Literature DB >> 28966016

Enhancing Structure Prediction and Design of Soluble and Membrane Proteins with Explicit Solvent-Protein Interactions.

Jason K Lai1, Joaquin Ambia1, Yumeng Wang2, Patrick Barth3.   

Abstract

Solvent molecules interact intimately with proteins and can profoundly regulate their structure and function. However, accurately and efficiently modeling protein solvation effects at the molecular level has been challenging. Here, we present a method that improves the atomic-level modeling of soluble and membrane protein structures and binding by efficiently predicting de novo protein-solvent molecule interactions. The method predicted with unprecedented accuracy buried water molecule positions, solvated protein conformations, and challenging mutational effects on protein binding. When applied to homology modeling, solvent-bound membrane protein structures, pockets, and cavities were recapitulated with near-atomic precision even from distant homologs. Blindly refined atomic-level structures of evolutionary distant G protein-coupled receptors imply strikingly different functional roles of buried solvent between receptor classes. The method should prove useful for refining low-resolution protein structures, accurately modeling drug-binding sites in structurally uncharacterized receptors, and designing solvent-mediated protein catalysis, recognition, ligand binding, and membrane protein signaling.
Copyright © 2017 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  G protein-coupled receptors; explicit solvation model; ligand-binding cavities; membrane proteins; protein binding energy prediction; protein design; protein homology modeling; protein modeling; protein-protein complexes; protein-solvent interactions

Mesh:

Substances:

Year:  2017        PMID: 28966016      PMCID: PMC5909693          DOI: 10.1016/j.str.2017.09.002

Source DB:  PubMed          Journal:  Structure        ISSN: 0969-2126            Impact factor:   5.006


  64 in total

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3.  Multipass membrane protein structure prediction using Rosetta.

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6.  Prediction of water and metal binding sites and their affinities by using the Fold-X force field.

Authors:  Joost W H Schymkowitz; Frederic Rousseau; Ivo C Martins; Jesper Ferkinghoff-Borg; Francois Stricher; Luis Serrano
Journal:  Proc Natl Acad Sci U S A       Date:  2005-07-08       Impact factor: 11.205

Review 7.  Insights into the structure of class B GPCRs.

Authors:  Kaspar Hollenstein; Chris de Graaf; Andrea Bortolato; Ming-Wei Wang; Fiona H Marshall; Raymond C Stevens
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8.  A smoothed backbone-dependent rotamer library for proteins derived from adaptive kernel density estimates and regressions.

Authors:  Maxim V Shapovalov; Roland L Dunbrack
Journal:  Structure       Date:  2011-06-08       Impact factor: 5.006

9.  Functional waters in intraprotein proton transfer monitored by FTIR difference spectroscopy.

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Authors:  Yixing Chen; Halil I Okur; Nikolaos Gomopoulos; Carlos Macias-Romero; Paul S Cremer; Poul B Petersen; Gabriele Tocci; David M Wilkins; Chungwen Liang; Michele Ceriotti; Sylvie Roke
Journal:  Sci Adv       Date:  2016-04-08       Impact factor: 14.136

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1.  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
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Authors:  Justina Jankauskaite; Brian Jiménez-García; Justas Dapkunas; Juan Fernández-Recio; Iain H Moal
Journal:  Bioinformatics       Date:  2019-02-01       Impact factor: 6.937

6.  A lipophilicity-based energy function for membrane-protein modelling and design.

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Journal:  PLoS Comput Biol       Date:  2019-08-28       Impact factor: 4.475

7.  Intermolecular Mechanism and Dynamic Investigation of Avian Influenza H7N9 Virus' Susceptibility to E119V-Substituted Peramivir-Neuraminidase Complex.

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8.  Energy Profile Bayes and Thompson Optimized Convolutional Neural Network protein structure prediction.

Authors:  Varanavasi Nallasamy; Malarvizhi Seshiah
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Review 9.  Macromolecular modeling and design in Rosetta: recent methods and frameworks.

Authors:  Julia Koehler Leman; Brian D Weitzner; Steven M Lewis; Jared Adolf-Bryfogle; Nawsad Alam; Rebecca F Alford; Melanie Aprahamian; David Baker; Kyle A Barlow; Patrick Barth; Benjamin Basanta; Brian J Bender; Kristin Blacklock; Jaume Bonet; Scott E Boyken; Phil Bradley; Chris Bystroff; Patrick Conway; Seth Cooper; Bruno E Correia; Brian Coventry; Rhiju Das; René M De Jong; Frank DiMaio; Lorna Dsilva; Roland Dunbrack; Alexander S Ford; Brandon Frenz; Darwin Y Fu; Caleb Geniesse; Lukasz Goldschmidt; Ragul Gowthaman; Jeffrey J Gray; Dominik Gront; Sharon Guffy; Scott Horowitz; Po-Ssu Huang; Thomas Huber; Tim M Jacobs; Jeliazko R Jeliazkov; David K Johnson; Kalli Kappel; John Karanicolas; Hamed Khakzad; Karen R Khar; Sagar D Khare; Firas Khatib; Alisa Khramushin; Indigo C King; Robert Kleffner; Brian Koepnick; Tanja Kortemme; Georg Kuenze; Brian Kuhlman; Daisuke Kuroda; Jason W Labonte; Jason K Lai; Gideon Lapidoth; Andrew Leaver-Fay; Steffen Lindert; Thomas Linsky; Nir London; Joseph H Lubin; Sergey Lyskov; Jack Maguire; Lars Malmström; Enrique Marcos; Orly Marcu; Nicholas A Marze; Jens Meiler; Rocco Moretti; Vikram Khipple Mulligan; Santrupti Nerli; Christoffer Norn; Shane Ó'Conchúir; Noah Ollikainen; Sergey Ovchinnikov; Michael S Pacella; Xingjie Pan; Hahnbeom Park; Ryan E Pavlovicz; Manasi Pethe; Brian G Pierce; Kala Bharath Pilla; Barak Raveh; P Douglas Renfrew; Shourya S Roy Burman; Aliza Rubenstein; Marion F Sauer; Andreas Scheck; William 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 Watkins; Lior Zimmerman; Richard Bonneau
Journal:  Nat Methods       Date:  2020-06-01       Impact factor: 28.547

  9 in total

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