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.

Active sites and ligand binding cavities in native proteins are often formed by curved β-sheets, and the ability to control β-sheet curvature would allow design of binding proteins with cavities customized to specific ligands. Towards this end, we investigated the mechanisms controlling β-sheet curvature by studying the geometry of β-sheets in naturally occurring protein structures and * Correspondence to: dabaker@u.washington.edu. † These authors contributed equally to this work. Supplementary Materials: Materials and MethodsFigs. S1 to S22 Tables S1 to S7 Input files and command lines for design calculations HHS Public Access Author Manuscript Author ManuscriptAuthor ManuscriptAuthor Manuscript folding simulations. The principles emerging from this analysis were used to de novo design a series of proteins with curved β-sheets topped with a-helices. NMR and crystal structures of the designs closely match the computational models, showing that β-sheet curvature can be controlled with atomic-level accuracy. Our approach enables the design of proteins with cavities and provides a route to custom design ligand binding and catalytic sites.Ligand binding proteins with curved β-sheets surrounding the binding pocket, as in the NTF2-like, β-barrel, and jelly roll folds, play key roles in molecular recognition, metabolic pathways and cell signaling. Approaches to designing small molecule binding proteins and enzymes to date have started by searching for native protein scaffolds with ligand binding pockets with roughly the right geometry, and then redesigning the surrounding residues to optimize interactions with the small molecule. While this approach has yielded new binding proteins and catalysts (1-5), it is not optimal: there may be no naturally occurring scaffold with a pocket with the correct geometry, and introduction of mutations in the design process may change the pocket structure (6, 7). Building de novo proteins with custom-tailored binding sites could be a more effective strategy, but this remains an outstanding challenge (8-11). De novo protein design has recently focused on proteins with ideal backbone structures (12-16) (straight helices, uniform β-strands and short loops; see ref (17) for a recent exception) and optimal core sidechain packing, but the binding pockets of naturally occurring proteins lie on concave surfaces formed by non-ideal features such as kinked helices, curved β-sheets or long loops. The design of proteins with concave surfaces requires examination of how such irregular structural features can be programmed into the amino acid sequence.We begin by analyzing how classic (18, 19) β-bulges (irregularities in the pleating of edge strands) and register shifts (local termination of strand pairing) coupled with intrinsic β-strand geometry induce curvature in antiparallel β-sheets (20, 21). We quantify the curvature of an edge strand making an antiparallel pairing with a second strand by the bend angle (Fig. 1A). The absolute value of the bend angle (α) at residue i is the angle bet...

Small heat shock proteins (sHSPs) are nature’s ‘first responders’ to cellular stress, interacting with affected proteins to prevent their aggregation. Little is known about sHSP structure beyond its structured α-crystallin domain (ACD), which is flanked by disordered regions. In the human sHSP HSPB1, the disordered N-terminal region (NTR) represents nearly 50% of the sequence. Here, we present a hybrid approach involving NMR, hydrogen-deuterium exchange mass spectrometry, and modeling to provide the first residue-level characterization of the NTR. The results support a model in which multiple grooves on the ACD interact with specific NTR regions, creating an ensemble of ‘quasi-ordered’ NTR states that can give rise to the known heterogeneity and plasticity of HSPB1. Phosphorylation-dependent interactions inform a mechanism by which HSPB1 is activated under stress conditions. Additionally, we examine the effects of disease-associated NTR mutations on HSPB1 structure and dynamics, leveraging our emerging structural insights.

To create new enzymes and biosensors from scratch, precise control over the structure of small-molecule binding sites is of paramount importance, but systematically designing arbitrary protein pocket shapes and sizes remains an outstanding challenge. Using the NTF2-like structural superfamily as a model system, we developed an enumerative algorithm for creating a virtually unlimited number of de novo proteins supporting diverse pocket structures. The enumerative algorithm was tested and refined through feedback from two rounds of large-scale experimental testing, involving in total the assembly of synthetic genes encoding 7,896 designs and assessment of their stability on yeast cell surface, detailed biophysical characterization of 64 designs, and crystal structures of 5 designs. The refined algorithm generates proteins that remain folded at high temperatures and exhibit more pocket diversity than naturally occurring NTF2-like proteins. We expect this approach to transform the design of small-molecule sensors and enzymes by enabling the creation of binding and active site geometries much more optimal for specific design challenges than is accessible by repurposing the limited number of naturally occurring NTF2-like proteins.