Machine learning driven antibody-antigen complex modelling and its applications to antibody therapeutics design

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Antibodies are one of the most important classes of pharmaceuticals, with over 100 antibody therapeutics approved against a wide variety of diseases and many more in active development. The development of antibody therapeutics is a time- and cost-intensive process, making it a promising target for acceleration using computational and ML-driven screening and design techniques. The prediction of the structure of antibodies in complex with antigen targets (also referred to as antibody-antigen docking) remains a challenging problem, with little significant progress published in recent years. The modelling of antibody structures in isolation, like many other areas of protein structure prediction, has been overhauled since 2020 by the application of modern machine learning models, inspired in many cases by the success of AlphaFold2. AlphaFold-multimer has shown promising results for general protein-protein docking but performs poorly on antibody-antigen docking due to the high diversity of the antibody binding regions. The proposed project would aim to develop a framework for the prediction of antibody-antigen interaction structures drawing on the recent advances in diffusion generative models and equivariant graph neural network architectures which have proven capable of step changes in protein and specifically antibody structure prediction. In recent years, diffusion models have drastically improved AI-generated image quality and have been successfully applied to biological tasks including protein engineering. The current state-of-the art for small molecule protein docking is DiffDock, a diffusion model. DiffDock-PP is an adapted version which performs rigid body protein-protein docking. During my rotation project I investigated the ability of DiffDock-PP to perform antibody-antigen docking when trained on an increased pool of antibody structural data. During my DPhil I will develop a new diffusion-based model for protein-protein docking, with the specific aim of docking antibodies. This will include changes to the spatial representations, more efficient memory usage, an attention model, and incorporation of physics-like terms to guide the reverse diffusion process. The model will be tested on experimentally derived docked 3D structures and, if successful, on computationally predicted structures. The creation of a reliable antibody-antigen docking model for computationally predicted 3D structures would allow researchers to understand how antibodies interact purely from sequencing data. This would greatly improve the pace of therapeutic antibody engineering as well as improve our understanding of immune escape during future pandemics. This project falls within the EPSRC research areas of "artificial intelligence technologies", "biological informatics research", and "computational and theoretical chemistry". It will be performed under the supervision of Professor Charlotte Deane from the Statistics department at the University of Oxford as well as Dr Constantin Schneider from Exscientia.