Rosetta is molecular modeling software that has been developed for the prediction and design of macromolecular structure. Rosetta has performed well in community wide assessments of protein structure prediction and docking, and it has been used to design new protein structures as well as design altered specificity protein-protein and protein-DNA interactions. Rosetta is developed and maintained by research groups at 11 separate universities, and over 2400 laboratories have obtained free licenses for the software. Until 2004, Rosetta was written in Fortran 77. Over the past three years we have used NIH support to create a new C++ object-oriented version of Rosetta. The new software is organized as a set of libraries that contain classes and routines for representing and scoring molecular systems, for holding move sets, and for performing optimization of macromolecular conformation and sequence. The primary goal of this proposal is to capitalize on this rewrite, and extend the Rosetta software in directions that were less feasible when the code was not modular and object-oriented. Currently, Rosetta runs through a command line interface that forces users to choose from a fixed set of modeling protocols, such as protein-protein docking or the design of protein monomers. We will create a framework within Rosetta that allows users to easily create custom protocols in either C++ or with the scripting language Python. The python binding will be used to create an interface with PyMOL, a widely used program for molecular visualization. This will provide a graphical interface for initiating and interacting with Rosetta simulations as well as rapidly evaluating the quality of Rosetta models. Most applications of Rosetta benefit significantly from increased sampling of conformational and/or sequence space, and therefore, benefit from faster algorithms. We will increase the speed of Rosetta calculations by taking advantage of new C++ objects for caching energies. This grant will also support developers and users by maintaining benchmarks that test the integrity of the code, maintaining the user's guide and supporting meetings between developers at the various institutions.

Public Health Relevance

The function of a protein, RNA or DNA molecule is largely determined by its 3- dimensional structure.
We aim to develop a state-of-the-art computer program for predicting and designing the structures of biological macromolecules. The program will be freely available to academic laboratories and its predictions will help investigators understand and fight human diseases such as cancer and AIDS.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Special Emphasis Panel (ZRG1-BST-Q (01))
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Friedman, Fred K
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University of North Carolina Chapel Hill
Schools of Medicine
Chapel Hill
United States
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Li, Bian; Mendenhall, Jeffrey; Nguyen, Elizabeth Dong et al. (2017) Improving prediction of helix-helix packing in membrane proteins using predicted contact numbers as restraints. Proteins 85:1212-1221
Weitzner, Brian D; Gray, Jeffrey J (2017) Accurate Structure Prediction of CDR H3 Loops Enabled by a Novel Structure-Based C-Terminal Constraint. J Immunol 198:505-515
Alford, Rebecca F; Leaver-Fay, Andrew; Jeliazkov, Jeliazko R et al. (2017) The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design. J Chem Theory Comput 13:3031-3048
Weitzner, Brian D; Jeliazkov, Jeliazko R; Lyskov, Sergey et al. (2017) Modeling and docking of antibody structures with Rosetta. Nat Protoc 12:401-416
Leffler, Abba E; Kuryatov, Alexander; Zebroski, Henry A et al. (2017) Discovery of peptide ligands through docking and virtual screening at nicotinic acetylcholine receptor homology models. Proc Natl Acad Sci U S A 114:E8100-E8109
Gajewski, Stefan; Waddell, Michael Brett; Vaithiyalingam, Sivaraja et al. (2016) Structure and mechanism of the phage T4 recombination mediator protein UvsY. Proc Natl Acad Sci U S A 113:3275-80
Sliwoski, Gregory; Mendenhall, Jeffrey; Meiler, Jens (2016) Autocorrelation descriptor improvements for QSAR: 2DA_Sign and 3DA_Sign. J Comput Aided Mol Des 30:209-17
Li, Bian; Mendenhall, Jeffrey; Nguyen, Elizabeth Dong et al. (2016) Accurate Prediction of Contact Numbers for Multi-Spanning Helical Membrane Proteins. J Chem Inf Model 56:423-34
Bornhop, Darryl J; Kammer, Michael N; Kussrow, Amanda et al. (2016) Origin and prediction of free-solution interaction studies performed label-free. Proc Natl Acad Sci U S A 113:E1595-604
Moretti, Rocco; Bender, Brian J; Allison, Brittany et al. (2016) Rosetta and the Design of Ligand Binding Sites. Methods Mol Biol 1414:47-62

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