to them and initiating their removal from the body. With approximately 100 antibodies approved for clinical use to date, they have become an important and growing class of pharmaceuticals. However, therapeutic antibody development is complicated by numerous requirements, including chemical stability, solubility, low viscosity, bioavailability, long serum half-life, non-immunogenicity, and resistance to fragmentation, aggregation, post-translational modification and proteolytic cleavage, while also retaining their desired functions (binding affinity, specificity and functional activity).Whilst methodologies for therapeutic antibody discovery are constantly evolving, it remains an expensive and cumbersome process. Insights introduced by in silico approaches, along with machine learning algorithms, which can extract and utilize information from previous experiments to predict the properties and functions of new antibodies, are therefore highly sought. Recently, methods such as UniRep have shown the possibilities of improving protein predictions by applying Natural Language Processing (NLP) inspired methods, notably transfer learning, on protein data. Transfer learning is when information from one domain is used in another related domain, which can be particularly powerful when only small data sets are available for the latter domain, a common occurrence with antibody data. Developing new ML tools for antibodies built on state-of-the-art NLP techniques can therefore have a large impact on the therapeutic antibody discovery.The aim of this project is to explore antibody data and develop novel machine learning tools for improving the predictions of antibody properties and functions. This includes; Investigating the large amount of sequence data available in the Observed Antibody Space database. Develop novel ML techniques, and adaptation of novel NLP techniques to work on biological data. Explore the use of these new techniques in antibody property and function prediction.This DPhil project is a collaboration between Prof. Charlotte Deane at the Oxford Protein Informatics Group (OPIG), University of Oxford and Dr. Iain H. Moal at GlaxoSmithKline (GSK), London. This project aligns with several of EPSRC's strategies and research areas. It mainly falls within the EPSRC Biological Informatics research area for its development of novel computational techniques to model and analyse biological data (machine learning tools for antibody predictions). Additionally, the project also falls within the EPSRC Analytical Science and Artificial Intelligence Technologies research areas, for our use of novel ML techniques to extract information from large datasets for analyzing and predicting properties of antibodies.