Literature DB >> 28430426

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

Rebecca F Alford1, Andrew Leaver-Fay2, Jeliazko R Jeliazkov3, Matthew J O'Meara4, Frank P DiMaio5, Hahnbeom Park6, Maxim V Shapovalov7, P Douglas Renfrew8,9, Vikram K Mulligan6, Kalli Kappel10, Jason W Labonte1, Michael S Pacella11, Richard Bonneau8,9, Philip Bradley12, Roland L Dunbrack7, Rhiju Das10, David Baker6,13, Brian Kuhlman2, Tanja Kortemme14, Jeffrey J Gray1,3.   

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.

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Year:  2017        PMID: 28430426      PMCID: PMC5717763          DOI: 10.1021/acs.jctc.7b00125

Source DB:  PubMed          Journal:  J Chem Theory Comput        ISSN: 1549-9618            Impact factor:   6.006


  102 in total

1.  The Protein Data Bank.

Authors:  H M Berman; J Westbrook; Z Feng; G Gilliland; T N Bhat; H Weissig; I N Shindyalov; P E Bourne
Journal:  Nucleic Acids Res       Date:  2000-01-01       Impact factor: 16.971

2.  The penultimate rotamer library.

Authors:  S C Lovell; J M Word; J S Richardson; D C Richardson
Journal:  Proteins       Date:  2000-08-15

3.  Protein-protein docking with simultaneous optimization of rigid-body displacement and side-chain conformations.

Authors:  Jeffrey J Gray; Stewart Moughon; Chu Wang; Ora Schueler-Furman; Brian Kuhlman; Carol A Rohl; David Baker
Journal:  J Mol Biol       Date:  2003-08-01       Impact factor: 5.469

4.  Toward high-resolution de novo structure prediction for small proteins.

Authors:  Philip Bradley; Kira M S Misura; David Baker
Journal:  Science       Date:  2005-09-16       Impact factor: 47.728

5.  Multipass membrane protein structure prediction using Rosetta.

Authors:  Vladimir Yarov-Yarovoy; Jack Schonbrun; David Baker
Journal:  Proteins       Date:  2006-03-01

6.  A simple physical model for the prediction and design of protein-DNA interactions.

Authors:  James J Havranek; Carlos M Duarte; David Baker
Journal:  J Mol Biol       Date:  2004-11-12       Impact factor: 5.469

7.  PyRosetta: a script-based interface for implementing molecular modeling algorithms using Rosetta.

Authors:  Sidhartha Chaudhury; Sergey Lyskov; Jeffrey J Gray
Journal:  Bioinformatics       Date:  2010-01-07       Impact factor: 6.937

8.  Improving hybrid statistical and physical forcefields through local structure enumeration.

Authors:  Patrick Conway; Frank DiMaio
Journal:  Protein Sci       Date:  2016-06-16       Impact factor: 6.725

9.  Importance of ligand conformational energies in carbohydrate docking: Sorting the wheat from the chaff.

Authors:  Anita K Nivedha; Spandana Makeneni; Bethany Lachele Foley; Matthew B Tessier; Robert J Woods
Journal:  J Comput Chem       Date:  2013-12-29       Impact factor: 3.376

10.  Protein-protein docking with dynamic residue protonation states.

Authors:  Krishna Praneeth Kilambi; Kavan Reddy; Jeffrey J Gray
Journal:  PLoS Comput Biol       Date:  2014-12-11       Impact factor: 4.475

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  307 in total

1.  A Unified De Novo Approach for Predicting the Structures of Ordered and Disordered Proteins.

Authors:  John J Ferrie; E James Petersson
Journal:  J Phys Chem B       Date:  2020-06-11       Impact factor: 2.991

2.  Optimization of Protein Thermostability and Exploitation of Recognition Behavior to Engineer Altered Protein-DNA Recognition.

Authors:  Abigail R Lambert; Jazmine P Hallinan; Rachel Werther; Dawid Głów; Barry L Stoddard
Journal:  Structure       Date:  2020-04-30       Impact factor: 5.006

Review 3.  A humanized yeast system to analyze cleavage of prelamin A by ZMPSTE24.

Authors:  Eric D Spear; Rebecca F Alford; Tim D Babatz; Kaitlin M Wood; Otto W Mossberg; Kamsi Odinammadu; Khurts Shilagardi; Jeffrey J Gray; Susan Michaelis
Journal:  Methods       Date:  2019-01-06       Impact factor: 3.608

4.  Flex ddG: Rosetta Ensemble-Based Estimation of Changes in Protein-Protein Binding Affinity upon Mutation.

Authors:  Kyle A Barlow; Shane Ó Conchúir; Samuel Thompson; Pooja Suresh; James E Lucas; Markus Heinonen; Tanja Kortemme
Journal:  J Phys Chem B       Date:  2018-02-15       Impact factor: 2.991

5.  Systematic Evaluation of Soluble Protein Expression Using a Fluorescent Unnatural Amino Acid Reveals No Reliable Predictors of Tolerability.

Authors:  Zachary M Hostetler; John J Ferrie; Marc R Bornstein; Itthipol Sungwienwong; E James Petersson; Rahul M Kohli
Journal:  ACS Chem Biol       Date:  2018-09-20       Impact factor: 5.100

6.  Rapid Sampling of Hydrogen Bond Networks for Computational Protein Design.

Authors:  Jack B Maguire; Scott E Boyken; David Baker; Brian Kuhlman
Journal:  J Chem Theory Comput       Date:  2018-04-20       Impact factor: 6.006

7.  Automated cryo-EM structure refinement using correlation-driven molecular dynamics.

Authors:  Maxim Igaev; Carsten Kutzner; Lars V Bock; Andrea C Vaiana; Helmut Grubmüller
Journal:  Elife       Date:  2019-03-04       Impact factor: 8.140

8.  Blind tests of RNA-protein binding affinity prediction.

Authors:  Kalli Kappel; Inga Jarmoskaite; Pavanapuresan P Vaidyanathan; William J Greenleaf; Daniel Herschlag; Rhiju Das
Journal:  Proc Natl Acad Sci U S A       Date:  2019-04-08       Impact factor: 11.205

9.  Computational-guided determination of the functional role of 447-52D long CDRH3.

Authors:  Edwin Kamau; Richard Bonneau; Xiang-Peng Kong
Journal:  Protein Eng Des Sel       Date:  2018-12-01       Impact factor: 1.650

10.  Rosetta Machine Learning Models Accurately Classify Positional Effects of Thioamides on Proteolysis.

Authors:  Sam Giannakoulias; Sumant R Shringari; Chunxiao Liu; Hoang Anh T Phan; Taylor M Barrett; John J Ferrie; E James Petersson
Journal:  J Phys Chem B       Date:  2020-09-01       Impact factor: 2.991

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