Literature DB >> 28104891

Protein structure determination using metagenome sequence data.

Sergey Ovchinnikov1,2,3, Hahnbeom Park1,2, Neha Varghese4, Po-Ssu Huang1,2, Georgios A Pavlopoulos4, David E Kim1,5, Hetunandan Kamisetty6, Nikos C Kyrpides4,7, David Baker8,2,5.   

Abstract

Despite decades of work by structural biologists, there are still ~5200 protein families with unknown structure outside the range of comparative modeling. We show that Rosetta structure prediction guided by residue-residue contacts inferred from evolutionary information can accurately model proteins that belong to large families and that metagenome sequence data more than triple the number of protein families with sufficient sequences for accurate modeling. We then integrate metagenome data, contact-based structure matching, and Rosetta structure calculations to generate models for 614 protein families with currently unknown structures; 206 are membrane proteins and 137 have folds not represented in the Protein Data Bank. This approach provides the representative models for large protein families originally envisioned as the goal of the Protein Structure Initiative at a fraction of the cost.
Copyright © 2017, American Association for the Advancement of Science.

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Year:  2017        PMID: 28104891      PMCID: PMC5493203          DOI: 10.1126/science.aah4043

Source DB:  PubMed          Journal:  Science        ISSN: 0036-8075            Impact factor:   47.728


  38 in total

1.  Scoring function for automated assessment of protein structure template quality.

Authors:  Yang Zhang; Jeffrey Skolnick
Journal:  Proteins       Date:  2004-12-01

2.  Genomics-aided structure prediction.

Authors:  Joanna I Sułkowska; Faruck Morcos; Martin Weigt; Terence Hwa; José N Onuchic
Journal:  Proc Natl Acad Sci U S A       Date:  2012-06-12       Impact factor: 11.205

Review 3.  A bioinformatician's guide to metagenomics.

Authors:  Victor Kunin; Alex Copeland; Alla Lapidus; Konstantinos Mavromatis; Philip Hugenholtz
Journal:  Microbiol Mol Biol Rev       Date:  2008-12       Impact factor: 11.056

4.  Learning generative models for protein fold families.

Authors:  Sivaraman Balakrishnan; Hetunandan Kamisetty; Jaime G Carbonell; Su-In Lee; Christopher James Langmead
Journal:  Proteins       Date:  2011-01-25

5.  Computation and Functional Studies Provide a Model for the Structure of the Zinc Transporter hZIP4.

Authors:  Sagar Antala; Sergey Ovchinnikov; Hetunandan Kamisetty; David Baker; Robert E Dempski
Journal:  J Biol Chem       Date:  2015-05-13       Impact factor: 5.157

6.  Sequence co-evolution gives 3D contacts and structures of protein complexes.

Authors:  Thomas A Hopf; Charlotta P I Schärfe; João P G L M Rodrigues; Anna G Green; Oliver Kohlbacher; Chris Sander; Alexandre M J J Bonvin; Debora S Marks
Journal:  Elife       Date:  2014-09-25       Impact factor: 8.140

7.  Three-dimensional structures of membrane proteins from genomic sequencing.

Authors:  Thomas A Hopf; Lucy J Colwell; Robert Sheridan; Burkhard Rost; Chris Sander; Debora S Marks
Journal:  Cell       Date:  2012-05-10       Impact factor: 41.582

8.  The Protein Structure Initiative: achievements and visions for the future.

Authors:  Gaetano T Montelione
Journal:  F1000 Biol Rep       Date:  2012-04-02

9.  Robust and accurate prediction of residue-residue interactions across protein interfaces using evolutionary information.

Authors:  Sergey Ovchinnikov; Hetunandan Kamisetty; David Baker
Journal:  Elife       Date:  2014-05-01       Impact factor: 8.140

10.  A structural model of the active ribosome-bound membrane protein insertase YidC.

Authors:  Stephan Wickles; Abhishek Singharoy; Jessica Andreani; Stefan Seemayer; Lukas Bischoff; Otto Berninghausen; Johannes Soeding; Klaus Schulten; Eli O van der Sluis; Roland Beckmann
Journal:  Elife       Date:  2014-07-10       Impact factor: 8.140

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  160 in total

1.  Computer Modeling of N-Acetylglutamate Synthase: From Primary Structure to Elemental Stages of Catalysis.

Authors:  I V Polyakov; A E Kniga; B L Grigorenko; A V Nemukhin; S D Varfolomeev
Journal:  Dokl Biochem Biophys       Date:  2020-12-25       Impact factor: 0.788

2.  Synthetic protein alignments by CCMgen quantify noise in residue-residue contact prediction.

Authors:  Susann Vorberg; Stefan Seemayer; Johannes Söding
Journal:  PLoS Comput Biol       Date:  2018-11-05       Impact factor: 4.475

3.  Deep-learning contact-map guided protein structure prediction in CASP13.

Authors:  Wei Zheng; Yang Li; Chengxin Zhang; Robin Pearce; S M Mortuza; Yang Zhang
Journal:  Proteins       Date:  2019-08-14

4.  Evolutionary couplings of amino acid residues reveal structure and function of bacterial signaling proteins.

Authors:  Hendrik Szurmant
Journal:  Mol Microbiol       Date:  2019-07-03       Impact factor: 3.501

Review 5.  Toward a mechanistic understanding of Feo-mediated ferrous iron uptake.

Authors:  Alexandrea E Sestok; Richard O Linkous; Aaron T Smith
Journal:  Metallomics       Date:  2018-07-18       Impact factor: 4.526

6.  Accelerating physical simulations of proteins by leveraging external knowledge.

Authors:  Alberto Perez; Joseph A Morrone; Ken A Dill
Journal:  Wiley Interdiscip Rev Comput Mol Sci       Date:  2017-04-19

7.  Folding Membrane Proteins by Deep Transfer Learning.

Authors:  Sheng Wang; Zhen Li; Yizhou Yu; Jinbo Xu
Journal:  Cell Syst       Date:  2017-09-27       Impact factor: 10.304

8.  Distance-based protein folding powered by deep learning.

Authors:  Jinbo Xu
Journal:  Proc Natl Acad Sci U S A       Date:  2019-08-09       Impact factor: 11.205

9.  The ghrelin O-acyltransferase structure reveals a catalytic channel for transmembrane hormone acylation.

Authors:  Maria B Campaña; Flaviyan Jerome Irudayanathan; Tasha R Davis; Kayleigh R McGovern-Gooch; Rosemary Loftus; Mohammad Ashkar; Najae Escoffery; Melissa Navarro; Michelle A Sieburg; Shikha Nangia; James L Hougland
Journal:  J Biol Chem       Date:  2019-08-14       Impact factor: 5.157

Review 10.  What has de novo protein design taught us about protein folding and biophysics?

Authors:  David Baker
Journal:  Protein Sci       Date:  2019-04       Impact factor: 6.725

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