Literature DB >> 22809379

Recursive protein modeling: a divide and conquer strategy for Protein Structure Prediction and its case study in CASP9.

Jianlin Cheng1, Jesse Eickholt, Zheng Wang, Xin Deng.   

Abstract

After decades of research, protein structure prediction remains a very challenging problem. In order to address the different levels of complexity of structural modeling, two types of modeling techniques--template-based modeling and template-free modeling--have been developed. Template-based modeling can often generate a moderate- to high-resolution model when a similar, homologous template structure is found for a query protein but fails if no template or only incorrect templates are found. Template-free modeling, such as fragment-based assembly, may generate models of moderate resolution for small proteins of low topological complexity. Seldom have the two techniques been integrated together to improve protein modeling. Here we develop a recursive protein modeling approach to selectively and collaboratively apply template-based and template-free modeling methods to model template-covered (i.e. certain) and template-free (i.e. uncertain) regions of a protein. A preliminary implementation of the approach was tested on a number of hard modeling cases during the 9th Critical Assessment of Techniques for Protein Structure Prediction (CASP9) and successfully improved the quality of modeling in most of these cases. Recursive modeling can significantly reduce the complexity of protein structure modeling and integrate template-based and template-free modeling to improve the quality and efficiency of protein structure prediction.

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Year:  2012        PMID: 22809379      PMCID: PMC3622867          DOI: 10.1142/S0219720012420036

Source DB:  PubMed          Journal:  J Bioinform Comput Biol        ISSN: 0219-7200            Impact factor:   1.122


  39 in total

1.  The Protein Data Bank.

Authors:  H M Berman; J Westbrook; Z Feng; G Gilliland; T N Bhat; H Weissig; I N Shindyalov; P E Bourne
Journal:  Nucleic Acids Res       Date:  2000-01-01       Impact factor: 16.971

2.  Protein structure prediction and structural genomics.

Authors:  D Baker; A Sali
Journal:  Science       Date:  2001-10-05       Impact factor: 47.728

3.  Cyclic coordinate descent: A robotics algorithm for protein loop closure.

Authors:  Adrian A Canutescu; Roland L Dunbrack
Journal:  Protein Sci       Date:  2003-05       Impact factor: 6.725

4.  A comparison of profile hidden Markov model procedures for remote homology detection.

Authors:  Martin Madera; Julian Gough
Journal:  Nucleic Acids Res       Date:  2002-10-01       Impact factor: 16.971

5.  Automated structure prediction of weakly homologous proteins on a genomic scale.

Authors:  Yang Zhang; Jeffrey Skolnick
Journal:  Proc Natl Acad Sci U S A       Date:  2004-05-04       Impact factor: 11.205

6.  QMEAN: A comprehensive scoring function for model quality assessment.

Authors:  Pascal Benkert; Silvio C E Tosatto; Dietmar Schomburg
Journal:  Proteins       Date:  2008-04

7.  Evaluating the absolute quality of a single protein model using structural features and support vector machines.

Authors:  Zheng Wang; Allison N Tegge; Jianlin Cheng
Journal:  Proteins       Date:  2009-05-15

8.  GenBank.

Authors:  D A Benson; M S Boguski; D J Lipman; J Ostell; B F Ouellette; B A Rapp; D L Wheeler
Journal:  Nucleic Acids Res       Date:  1999-01-01       Impact factor: 16.971

9.  APOLLO: a quality assessment service for single and multiple protein models.

Authors:  Zheng Wang; Jesse Eickholt; Jianlin Cheng
Journal:  Bioinformatics       Date:  2011-05-05       Impact factor: 6.937

10.  A self-organizing algorithm for modeling protein loops.

Authors:  Pu Liu; Fangqiang Zhu; Dmitrii N Rassokhin; Dimitris K Agrafiotis
Journal:  PLoS Comput Biol       Date:  2009-08-21       Impact factor: 4.475

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  5 in total

1.  Designing and benchmarking the MULTICOM protein structure prediction system.

Authors:  Jilong Li; Xin Deng; Jesse Eickholt; Jianlin Cheng
Journal:  BMC Struct Biol       Date:  2013-02-27

2.  Designing and evaluating the MULTICOM protein local and global model quality prediction methods in the CASP10 experiment.

Authors:  Renzhi Cao; Zheng Wang; Jianlin Cheng
Journal:  BMC Struct Biol       Date:  2014-04-15

3.  Enhancing fragment-based protein structure prediction by customising fragment cardinality according to local secondary structure.

Authors:  Jad Abbass; Jean-Christophe Nebel
Journal:  BMC Bioinformatics       Date:  2020-05-01       Impact factor: 3.169

4.  GalaxyTBM: template-based modeling by building a reliable core and refining unreliable local regions.

Authors:  Junsu Ko; Hahnbeom Park; Chaok Seok
Journal:  BMC Bioinformatics       Date:  2012-08-10       Impact factor: 3.169

5.  A large-scale conformation sampling and evaluation server for protein tertiary structure prediction and its assessment in CASP11.

Authors:  Jilong Li; Renzhi Cao; Jianlin Cheng
Journal:  BMC Bioinformatics       Date:  2015-10-23       Impact factor: 3.169

  5 in total

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