Macromolecular modeling and design in Rosetta: recent methods and frameworks.

The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at http://www.rosettacommons.org.

Utility of Covalent Labeling Mass Spectrometry Data in Protein Structure Prediction with Rosetta.

Covalent labeling mass spectrometry experiments are growing in popularity and provide important information regarding protein structure. Information obtained from these experiments correlates with residue solvent exposure within the protein in solution. However, it is impossible to determine protein structure from covalent labeling data alone. Incorporation of sparse covalent labeling data into the protein structure prediction software Rosetta has been shown to improve protein tertiary structure prediction. Here, covalent labeling techniques were analyzed computationally to provide insight into what labeling data is needed to optimize tertiary protein structure prediction in Rosetta. We have successfully implemented a new scoring functionality that provides improved predictions. We developed two new covalent labeling based score terms that use a "cone"-based neighbor count to quantify the relative solvent exposure of each amino acid. To test our method, we used a set of 20 proteins with structures deposited in the Protein Data Bank. Decoy model sets were generated for each of these 20 proteins, and the normalized covalent labeling score versus RMSD distributions were evaluated. On the basis of these distributions, we have determined an optimal subset of residues to use when performing covalent labeling experiments in order to maximize the structure prediction capabilities of the covalent labeling data. We also investigated how much false negative and false positive data can be tolerated without meaningfully impacting protein structure prediction. Using these new covalent labeling score terms, protein models were rescored and the resulting models improved by 3.9 Å RMSD on average. New models were also generated using Rosetta's AbinitioRelax program under the guidance of covalent labeling information, and improvement in model quality was observed.

Improving inverse docking target identification with Z-score selection.

The utilization of inverse docking methods for target identification has been driven by an increasing demand for efficient tools for detecting potential drug side-effects. Despite impressive achievements in the field of inverse docking, identifying true positives from a pool of potential targets still remains challenging. Notably, most of the developed techniques have low accuracies, limit the pool of possible targets that can be investigated or are not easy to use for non-experts due to a lack of available scripts or webserver. Guided by our finding that the absolute docking score was a poor indication of a ligand's protein target, we developed a novel "combined Z-score" method that used a weighted fraction of ligand and receptor-based Z-scores to identify the most likely binding target of a ligand. With our combined Z-score method, an additional 14%, 3.6%, and 6.3% of all ligand-protein pairs of the Astex, DUD, and DUD-E databases, respectively, were correctly predicted compared to a docking score-based selection. The combined Z-score had the highest area under the curve in a ROC curve analysis of all three datasets and the enrichment factor for the top 1% predictions using the combined Z-score analysis was the highest for the Astex and DUD-E datasets. Additionally, we developed a user-friendly python script (compatible with both Python2 and Python3) that enables users to employ the combined Z-score analysis for target identification using a user-defined list of ligands and targets. We are providing this python script and a user tutorial as part of the supplemental information.

Rosetta Protein Structure Prediction from Hydroxyl Radical Protein Footprinting Mass Spectrometry Data.

In recent years mass spectrometry-based covalent labeling techniques such as hydroxyl radical footprinting (HRF) have emerged as valuable structural biology techniques, yielding information on protein tertiary structure. These data, however, are not sufficient to predict protein structure unambiguously, as they provide information only on the relative solvent exposure of certain residues. Despite some recent advances, no software currently exists that can utilize covalent labeling mass spectrometry data to predict protein tertiary structure. We have developed the first such tool, which incorporates mass spectrometry derived protection factors from HRF labeling as a new centroid score term for the Rosetta scoring function to improve the prediction of protein tertiary structures. We tested our method on a set of four soluble benchmark proteins with known crystal structures and either published HRF experimental results or internally acquired data. Using the HRF labeling data, we rescored large decoy sets of structures predicted with Rosetta for each of the four benchmark proteins. As a result, the model quality improved for all benchmark proteins as compared to when scored with Rosetta alone. For two of the four proteins we were even able to identify atomic resolution models with the addition of HRF data.

Successful Identification of Cardiac Troponin Calcium Sensitizers Using a Combination of Virtual Screening and ROC Analysis of Known Troponin C Binders.

Calcium-dependent cardiac muscle contraction is regulated by the protein complex troponin. Calcium binds to the N-terminal domain of troponin C (cNTnC) which initiates the process of contraction. Heart failure is a consequence of a disruption of this process. With the prevalence of this condition, a strong need exists to find novel compounds to increase the calcium sensitivity of cNTnC. Desirable are small chemical molecules that bind to the interface between cTnC and the cTnI switch peptide and exhibit calcium sensitizing properties by possibly stabilizing cTnC in an open conformation. To identify novel drug candidates, we employed a structure-based drug discovery protocol that incorporated the use of a relaxed complex scheme (RCS). In preparation for the virtual screening, cNTnC conformations were identified based on their ability to correctly predict known cNTnC binders using a receiver operating characteristics analysis. Following a virtual screen of the National Cancer Institute's Developmental Therapeutic Program database, a small number of molecules were experimentally tested using stopped-flow kinetics and steady-state fluorescence titrations. We identified two novel compounds, 3-(4-methoxyphenyl)-6,7-chromanediol (NSC600285) and 3-(4-methylphenyl)-7,8-chromanediol (NSC611817), that show increased calcium sensitivity of cTnC in the presence of the regulatory domain of cTnI. The effects of NSC600285 and NSC611817 on the calcium dissociation rate was stronger than that of the known calcium sensitizer bepridil. Thus, we identified a 3-phenylchromane group as a possible key pharmacophore in the sensitization of cardiac muscle contraction. Building on this finding is of interest to researchers working on development of drugs for calcium sensitization.