Literature DB >> 23900763

One contact for every twelve residues allows robust and accurate topology-level protein structure modeling.

David E Kim1, Frank Dimaio, Ray Yu-Ruei Wang, Yifan Song, David Baker.   

Abstract

A number of methods have been described for identifying pairs of contacting residues in protein three-dimensional structures, but it is unclear how many contacts are required for accurate structure modeling. The CASP10 assisted contact experiment provided a blind test of contact guided protein structure modeling. We describe the models generated for these contact guided prediction challenges using the Rosetta structure modeling methodology. For nearly all cases, the submitted models had the correct overall topology, and in some cases, they had near atomic-level accuracy; for example the model of the 384 residue homo-oligomeric tetramer (Tc680o) had only 2.9 Å root-mean-square deviation (RMSD) from the crystal structure. Our results suggest that experimental and bioinformatic methods for obtaining contact information may need to generate only one correct contact for every 12 residues in the protein to allow accurate topology level modeling.
Copyright © 2013 Wiley Periodicals, Inc.

Entities:  

Keywords:  ab initio prediction; comparative modeling; contact prediction; homology modeling; protein structure prediction; rosetta

Mesh:

Substances:

Year:  2013        PMID: 23900763      PMCID: PMC4128384          DOI: 10.1002/prot.24374

Source DB:  PubMed          Journal:  Proteins        ISSN: 0887-3585


  43 in total

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Authors:  D T Jones
Journal:  J Mol Biol       Date:  1999-09-17       Impact factor: 5.469

2.  Improved recognition of native-like protein structures using a combination of sequence-dependent and sequence-independent features of proteins.

Authors:  K T Simons; I Ruczinski; C Kooperberg; B A Fox; C Bystroff; D Baker
Journal:  Proteins       Date:  1999-01-01

3.  TOUCHSTONEX: protein structure prediction with sparse NMR data.

Authors:  Wei Li; Yang Zhang; Daisuke Kihara; Yuanpeng Janet Huang; Deyou Zheng; Gaetano T Montelione; Andrzej Kolinski; Jeffrey Skolnick
Journal:  Proteins       Date:  2003-11-01

4.  Protein structure prediction using sparse dipolar coupling data.

Authors:  Youxing Qu; Jun-tao Guo; Victor Olman; Ying Xu
Journal:  Nucleic Acids Res       Date:  2004-01-26       Impact factor: 16.971

5.  LGA: A method for finding 3D similarities in protein structures.

Authors:  Adam Zemla
Journal:  Nucleic Acids Res       Date:  2003-07-01       Impact factor: 16.971

6.  Protein structure prediction using Rosetta.

Authors:  Carol A Rohl; Charlie E M Strauss; Kira M S Misura; David Baker
Journal:  Methods Enzymol       Date:  2004       Impact factor: 1.600

7.  NMR structure determination for larger proteins using backbone-only data.

Authors:  Srivatsan Raman; Oliver F Lange; Paolo Rossi; Michael Tyka; Xu Wang; James Aramini; Gaohua Liu; Theresa A Ramelot; Alexander Eletsky; Thomas Szyperski; Michael A Kennedy; James Prestegard; Gaetano T Montelione; David Baker
Journal:  Science       Date:  2010-02-04       Impact factor: 47.728

8.  Atomic model of the type III secretion system needle.

Authors:  Antoine Loquet; Nikolaos G Sgourakis; Rashmi Gupta; Karin Giller; Dietmar Riedel; Christian Goosmann; Christian Griesinger; Michael Kolbe; David Baker; Stefan Becker; Adam Lange
Journal:  Nature       Date:  2012-05-20       Impact factor: 49.962

9.  Extending CATH: increasing coverage of the protein structure universe and linking structure with function.

Authors:  Alison L Cuff; Ian Sillitoe; Tony Lewis; Andrew B Clegg; Robert Rentzsch; Nicholas Furnham; Marialuisa Pellegrini-Calace; David Jones; Janet Thornton; Christine A Orengo
Journal:  Nucleic Acids Res       Date:  2010-11-19       Impact factor: 16.971

10.  Modeling symmetric macromolecular structures in Rosetta3.

Authors:  Frank DiMaio; Andrew Leaver-Fay; Phil Bradley; David Baker; Ingemar André
Journal:  PLoS One       Date:  2011-06-22       Impact factor: 3.240

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  40 in total

1.  Protein contact prediction by integrating joint evolutionary coupling analysis and supervised learning.

Authors:  Jianzhu Ma; Sheng Wang; Zhiyong Wang; Jinbo Xu
Journal:  Bioinformatics       Date:  2015-08-14       Impact factor: 6.937

2.  Coevolution of Residues Provides Evidence of a Functional Heterodimer of 5-HT2AR and 5-HT2CR Involving Both Intracellular and Extracellular Domains.

Authors:  Bernard Fongang; Kathryn A Cunningham; Maga Rowicka; Andrzej Kudlicki
Journal:  Neuroscience       Date:  2019-06-01       Impact factor: 3.590

3.  Machine learning-assisted directed protein evolution with combinatorial libraries.

Authors:  Zachary Wu; S B Jennifer Kan; Russell D Lewis; Bruce J Wittmann; Frances H Arnold
Journal:  Proc Natl Acad Sci U S A       Date:  2019-04-12       Impact factor: 11.205

4.  KScons: a Bayesian approach for protein residue contact prediction using the knob-socket model of protein tertiary structure.

Authors:  Qiwei Li; David B Dahl; Marina Vannucci; Hyun Joo; Jerry W Tsai
Journal:  Bioinformatics       Date:  2016-08-24       Impact factor: 6.937

5.  CoinFold: a web server for protein contact prediction and contact-assisted protein folding.

Authors:  Sheng Wang; Wei Li; Renyu Zhang; Shiwang Liu; Jinbo Xu
Journal:  Nucleic Acids Res       Date:  2016-04-25       Impact factor: 16.971

6.  Analysis of deep learning methods for blind protein contact prediction in CASP12.

Authors:  Sheng Wang; Siqi Sun; Jinbo Xu
Journal:  Proteins       Date:  2017-09-06

7.  Driven to near-experimental accuracy by refinement via molecular dynamics simulations.

Authors:  Lim Heo; Collin F Arbour; Michael Feig
Journal:  Proteins       Date:  2019-06-24

8.  High-accuracy protein structures by combining machine-learning with physics-based refinement.

Authors:  Lim Heo; Michael Feig
Journal:  Proteins       Date:  2019-11-15

Review 9.  Protein folding and de novo protein design for biotechnological applications.

Authors:  George A Khoury; James Smadbeck; Chris A Kieslich; Christodoulos A Floudas
Journal:  Trends Biotechnol       Date:  2013-11-19       Impact factor: 19.536

Review 10.  Finding the needle in the haystack: towards solving the protein-folding problem computationally.

Authors:  Bian Li; Michaela Fooksa; Sten Heinze; Jens Meiler
Journal:  Crit Rev Biochem Mol Biol       Date:  2017-10-04       Impact factor: 8.250

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