Structural Bioinformatics Computational design of therapeutic antibodies binding GPCRs in a conformation-dependent manner

Lead Research Organisation: University of Oxford
Department Name: Statistics


Antibodies are proteins produced by the human adaptive immune system. They specifically bind regions (epitopes) on other molecules (antigens) and elicit functions vital in the immune response. In addition to their endogenous roles, antibodies have great potential in medicine and represent a major class of therapeutics (Ecker et al, 2015). However, traditional processes for generating antibodies are time consuming and costly, usually requiring animal immunisation and extensive experimental screening (Fischman & Ofran, 2018). Computational approaches for antibody design can overcome these restrictions.
G protein-coupled receptors (GPCRs) have essential roles in human physiology and are therefore important targets for therapeutic modulation by antibody binding. These receptors exist in multiple conformational states (e.g. active and inactive) associated with different signalling functions. The rational design of antibodies that stabilise GPCRs in a particular conformation would have vast implications across medicine, as well as in basic research - for example, these antibodies could facilitate solving the structures of GPCRs in different states, contributing to the understanding of receptor function and structure-based drug discovery (De Groof et al, 2019).
Now is the right time to investigate this problem: there are structures available for more than 60 unique GPCRs (Pándy-Szekeres et al, 2018) and computational antibody design has advanced enormously, driven by research from Prof. Charlotte Deane's group, for example in improving structural modelling of antibody-antigen complexes (Dunbar et al, 2016; Marks et al, 2017; Leem et al, 2018).
This project involves the application of statistical and bioinformatics techniques to tackle a major problem in protein design. First, I will analyse GPCRs and antibodies at the level of both sequence and structure. Known GPCR-binding antibodies, and their preferences for receptor conformation, will be investigated. Novel state-specific epitopes will then be identified from structures of GPCRs in particular conformations using modern computational techniques. Finally, the new antibody-GPCR complexes will be computationally modelled (Leem et al, 2016) and the antibody binding affinity optimised in silico (Cannon et al, 2019). My proposed research will contribute to the development of robust computational methods that enable high-throughput prediction of suitable antibody sequences/structures. Additionally, if possible, the designed antibodies will be experimentally validated with a collaborator.

The project falls within the MRC research priority of Discovery Science - Precision medicine.


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