The Rosetta software suite for macromolecular modeling, docking, and design is widely used in pharmaceutical, industrial, academic, non-profit, and government laboratories. Despite its broad modeling capabilities, Rosetta remains consistently among leading software suites when compared to other methods created for highly specialized protein modeling and design tasks. Developed for over two decades by a global community of over 60 laboratories, Rosetta has undergone multiple refactorings, and now comprises over three million lines of code. Here we discuss methods developed in the last five years in Rosetta, involving the latest protocols for structure prediction; protein-protein and protein-small molecule docking; protein structure and interface design; loop modeling; the incorporation of various types of experimental data; modeling of peptides, antibodies and proteins in the immune system, nucleic acids, non-standard chemistries, carbohydrates, and membrane proteins. We briefly discuss improvements to the energy function, user interfaces, and usability of the software. Rosetta is available at www.rosettacommons.org.

Summary Bacteria use rapid contraction of a long sheath of the Type VI secretion system (T6SS) to deliver effectors into a target cell. Here we present an atomic resolution structure of a native contracted Vibrio cholerae sheath determined by cryo-electron microscopy. The sheath subunits, composed of tightly interacting proteins VipA and VipB, assemble into a six-start helix. The helix is stabilized by a core domain assembled from four β-strands donated by one VipA and two VipB molecules. The fold of inner and middle layers is conserved between T6SS and phage sheaths. However, the structure of the outer layer is distinct and suggests a mechanism of interaction of the bacterial sheath with an accessory ATPase, ClpV, that facilitates multiple rounds of effector delivery. Our results provide a mechanistic insight into assembly of contractile nanomachines that bacteria and phages use to translocate macromolecules across membranes.

Summary Mitochondrial inheritance, genome maintenance, and metabolic adaptation depend on organelle fission by Dynamin-Related Protein 1 (DRP1) and its mitochondrial receptors. DRP1 receptors include the paralogs Mitochondrial Dynamics 49 and 51 (MID49/MID51) and Mitochondrial Fission Factor (MFF), but the mechanisms by which these proteins recruit and regulate DRP1 are unknown. Here we present a cryoEM structure of human, full-length DRP1 coassembled with MID49 and an analysis of structure- and disease-based mutations. We report that GTP induces a remarkable elongation and rotation of the G-domain, Bundle-Signaling Element (BSE) and connecting hinge loops of DRP1. In this conformation, a network of multivalent interactions promotes polymerization of a linear DRP1 filament with MID49/MID51. Following coassembly, GTP hydrolysis and exchange lead to MID receptor dissociation, filament shortening and curling of DRP1 oligomers into constricted and closed rings. Together, these views of full-length, receptor- and nucleotide-bound conformations reveal how DRP1 performs mechanical work through nucleotide-driven allostery.

We present a de novo model building approach that combines predicted backbone conformations with side-chain density-fit to accurately assign sequence into density maps. We show this method yields accurate models for six experimental maps at 3.3–4.8 Å resolution, and produces a nearly complete model for an unsolved map containing a 660-residue hetero-dimeric protein. This method should enable rapid and reliable protein structure determination from near-atomic resolution cryo-EM maps.

We describe CASP11 de novo blind structure predictions made using the Rosetta structure prediction methodology with both automatic and human assisted protocols. Model accuracy was generally improved using coevolution derived residue–residue contact information as restraints during Rosetta conformational sampling and refinement, particularly when the number of sequences in the family was more than three times the length of the protein. The highlight was the human assisted prediction of T0806, a large and topologically complex target with no homologs of known structure, which had unprecedented accuracy—<3.0 Å root-mean-square deviation (RMSD) from the crystal structure over 223 residues. For this target, we increased the amount of conformational sampling over our fully automated method by employing an iterative hybridization protocol. Our results clearly demonstrate, in a blind prediction scenario, that coevolution derived contacts can considerably increase the accuracy of template-free structure modeling.

Protein structure prediction methods such as Rosetta search for the lowest energy conformation of the polypeptide chain. However, the experimentally observed native state is at a minimum of the free energy, rather than the energy. The neglect of the missing configurational entropy contribution to the free energy can be partially justified by the assumption that the entropies of alternative folded states, while very much less than unfolded states, are not too different from one another, and hence can be to a first approximation neglected when searching for the lowest free energy state. The shortcomings of current structure prediction methods may be due in part to the breakdown of this assumption. Particularly problematic are proteins with significant disordered regions which do not populate single low energy conformations even in the native state. We describe two approaches within the Rosetta structure modeling methodology for treating such regions. The first does not require advance knowledge of the regions likely to be disordered; instead these are identified by minimizing a simple free energy function used previously to model protein folding landscapes and transition states. In this model, residues can be either completely ordered or completely disordered; they are considered disordered if the gain in entropy outweighs the loss of favorable energetic interactions with the rest of the protein chain. The second approach requires identification in advance of the disordered regions either from sequence alone using for example the DISOPRED server or from experimental data such as NMR chemical shifts. During Rosetta structure prediction calculations the disordered regions make only unfavorable repulsive contributions to the total energy. We find that the second approach has greater practical utility and illustrate this with examples from de novo structure prediction, NMR structure calculation, and comparative modeling.