Physics-informed deep neural network for rigid-body protein docking

Proteins are biological macromolecules that perform many essential roles within all living organisms. Many protein functions arise from physical interactions between them and also with other biomolecules (e.g. DNA, metabolites). Being able to predict whether and how two proteins interact is an important problem in fundamental biological research and translational drug discovery.In this work, we present an energy-based model for generating ensembles of rigid-body transformations to predict the configuration of protein complexes. The method incorporates strong, interpretable physical priors, it is by construction $\text{SE}(3)$ equivariant and fully-differentiable back to the atomic structure.We rely on the observation that bound protein-protein complexes show high geometric and chemical complementarity at the interface of the two proteins. Our method efficiently makes use of this prior by generating on-the-fly point cloud representations of the solvent-excluded surfaces of the proteins. Through learned point descriptors, we can infer regions of high complementarity between the two proteins and compute a proxy for the binding energy. By sampling transformations expected to adopt low energy states, we generate ensembles of bound poses where the two protein surfaces are brought into contact.We expect that the strong physical priors enforced by the network construction will aid in generalization to other related tasks and lead to a richer human understanding of the process behind the generation and scoring of the docked poses.The fact that the method is also fully differentiable allows for gradient-based modifications of the atomic structure which could be critical in tasks related to unbound docking or protein design which remain outstanding problems in protein modelling.