Investigating B-cell repertoire data using deep learning approaches to aid in the development of antibody therapeutics - Olsen, TH (2023)
Antibodies have become an invaluable form of biotherapeutics, with an increasing number of new antibody derived therapeutics being developed and marketed each year. Despite their success, the process of antibody discovery and design remains challenging. In this DPhil, we leverage publicly available antibody sequences to develop computational tools to aid in the design of therapeutic antibodies. We introduce two new databases; an updated and expanded Observed Antibody Space (OAS) and the Patent and Literature Antibody Database (PLAbDab). To investigate these databases, we developed KA-Search, a rapid and flexible tool for antibody sequence identity search, and demonstrated its use at mining the billions of sequences in OAS and obtaining new binding-specific insights with PLAbDAb.