AlphaFold2-based structure prediction and target study of PD-L1 protein

Abstract

PD-L1 is an immune protein in human body that can play an important role in cancer immunotherapy. By binding to antibodies, the binding activity of PD-L1 and PD-1 is blocked, which in turn inhibits cancer cells. Thus the structure of PD-L1 is very important in studying the binding of antibodies to it. However, experimental methods to solve the structures of PD- L1 and numerous complexes are expensive and consuming. Thus, it is essential to exploit computational methods to help biologists figure out the structures and the underlying mechanisms. In this paper, we explore whether AlphaFold2 is able to accurately predict the structure of PD-L1 and whether we can use AlphaFold2 to capture the binding sites of PD-L1 when binding to different antibodies. Our results show that AlphaFold2 has high confident scores and accuracy in predicting the structure of PD-L1 and the binding sites with atezolizumab and durvalumab. For the interaction between PD-L1 and the antibodies, AlphaFold2 can capture most of the hydrogen bonds as well as the salt bridges. Our work suggests that AlphaFold2 can not only be used as a tool to predict the structure of proteins, but also serves as a useful tool for antibody discovery, e.g. providing high-quality predicted structures for downstreaming docking, which brings new hope for drug discovery.