CSM-AB: graph-based antibody–antigen binding affinity prediction and docking scoring function

• DOI: 10.1093/bioinformatics/btab762
• Publication Date: 2021-11-01T20:13:13Z
• Authors: Yoochan Myung; Douglas E V Pires; David B Ascher
• Keywords: Machine Learning; Molecular Docking Simulation; 0303 health sciences; 03 medical and health sciences; Antigens; Software; Antibodies; Protein Binding; 3. Good health

AbstractMotivationUnderstanding antibody–antigen interactions is key to improving their binding affinities and specificities. While experimental approaches are fundamental for developing new therapeutics, computational methods can provide quick assessment of binding landscapes, guiding experimental design. Despite this, little effort has been devoted to accurately predicting the binding affinity between antibodies and antigens and to develop tailored docking scoring functions for this type of interaction. Here, we developed CSM-AB, a machine learning method capable of predicting antibody–antigen binding affinity by modelling interaction interfaces as graph-based signatures.ResultsCSM-AB outperformed alternative methods achieving a Pearson's correlation of up to 0.64 on blind tests. We also show CSM-AB can accurately rank near-native poses, working effectively as a docking scoring function. We believe CSM-AB will be an invaluable tool to assist in the development of new immunotherapies.Availability and implementationCSM-AB is freely available as a user-friendly web interface and API at http://biosig.unimelb.edu.au/csm_ab/datasets.Supplementary informationSupplementary data are available at Bioinformatics online.