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

Handle URI:
http://hdl.handle.net/10754/579601
Title:
Compare local pocket and global protein structure models by small structure patterns
Authors:
Cui, Xuefeng; Kuwahara, Hiroyuki; Li, Shuai Cheng; Gao, Xin ( 0000-0002-7108-3574 )
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/
KAUST Department:
Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Publisher:
Association for Computing Machinery (ACM)
Journal:
Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics - BCB '15
Conference/Event name:
BCB '15 Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics
Issue Date:
9-Sep-2015
DOI:
10.1145/2808719.2808756
Type:
Conference Paper
Additional Links:
http://dl.acm.org/citation.cfm?doid=2808719.2808756
Appears in Collections:
Conference Papers; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorCui, Xuefengen
dc.contributor.authorKuwahara, Hiroyukien
dc.contributor.authorLi, Shuai Chengen
dc.contributor.authorGao, Xinen
dc.date.accessioned2015-10-13T12:58:33Zen
dc.date.available2015-10-13T12:58:33Zen
dc.date.issued2015-09-09en
dc.identifier.doi10.1145/2808719.2808756en
dc.identifier.urihttp://hdl.handle.net/10754/579601en
dc.description.abstractResearchers 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/en
dc.publisherAssociation for Computing Machinery (ACM)en
dc.relation.urlhttp://dl.acm.org/citation.cfm?doid=2808719.2808756en
dc.titleCompare local pocket and global protein structure models by small structure patternsen
dc.typeConference Paperen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalProceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics - BCB '15en
dc.conference.dateSeptember 9–12, 2015en
dc.conference.nameBCB '15 Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informaticsen
dc.conference.locationAtlanta, GA, USAen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionCity University of Hong Kong, Kowloon, Hong Kongen
kaust.authorCui, Xuefengen
kaust.authorKuwahara, Hiroyukien
kaust.authorGao, Xinen
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