J

Jeffrey J. Gray

Johns Hopkins University

ORCID: 0000-0001-6380-2324

Publishes on Protein Structure and Dynamics, Monoclonal and Polyclonal Antibodies Research, Enzyme Structure and Function. 377 papers and 21.2k citations.

377Publications
21.2kTotal Citations
#4in Antibody Discovery

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Top publicationsby citations

The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design
Rebecca F. Alford, Andrew Leaver‐Fay, Jeliazko R. Jeliazkov et al.|Journal of Chemical Theory and Computation|2017
Cited by 1.6k

Over the past decade, the Rosetta biomolecular modeling suite has informed diverse biological questions and engineering challenges ranging from interpretation of low-resolution structural data to design of nanomaterials, protein therapeutics, and vaccines. Central to Rosetta's success is the energy function: a model parametrized from small-molecule and X-ray crystal structure data used to approximate the energy associated with each biomolecule conformation. This paper describes the mathematical models and physical concepts that underlie the latest Rosetta energy function, called the Rosetta Energy Function 2015 (REF15). Applying these concepts, we explain how to use Rosetta energies to identify and analyze the features of biomolecular models. Finally, we discuss the latest advances in the energy function that extend its capabilities from soluble proteins to also include membrane proteins, peptides containing noncanonical amino acids, small molecules, carbohydrates, nucleic acids, and other macromolecules.

PyRosetta: a script-based interface for implementing molecular modeling algorithms using Rosetta
Cited by 899Open Access

SUMMARY: PyRosetta is a stand-alone Python-based implementation of the Rosetta molecular modeling package that allows users to write custom structure prediction and design algorithms using the major Rosetta sampling and scoring functions. PyRosetta contains Python bindings to libraries that define Rosetta functions including those for accessing and manipulating protein structure, calculating energies and running Monte Carlo-based simulations. PyRosetta can be used in two ways: (i) interactively, using iPython and (ii) script-based, using Python scripting. Interactive mode contains a number of help features and is ideal for beginners while script-mode is best suited for algorithm development. PyRosetta has similar computational performance to Rosetta, can be easily scaled up for cluster applications and has been implemented for algorithms demonstrating protein docking, protein folding, loop modeling and design. AVAILABILITY: PyRosetta is a stand-alone package available at http://www.pyrosetta.org under the Rosetta license which is free for academic and non-profit users. A tutorial, user's manual and sample scripts demonstrating usage are also available on the web site.

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