Literature DB >> 28236237

Modeling Peptide-Protein Structure and Binding Using Monte Carlo Sampling Approaches: Rosetta FlexPepDock and FlexPepBind.

Nawsad Alam1, Ora Schueler-Furman2.   

Abstract

Many signaling and regulatory processes involve peptide-mediated protein interactions, i.e., the binding of a short stretch in one protein to a domain in its partner. Computational tools that generate accurate models of peptide-receptor structures and binding improve characterization and manipulation of known interactions, help to discover yet unknown peptide-protein interactions and networks, and bring into reach the design of peptide-based drugs for targeting specific systems of medical interest.Here, we present a concise overview of the Rosetta FlexPepDock protocol and its derivatives that we have developed for the structure-based characterization of peptide-protein binding. Rosetta FlexPepDock was built to generate precise models of protein-peptide complex structures, by effectively addressing the challenge of the considerable conformational flexibility of the peptide. Rosetta FlexPepBind is an extension of this protocol that allows characterizing peptide-binding affinities and specificities of various biological systems, based on the structural models generated by Rosetta FlexPepDock. We provide detailed descriptions and guidelines for the usage of these protocols, and on a specific example, we highlight the variety of different challenges that can be met and the questions that can be answered with Rosetta FlexPepDock.

Keywords:  Peptide binding; Peptide docking; Peptide modeling; Peptide specificity; Peptide-protein interactions; Rosetta FlexPepBind; Rosetta FlexPepDock

Mesh:

Substances:

Year:  2017        PMID: 28236237     DOI: 10.1007/978-1-4939-6798-8_9

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  10 in total

1.  Markov state modeling reveals alternative unbinding pathways for peptide-MHC complexes.

Authors:  Jayvee R Abella; Dinler Antunes; Kyle Jackson; Gregory Lizée; Cecilia Clementi; Lydia E Kavraki
Journal:  Proc Natl Acad Sci U S A       Date:  2020-11-12       Impact factor: 11.205

2.  Structure-function guided modeling of chemokine-GPCR specificity for the chemokine XCL1 and its receptor XCR1.

Authors:  Jamie C Fox; Monica A Thomas; Acacia F Dishman; Olav Larsen; Takashi Nakayama; Osamu Yoshie; Mette Marie Rosenkilde; Brian F Volkman
Journal:  Sci Signal       Date:  2019-09-03       Impact factor: 8.192

3.  Web-accessible molecular modeling with Rosetta: The Rosetta Online Server that Includes Everyone (ROSIE).

Authors:  Rocco Moretti; Sergey Lyskov; Rhiju Das; Jens Meiler; Jeffrey J Gray
Journal:  Protein Sci       Date:  2017-10-27       Impact factor: 6.725

4.  Flexible docking of peptides to proteins using CABS-dock.

Authors:  Mateusz Kurcinski; Aleksandra Badaczewska-Dawid; Michal Kolinski; Andrzej Kolinski; Sebastian Kmiecik
Journal:  Protein Sci       Date:  2019-11-11       Impact factor: 6.725

5.  Post-translational modifications reshape the antigenic landscape of the MHC I immunopeptidome in tumors.

Authors:  Assaf Kacen; Aaron Javitt; Matthias P Kramer; David Morgenstern; Tomer Tsaban; Merav D Shmueli; Guo Ci Teo; Felipe da Veiga Leprevost; Eilon Barnea; Fengchao Yu; Arie Admon; Lea Eisenbach; Yardena Samuels; Ora Schueler-Furman; Yishai Levin; Alexey I Nesvizhskii; Yifat Merbl
Journal:  Nat Biotechnol       Date:  2022-10-06       Impact factor: 68.164

6.  Matching protein surface structural patches for high-resolution blind peptide docking.

Authors:  Alisa Khramushin; Ziv Ben-Aharon; Tomer Tsaban; Julia K Varga; Orly Avraham; Ora Schueler-Furman
Journal:  Proc Natl Acad Sci U S A       Date:  2022-04-28       Impact factor: 12.779

7.  Combining Three-Dimensional Modeling with Artificial Intelligence to Increase Specificity and Precision in Peptide-MHC Binding Predictions.

Authors:  Michelle P Aranha; Yead S M Jewel; Robert A Beckman; Louis M Weiner; Julie C Mitchell; Jerry M Parks; Jeremy C Smith
Journal:  J Immunol       Date:  2020-09-02       Impact factor: 5.422

8.  Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks.

Authors:  Julia Koehler Leman; Sergey Lyskov; Steven M Lewis; Jared Adolf-Bryfogle; Rebecca F Alford; Kyle Barlow; Ziv Ben-Aharon; Daniel Farrell; Jason Fell; William A Hansen; Ameya Harmalkar; Jeliazko Jeliazkov; Georg Kuenze; Justyna D Krys; Ajasja Ljubetič; Amanda L Loshbaugh; Jack Maguire; Rocco Moretti; Vikram Khipple Mulligan; Morgan L Nance; Phuong T Nguyen; Shane Ó Conchúir; Shourya S Roy Burman; Rituparna Samanta; Shannon T Smith; Frank Teets; Johanna K S Tiemann; Andrew Watkins; Hope Woods; Brahm J Yachnin; Christopher D Bahl; Chris Bailey-Kellogg; David Baker; Rhiju Das; Frank DiMaio; Sagar D Khare; Tanja Kortemme; Jason W Labonte; Kresten Lindorff-Larsen; Jens Meiler; William Schief; Ora Schueler-Furman; Justin B Siegel; Amelie Stein; Vladimir Yarov-Yarovoy; Brian Kuhlman; Andrew Leaver-Fay; Dominik Gront; Jeffrey J Gray; Richard Bonneau
Journal:  Nat Commun       Date:  2021-11-29       Impact factor: 17.694

9.  Oxidation of SQSTM1/p62 mediates the link between redox state and protein homeostasis.

Authors:  Bernadette Carroll; Elsje G Otten; Diego Manni; Rhoda Stefanatos; Fiona M Menzies; Graham R Smith; Diana Jurk; Niall Kenneth; Simon Wilkinson; Joao F Passos; Johannes Attems; Elizabeth A Veal; Elisa Teyssou; Danielle Seilhean; Stéphanie Millecamps; Eeva-Liisa Eskelinen; Agnieszka K Bronowska; David C Rubinsztein; Alberto Sanz; Viktor I Korolchuk
Journal:  Nat Commun       Date:  2018-01-17       Impact factor: 14.919

10.  Structure-based prediction of HDAC6 substrates validated by enzymatic assay reveals determinants of promiscuity and detects new potential substrates.

Authors:  Julia K Varga; Kelsey Diffley; Katherine R Welker Leng; Carol A Fierke; Ora Schueler-Furman
Journal:  Sci Rep       Date:  2022-02-02       Impact factor: 4.379

  10 in total

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