Literature DB >> 26492194

Analysis of free modeling predictions by RBO aleph in CASP11.

Mahmoud Mabrouk1, Tim Werner1, Michael Schneider1, Ines Putz1, Oliver Brock2.   

Abstract

The CASP experiment is a biannual benchmark for assessing protein structure prediction methods. In CASP11, RBO Aleph ranked as one of the top-performing automated servers in the free modeling category. This category consists of targets for which structural templates are not easily retrievable. We analyze the performance of RBO Aleph and show that its success in CASP was a result of its ab initio structure prediction protocol. A detailed analysis of this protocol demonstrates that two components unique to our method greatly contributed to prediction quality: residue-residue contact prediction by EPC-map and contact-guided conformational space search by model-based search (MBS). Interestingly, our analysis also points to a possible fundamental problem in evaluating the performance of protein structure prediction methods: Improvements in components of the method do not necessarily lead to improvements of the entire method. This points to the fact that these components interact in ways that are poorly understood. This problem, if indeed true, represents a significant obstacle to community-wide progress. Proteins 2016; 84(Suppl 1):87-104.
© 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Entities:  

Keywords:  CASP; ab initio structure prediction; conformational space search; contact prediction; free modeling; structure prediction pipeline

Mesh:

Substances:

Year:  2015        PMID: 26492194      PMCID: PMC4841752          DOI: 10.1002/prot.24950

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


  33 in total

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5.  RBO Aleph: leveraging novel information sources for protein structure prediction.

Authors:  Mahmoud Mabrouk; Ines Putz; Tim Werner; Michael Schneider; Moritz Neeb; Philipp Bartels; Oliver Brock
Journal:  Nucleic Acids Res       Date:  2015-04-20       Impact factor: 16.971

6.  I-TASSER server: new development for protein structure and function predictions.

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Authors:  David T Jones; Tanya Singh; Tomasz Kosciolek; Stuart Tetchner
Journal:  Bioinformatics       Date:  2014-11-26       Impact factor: 6.937

9.  Combining physicochemical and evolutionary information for protein contact prediction.

Authors:  Michael Schneider; Oliver Brock
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  3 in total

1.  CONFOLD2: improved contact-driven ab initio protein structure modeling.

Authors:  Badri Adhikari; Jianlin Cheng
Journal:  BMC Bioinformatics       Date:  2018-01-25       Impact factor: 3.169

2.  DNCON2: improved protein contact prediction using two-level deep convolutional neural networks.

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Journal:  Bioinformatics       Date:  2018-05-01       Impact factor: 6.937

3.  UniCon3D: de novo protein structure prediction using united-residue conformational search via stepwise, probabilistic sampling.

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Journal:  Bioinformatics       Date:  2016-06-03       Impact factor: 6.937

  3 in total

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