The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design

Rebecca F. Alford, Andrew Leaver-Fay, Jeliazko R. Jeliazkov, Matthew J. O'Meara, Frank P. DiMaio, Hahnbeom Park, Maxim V. Shapovalov, P. Douglas Renfrew, Vikram K. Mulligan, Kalli Kappel, Jason W. Labonte, Michael S. Pacella, Richard Bonneau, Philip Bradley, Roland L. Dunbrack, Rhiju Das, David Baker, Brian Kuhlman, Tanja Kortemme, Jeffrey J Gray

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Pages (from-to)3031-3048
Number of pages18
JournalJournal of Chemical Theory and Computation
Volume13
Issue number6
DOIs
StatePublished - Jun 13 2017

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Atoms
Proteins
atoms
proteins
Molecules
Vaccines
energy
Nucleic acids
Biomolecules
Carbohydrates
Macromolecules
Nanostructured materials
Nucleic Acids
Peptides
vaccines
Conformations
Amino acids
Membrane Proteins
Crystal structure
carbohydrates

ASJC Scopus subject areas

  • Computer Science Applications
  • Physical and Theoretical Chemistry

Cite this

Alford, R. F., Leaver-Fay, A., Jeliazkov, J. R., O'Meara, M. J., DiMaio, F. P., Park, H., ... Gray, J. J. (2017). The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design. Journal of Chemical Theory and Computation, 13(6), 3031-3048. https://doi.org/10.1021/acs.jctc.7b00125

The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design. / Alford, Rebecca F.; Leaver-Fay, Andrew; Jeliazkov, Jeliazko R.; O'Meara, Matthew J.; DiMaio, Frank P.; Park, Hahnbeom; Shapovalov, Maxim V.; Renfrew, P. Douglas; Mulligan, Vikram K.; Kappel, Kalli; Labonte, Jason W.; Pacella, Michael S.; Bonneau, Richard; Bradley, Philip; Dunbrack, Roland L.; Das, Rhiju; Baker, David; Kuhlman, Brian; Kortemme, Tanja; Gray, Jeffrey J.

In: Journal of Chemical Theory and Computation, Vol. 13, No. 6, 13.06.2017, p. 3031-3048.

Research output: Contribution to journalArticle

Alford, RF, Leaver-Fay, A, Jeliazkov, JR, O'Meara, MJ, DiMaio, FP, Park, H, Shapovalov, MV, Renfrew, PD, Mulligan, VK, Kappel, K, Labonte, JW, Pacella, MS, Bonneau, R, Bradley, P, Dunbrack, RL, Das, R, Baker, D, Kuhlman, B, Kortemme, T & Gray, JJ 2017, 'The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design', Journal of Chemical Theory and Computation, vol. 13, no. 6, pp. 3031-3048. https://doi.org/10.1021/acs.jctc.7b00125
Alford RF, Leaver-Fay A, Jeliazkov JR, O'Meara MJ, DiMaio FP, Park H et al. The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design. Journal of Chemical Theory and Computation. 2017 Jun 13;13(6):3031-3048. https://doi.org/10.1021/acs.jctc.7b00125
Alford, Rebecca F. ; Leaver-Fay, Andrew ; Jeliazkov, Jeliazko R. ; O'Meara, Matthew J. ; DiMaio, Frank P. ; Park, Hahnbeom ; Shapovalov, Maxim V. ; Renfrew, P. Douglas ; Mulligan, Vikram K. ; Kappel, Kalli ; Labonte, Jason W. ; Pacella, Michael S. ; Bonneau, Richard ; Bradley, Philip ; Dunbrack, Roland L. ; Das, Rhiju ; Baker, David ; Kuhlman, Brian ; Kortemme, Tanja ; Gray, Jeffrey J. / The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design. In: Journal of Chemical Theory and Computation. 2017 ; Vol. 13, No. 6. pp. 3031-3048.
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