ProteinDock: A physics-informed layer to improve protein-protein docking reliability
TL;DR
It is demonstrated that a truncated version of ProteinDock can be used to choose the optimal prediction among outputs from multiple deep learning-based tools, and shown that this strategy is a computationally efficient alternative to increasing the seed quantity for deep-learning predictions.
Abstract
Computational modeling provides geometric insight into protein-protein interactions without requiring the resources of experimentation. However, reliability can be hindered when modeling proteins with distinctive features, such as antibodies, that use flexible, polar-rich loops to bind antigens. We developed ProteinDock, a physics-based tool that can be used in combination with leading modeling programs to improve the reliability of protein-protein docking; this work provides a case study of antibody-antigen interfaces. ProteinDock was layered onto Rosetta for docking unbound experimentally determined structures, and when evaluated on Docking Benchmark Set 5.5, generated CAPRI acceptable-quality or better for 80.2% of targets, an improvement of 32.8 percentage points over vanilla Rosetta’s 47.4% on the same dataset. To improve protein-protein prediction reliability from sequence inputs, we demonstrate that a truncated version of ProteinDock can be used to choose the optimal prediction among outputs from multiple deep learning-based tools. We show that this strategy is a computationally efficient alternative to increasing the seed quantity for deep-learning predictions. A graphical user interface for layering ProteinDock has been created and is available at https://github.com/Kimmel-Lab/proteindock and https://proteindock.com/. TOC Figure