ColabDock: inverting AlphaFold structure prediction model for protein-protein docking with experimental restraints

Abstract Prediction of protein complex structures and interfaces potentially has wide applications and can benefit the study of biological mechanisms involving protein-protein interactions. However, the surface prediction accuracy of traditional docking methods and AlphaFold-Multimer is limited. Here we present ColabDock, a framework that makes use of ColabDesign, but reimplements it for the purpose of restrained complex conformation prediction. With a generation-prediction architecture and trained ranking model, ColabDock outperforms HADDOCK and ClusPro not only in complex structure predictions with simulated residue and surface restraints, but also in those assisted by NMR chemical shift perturbation as well as covalent labeling. It further assists antibody-antigen interface prediction with emulated interface scan restraints, which could be obtained by experiments such as Deep Mutation Scan. ColabDock provides a general approach to integrate sparse interface restraints of different experimental forms and sources into one optimization framework.