Integrated structure prediction of protein–protein docking with experimental restraints using ColabDock

Abstract

Protein complex structure prediction plays important roles in various applications, such as drug discovery and antibody design. However, due to limited prediction accuracy, there are frequent inconsistencies between the predictions and the experiments. Here we present ColabDock, a general framework adapting deep learning structure prediction models to integrate experimental restraints of different forms and sources without further large-scale retraining or fine tuning. With a generation–prediction architecture and trained ranking model, ColabDock outperforms HADDOCK and ClusPro using AlphaFold2 as the structure prediction model, not only in complex structure predictions with simulated residue and surface restraints but also in those assisted by nuclear magnetic resonance chemical shift perturbation as well as covalent labelling. It also assists antibody–antigen interface prediction with emulated interface scan restraints, which could be obtained by experiments such as deep mutational scanning. As a unified framework, we hope that ColabDock can help to bridge the gap between experimental and computational protein science. Despite rapid developments in predicting the complex structures of proteins, there are still inconsistencies between predictions and experiments. Feng et al. developed ColabDock, a general framework for deep learning models that integrates various experimental restraints and improves complex interface prediction, including antibody–antigen interactions.