Rosetta FlexPepDock ab-initio: simultaneous folding, docking and refinement of peptides onto their receptors.
TLDR
The results presented here significantly extend the scope of state-of-the-art methods for high-resolution peptide modeling, which can now be applied to a wide variety of peptide-protein interactions where no prior information about the peptide backbone conformation is available, enabling detailed structure-based studies and manipulation of those interactions.Abstract:
Flexible peptides that fold upon binding to another protein molecule mediate a large number of regulatory interactions in the living cell and may provide highly specific recognition modules. We present Rosetta FlexPepDock ab-initio, a protocol for simultaneous docking and de-novo folding of peptides, starting from an approximate specification of the peptide binding site. Using the Rosetta fragments library and a coarse-grained structural representation of the peptide and the receptor, FlexPepDock ab-initio samples efficiently and simultaneously the space of possible peptide backbone conformations and rigid-body orientations over the receptor surface of a given binding site. The subsequent all-atom refinement of the coarse-grained models includes full side-chain modeling of both the receptor and the peptide, resulting in high-resolution models in which key side-chain interactions are recapitulated. The protocol was applied to a benchmark in which peptides were modeled over receptors in either their bound backbone conformations or in their free, unbound form. Near-native peptide conformations were identified in 18/26 of the bound cases and 7/14 of the unbound cases. The protocol performs well on peptides from various classes of secondary structures, including coiled peptides with unusual turns and kinks. The results presented here significantly extend the scope of state-of-the-art methods for high-resolution peptide modeling, which can now be applied to a wide variety of peptide-protein interactions where no prior information about the peptide backbone conformation is available, enabling detailed structure-based studies and manipulation of those interactions.read more
Citations
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Journal ArticleDOI
Improved Docking of Polypeptides with Glide
TL;DR: The optimized polypeptide protocol is most accurate for extended peptides of limited size and number of formal charges, defining a domain of applicability for this approach.
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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 DiMaio,Hahnbeom Park,Maxim V. Shapovalov,P. Douglas Renfrew,Vikram Khipple 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 +19 more
TL;DR: This paper describes the mathematical models and physical concepts that underlie the latest Rosetta energy function, called the Rosetta Energy Function 2015 (REF15), and explains how to use Rosetta energies to identify and analyze the features of biomolecular models.
Journal ArticleDOI
Coarse-Grained Protein Models and Their Applications
Sebastian Kmiecik,Dominik Gront,Michal Kolinski,Lukasz Wieteska,Lukasz Wieteska,Aleksandra Elzbieta Dawid,Andrzej Kolinski +6 more
TL;DR: An overview of coarse-grained models focusing on their design, including choices of representation, models of energy functions, sampling of conformational space, and applications in the modeling of protein structure, dynamics, and interactions are provided.
Journal ArticleDOI
Macromolecular modeling and design in Rosetta: recent methods and frameworks
Julia Koehler Leman,Brian D. Weitzner,Brian D. Weitzner,Steven M. Lewis,Steven M. Lewis,Jared Adolf-Bryfogle,Nawsad Alam,Rebecca F. Alford,Melanie L. Aprahamian,David Baker,Kyle A. Barlow,Patrick Barth,Patrick Barth,Benjamin Basanta,Brian J. Bender,Kristin Blacklock,Jaume Bonet,Jaume Bonet,Scott E. Boyken,Phil Bradley,Christopher Bystroff,Patrick Conway,Seth Cooper,Bruno E. Correia,Bruno E. Correia,Brian Coventry,Rhiju Das,René M. de Jong,Frank DiMaio,Lorna Dsilva,Roland L. Dunbrack,Alex Ford,Brandon Frenz,Darwin Y. Fu,Caleb Geniesse,Lukasz Goldschmidt,Ragul Gowthaman,Jeffrey J. Gray,Dominik Gront,Sharon L. Guffy,Scott Horowitz,Po-Ssu Huang,Thomas Huber,Timothy M. Jacobs,Jeliazko R. Jeliazkov,David K. Johnson,Kalli Kappel,John Karanicolas,Hamed Khakzad,Hamed Khakzad,Karen R. Khar,Sagar D. Khare,Firas Khatib,Alisa Khramushin,Indigo Chris King,Robert Kleffner,Brian Koepnick,Tanja Kortemme,Georg Kuenze,Brian Kuhlman,Daisuke Kuroda,Jason W. Labonte,Jason W. Labonte,Jason K. Lai,Gideon Lapidoth,Andrew Leaver-Fay,Steffen Lindert,Thomas W. Linsky,Nir London,Joseph H. Lubin,Sergey Lyskov,Jack Maguire,Lars Malmström,Lars Malmström,Lars Malmström,Enrique Marcos,Orly Marcu,Nicholas A. Marze,Jens Meiler,Rocco Moretti,Vikram Khipple Mulligan,Santrupti Nerli,Christoffer Norn,Shane O’Conchúir,Noah Ollikainen,Sergey Ovchinnikov,Michael S. Pacella,Xingjie Pan,Hahnbeom Park,Ryan E. Pavlovicz,Manasi A. Pethe,Brian G. Pierce,Kala Bharath Pilla,Barak Raveh,P. Douglas Renfrew,Shourya S. Roy Burman,Aliza B. Rubenstein,Marion F. Sauer,Andreas Scheck,Andreas Scheck,William R. Schief,Ora Schueler-Furman,Yuval Sedan,Alexander M. Sevy,Nikolaos G. Sgourakis,Lei Shi,Justin B. Siegel,Daniel-Adriano Silva,Shannon Smith,Yifan Song,Amelie Stein,Maria Szegedy,Frank D. Teets,Summer B. Thyme,Ray Yu-Ruei Wang,Andrew M. Watkins,Lior Zimmerman,Richard Bonneau +117 more
TL;DR: This Perspective reviews tools developed over the past five years in the Rosetta software, including over 80 methods, and discusses improvements to the score function, user interfaces and usability.
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A Comprehensive Review on Current Advances in Peptide Drug Development and Design.
TL;DR: An updated review on key developments of computational modeling of peptide–protein interactions (PepPIs) with an aim to assist experimental biologists exploit suitable docking methods to advance peptide interfering strategies against PPIs.
References
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Book ChapterDOI
Protein Structure Prediction Using Rosetta
TL;DR: This chapter elaborates protein structure prediction using Rosetta, where short fragments of known proteins are assembled by a Monte Carlo strategy to yield native-like protein conformations.
Journal ArticleDOI
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