research-article

Compare local pocket and global protein structure models by small structure patterns

Authors:

Hiroyuki Kuwahara,

Shuai Cheng Li,

Xin GaoAuthors Info & Claims

BCB '15: Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics

Pages 355 - 365

https://doi.org/10.1145/2808719.2808756

Published: 09 September 2015 Publication History

Abstract

Researchers proposed several criteria to assess the quality of predicted protein structures because it is one of the essential tasks in the Critical Assessment of Techniques for Protein Structure Prediction (CASP) competitions. Popular criteria include root mean squared deviation (RMSD), MaxSub score, TM-score, GDT-TS and GDT-HA scores. All these criteria require calculation of rigid transformations to superimpose the the predicted protein structure to the native protein structure. Yet, how to obtain the rigid transformations is unknown or with high time complexity, and, hence, heuristic algorithms were proposed.

In this work, we carefully design various small structure patterns, including the ones specifically tuned for local pockets. Such structure patterns are biologically meaningful, and address the issue of relying on a sufficient number of backbone residue fragments for existing methods. We sample the rigid transformations from these small structure patterns; and the optimal superpositions yield by these small structures are refined and reported. As a result, among 11; 669 pairs of predicted and native local protein pocket models from the CASP10 dataset, the GDT-TS scores calculated by our method are significantly higher than those calculated by LGA. Moreover, our program is computationally much more efficient.

Source codes and executables are publicly available at http://www.cbrc.kaust.edu.sa/prosta/

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Cited By

Cui XMin Y(2019)Homologous Protein DetectionEncyclopedia of Bioinformatics and Computational Biology10.1016/B978-0-12-809633-8.90698-8(697-705)Online publication date: 2019
https://doi.org/10.1016/B978-0-12-809633-8.90698-8
Budowski-Tal IKolodny RMandel-Gutfreund Y(2018)A Novel Geometry-Based Approach to Infer Protein Interface SimilarityScientific Reports10.1038/s41598-018-26497-z8:1Online publication date: 29-May-2018
https://doi.org/10.1038/s41598-018-26497-z
Cui XLu ZWang SJing-Yan Wang JGao X(2016)CMsearch: simultaneous exploration of protein sequence space and structure space improves not only protein homology detection but also protein structure predictionBioinformatics10.1093/bioinformatics/btw27132:12(i332-i340)Online publication date: 15-Jun-2016
https://doi.org/10.1093/bioinformatics/btw271

Index Terms

Compare local pocket and global protein structure models by small structure patterns

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cover image ACM Conferences

BCB '15: Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics

September 2015

683 pages

ISBN:9781450338530

DOI:10.1145/2808719

Copyright © 2015 ACM.

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Published: 09 September 2015

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King Abdullah University of Science and Technology (KAUST)

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BCB '15

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SIGBio

BCB '15: ACM International Conference on Bioinformatics, Computational Biology and Biomedicine

September 9 - 12, 2015

Georgia, Atlanta

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BCB '15 Paper Acceptance Rate 48 of 141 submissions, 34%;

Overall Acceptance Rate 254 of 885 submissions, 29%

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Cited By

Cui XMin Y(2019)Homologous Protein DetectionEncyclopedia of Bioinformatics and Computational Biology10.1016/B978-0-12-809633-8.90698-8(697-705)Online publication date: 2019
https://doi.org/10.1016/B978-0-12-809633-8.90698-8
Budowski-Tal IKolodny RMandel-Gutfreund Y(2018)A Novel Geometry-Based Approach to Infer Protein Interface SimilarityScientific Reports10.1038/s41598-018-26497-z8:1Online publication date: 29-May-2018
https://doi.org/10.1038/s41598-018-26497-z
Cui XLu ZWang SJing-Yan Wang JGao X(2016)CMsearch: simultaneous exploration of protein sequence space and structure space improves not only protein homology detection but also protein structure predictionBioinformatics10.1093/bioinformatics/btw27132:12(i332-i340)Online publication date: 15-Jun-2016
https://doi.org/10.1093/bioinformatics/btw271

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