We describe an improved method for comparative modeling, RosettaCM, which optimizes a physically realistic all-atom energy function over the conformational space defined by homologous structures. Given a set of sequence alignments, RosettaCM assembles topologies by recombining aligned segments in Cartesian-space and building unaligned regions de novo in torsion space. The junctions between segments are regularized using a loop-closure method combining fragment superposition with gradient-based minimization. The energies of the resulting models are optimized by all-atom refinement, and the most representative low energy model is selected. The CASP10 experiment suggests RosettaCM yields models with more accurate sidechain and backbone conformations than other methods when the sequence identity to the templates is greater than ∼15%.

A correct alignment is an essential requirement in homology modeling. Yet in order to bridge the structural gap between template and target, which may not only involve loop rearrangements, but also shifts of secondary structure elements and repacking of core residues, high‐resolution refinement methods with full atomic details are needed. Here, we describe four approaches that address this “last mile of the protein folding problem” and have performed well during CASP8, yielding physically realistic models: YASARA, which runs molecular dynamics simulations of models in explicit solvent, using a new partly knowledge‐based all atom force field derived from Amber, whose parameters have been optimized to minimize the damage done to protein crystal structures. The LEE‐SERVER, which makes extensive use of conformational space annealing to create alignments, to help Modeller build physically realistic models while satisfying input restraints from templates and CHARMM stereochemistry, and to remodel the side‐chains. ROSETTA, whose high resolution refinement protocol combines a physically realistic all atom force field with Monte Carlo minimization to allow the large conformational space to be sampled quickly. And finally UNDERTAKER, which creates a pool of candidate models from various templates and then optimizes them with an adaptive genetic algorithm, using a primarily empirical cost function that does not include bond angle, bond length, or other physics‐like terms. Proteins 2009. © 2009 Wiley‐Liss, Inc.

We have recently completed a full re-architecturing of the Rosetta molecular modeling program, generalizing and expanding its existing functionality. The new architecture enables the rapid prototyping of novel protocols by providing easy to use interfaces to powerful tools for molecular modeling. The source code of this rearchitecturing has been released as Rosetta3 and is freely available for academic use. At the time of its release, it contained 470,000 lines of code. Counting currently unpublished protocols at the time of this writing, the source includes 1,285,000 lines. Its rapid growth is a testament to its ease of use. This document describes the requirements for our new architecture, justifies the design decisions, sketches out central classes, and highlights a few of the common tasks that the new software can perform.

We describe predictions made using the Rosetta structure prediction methodology for the Eighth Critical Assessment of Techniques for Protein Structure Prediction. Aggressive sampling and all-atom refinement were carried out for nearly all targets. A combination of alignment methodologies was used to generate starting models from a range of templates, and the models were then subjected to Rosetta all atom refinement. For 50 targets with readily identified templates, the best submitted model was better than the best alignment to the best template in the Protein Data Bank for 24 domains, and improved over the best starting model for 43 domains. For 13 targets where only very distant sequence relationships to proteins of known structure were detected, models were generated using the Rosetta de novo structure prediction methodology followed by all-atom refinement; in several cases the submitted models were better than those based on the available templates. Of the 12 refinement challenges, the best submitted model improved on the starting model in 7 cases. These improvements over the starting template-based models and refinement tests demonstrate the power of Rosetta structure refinement in improving model accuracy.

Accurate energy functions are critical to macromolecular modeling and design. We describe new tools for identifying inaccuracies in energy functions and guiding their improvement, and illustrate the application of these tools to improvement of the Rosetta energy function. The feature analysis tool identifies discrepancies between structures deposited in the PDB and low energy structures generated by Rosetta; these likely arise from inaccuracies in the energy function. The optE tool optimizes the weights on the different components of the energy function by maximizing the recapitulation of a wide range of experimental observations. We use the tools to examine three proposed modifications to the Rosetta energy function: improving the unfolded state energy model (reference energies), using bicubic spline interpolation to generate knowledge based torisonal potentials, and incorporating the recently developed Dunbrack 2010 rotamer library (Shapovalov and Dunbrack, 2011).

Following the failure of a wide range of attempts to solve the crystal structure of M-PMV retroviral protease by molecular replacement, we challenged players of the protein folding game Foldit to produce accurate models of the protein. Remarkably, Foldit players were able to generate models of sufficient quality for successful molecular replacement and subsequent structure determination. The refined structure provides new insights for the design of antiretroviral drugs.

We describe predictions made using the Rosetta structure prediction methodology for both template-based modeling and free modeling categories in the Seventh Critical Assessment of Techniques for Protein Structure Prediction. For the first time, aggressive sampling and all-atom refinement could be carried out for the majority of targets, an advance enabled by the Rosetta@home distributed computing network. Template-based modeling predictions using an iterative refinement algorithm improved over the best existing templates for the majority of proteins with less than 200 residues. Free modeling methods gave near-atomic accuracy predictions for several targets under 100 residues from all secondary structure classes. These results indicate that refinement with an all-atom energy function, although computationally expensive, is a powerful method for obtaining accurate structure predictions.

ith more than 53 million confirmed cases of COVID-19 and 1.3 million associated deaths worldwide (World Health Organization, 13 November 2020), there is an urgent need to understand the molecular mechanism of infection and disease to identify patients' susceptibilities and targets for therapeutic intervention. ACE2 is the main viral entry point for coronavirus N63, SARS-CoV and SARS-CoV-2, which cause severe acute respiratory syndromes, the last being responsible for COVID-19 in humans 1-4. ACE2 binds to the S1 domain of trimeric SARS-CoV spike (S) glycoprotein 1 and SARS-CoV-2 S protein 5 , which is primed by TMPRSS2 (ref. 6). Cellular entry of SARS-CoV is dependent on the extracellular domain of ACE2 being cleaved by TMPRSS2 protease at Arg 697 and Lys 716, and the transmembrane domain of ACE2 internalized with the virus 7-9. ACE2 is a carboxypeptidase with several known physiological functions including regulation of blood pressure, salt and water balance in mammals 10,11 , amino acid uptake in the small intestine 12,13 , and glucose homeostasis and pancreatic β-cell function 14,15. Interestingly, ACE2 has been suggested to play an important role in protection from acute lung injury 16-19. ACE2 expression in different tissues is controlled by multiple promoter elements 20-22. In human nasal epithelia and lung tissue, ACE2 expression has been reported to be interferon (IFN) regulated, with evidence of STAT1-, STAT3-, IRF8-and IRF1-binding sites within the ACE2 promoter 23. Activation of IFN-responsive genes is an important antiviral defense pathway in humans, and both interferon and influenza exposure have been reported to increase ACE2 expression in the human airway 23. Bulk and single-cell RNA-sequencing (scRNA-seq) data 24 detect low-level expression of ACE2 in multiple tissues 25. ACE2 expression in the airways is relatively high in nasal epithelium and progressively lower in the bronchial and alveolar regions; this expression profile correlates with levels of infection of SARS-CoV-2 isolates from patients in different airway compartments 26. Consistently, SARS-CoV-2 viral loads have been found to be higher in swabs taken from the nose than swabs taken from the throat of COVID-19 patients 27. Highest ACE2 expression is seen in goblet and ciliated cells of the nasal epithelium 25 , and ACE2 protein localizes to the membrane of motile cilia of respiratory tract epithelia 28. Consistent with this, SARS-CoV-2 has been detected in situ in ciliated airway cells and upper airway epithelium, in addition to pulmonary A novel ACE2 isoform is expressed in human respiratory epithelia and is upregulated in response to interferons and RNA respiratory virus infection