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.

The rise in the number of protein sequences in the post-genomic era has led to a major breakthrough in fitting generative sequence models for contact prediction, protein design, alignment, and homology search. Despite this success, the interpretability of the modeled pairwise parameters continues to be limited due to the entanglement of coevolution, phylogeny, and entropy. For contact prediction, post-correction methods have been developed to remove the contribution of entropy from the predicted contact maps. However, all remaining applications that rely on the raw parameters, lack a direct method to correct for entropy. In this paper, we investigate the origins of the entropy signal and propose a new spectral regularizer to down weight it during model fitting. We find the added regularizer to GREMLIN, a Markov Random Field or Potts model, allows for the inference of a sparse contact map without loss in precision, meanwhile improving interpretability, and resolving overfitting issues important for sequence evaluation and design.