Flexible Backbone Assembly and Refinement of Symmetrical Homomeric Complexes

Symmetrical homomeric proteins are ubiquitous in every domain of life, and information about their structure is essential to decipher function. The size of these complexes often makes them intractable to high-resolution structure determination experiments. Computational docking algorithms offer a promising alternative for modeling large complexes with arbitrary symmetry. Accuracy of existing algorithms, however, is limited by backbone inaccuracies when using homology-modeled monomers. Here, we present Rosetta SymDock2 with a broad search of symmetrical conformational space using a six-dimensional coarse-grained score function followed by an all-atom flexible-backbone refinement, which we demonstrate to be essential for physically realistic modeling of tightly packed complexes. In global docking of a benchmark set of complexes of different point symmetries—starting from homology-modeled monomers—we successfully dock (defined as predicting three near-native structures in the five top-scoring models) 17 out of 31 cyclic complexes and 3 out of 12 dihedral complexes. [Display omitted] •SymDock2 is an algorithm to assemble symmetric protein structures from monomers•Coarse-grained score function discriminates near-native conformations•Flexible-backbone refinement is necessary to create realistic all-atom models•Results improve 5-fold and outperform other symmetric docking algorithms Roy Burman et al. present Rosetta SymDock2, a computational method to predict symmetric protein assemblies. The method incorporates backbone flexibility, which is demonstrated to be essential for generating physically realistic models. From an approximate starting monomer, Rosetta SymDock2 accurately predicts structures for complexes with various symmetries.