Literature DB >> 23996302

Improved flexible refinement of protein docking in CAPRI rounds 22-27.

Yang Shen1.   

Abstract

Since the fourth evaluation for critical assessment of prediction of interactions (CAPRI), we have made improvements in three major areas in our refinement approach, namely the treatment of conformational flexibility, the binding free energy model, and the search algorithm. First, we incorporated backbone flexibility into our previous approach, which only optimized rigid backbone poses with limited side-chain flexibility. Here, we formulated and solved the conformational search as a hierarchical optimization problem (involving rigid-body poses, backbone flexibility, and side-chain flexibility). Second, we used continuum electrostatic calculations to include solvation effects in the binding free energy model. Finally, we eliminated sloppy modes (directions in which the free energy is essentially constant) to improve the efficiency of the search. With these improvements, we produced correct predictions for 6 of the 10 latest CAPRI targets, including one high, three medium, and two acceptable accuracy predictions. Compared to our previous performance in CAPRI, substantial improvements have been made for targets requiring homology modeling.
Copyright © 2013 Wiley Periodicals, Inc.

Keywords:  continuum electrostatics; energy funnel; free energy minimization; hierarchical optimization; normal modes; protein docking; protein flexibility; sloppy modes; structural refinement

Mesh:

Substances:

Year:  2013        PMID: 23996302     DOI: 10.1002/prot.24404

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


  6 in total

1.  Bayesian Active Learning for Optimization and Uncertainty Quantification in Protein Docking.

Authors:  Yue Cao; Yang Shen
Journal:  J Chem Theory Comput       Date:  2020-07-06       Impact factor: 6.006

2.  The ClusPro web server for protein-protein docking.

Authors:  Dima Kozakov; David R Hall; Bing Xia; Kathryn A Porter; Dzmitry Padhorny; Christine Yueh; Dmitri Beglov; Sandor Vajda
Journal:  Nat Protoc       Date:  2017-01-12       Impact factor: 13.491

3.  Predicting pathogenicity of missense variants with weakly supervised regression.

Authors:  Yue Cao; Yuanfei Sun; Mostafa Karimi; Haoran Chen; Oluwaseyi Moronfoye; Yang Shen
Journal:  Hum Mutat       Date:  2019-08-07       Impact factor: 4.878

4.  Determination of an effective scoring function for RNA-RNA interactions with a physics-based double-iterative method.

Authors:  Yumeng Yan; Zeyu Wen; Di Zhang; Sheng-You Huang
Journal:  Nucleic Acids Res       Date:  2018-05-18       Impact factor: 16.971

5.  Pushing the Backbone in Protein-Protein Docking.

Authors:  Daisuke Kuroda; Jeffrey J Gray
Journal:  Structure       Date:  2016-08-25       Impact factor: 5.006

6.  iCFN: an efficient exact algorithm for multistate protein design.

Authors:  Mostafa Karimi; Yang Shen
Journal:  Bioinformatics       Date:  2018-09-01       Impact factor: 6.937

  6 in total

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