Flexibility-aware graph model for accurate epitope identification

Abstract

Structural flexibility is an intrinsic characteristics of a protein upon interacting with other molecules, which mainly comes from the movement of a residue’s side chain, backbone and even an entire domain. Considering this property can be very helpful in protein binding analysis, such as epitope identification during antibody–antigen interaction. Unfortunately, to our knowledge, no approach is available at studying the dynamicity of protein binding from the computational perspective. We are pioneering a new perspective of exploring protein binding sites with considering the structural flexibility, particularly from the epitopes identification angle in antibody–antigen binding. To this end, we first obtained protein antigen structures with epitopes available, and built residue-level graphs of antigens. These graphs were highly densified subsequently by incorporating the structural flexibility. Later, the edge enriched graphs were clustered into overlapping subgraphs and were classified as epitope or non-epitope by a graph convolutional network. Experiments on epitope identification shown that the proposed flexibility-aware model markedly outperformed existing approaches by lifting the F1-score to 0.656, making a remarkable increment of 16.3% compared to the state-of-the-art. A quick study on generic protein binding site prediction also made a noteworthy improvement with increasing the F1-score by 8%. The superior performance obtained from both the specific and generic protein interaction analysis demonstrate that incorporating flexibility in computational models is helpful to strength the capability of identifying epitopes as well as general protein binding sites. This seminal study can be inspiring and promising to the wide range of protein interaction analysis.