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

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/

Citation
Cui, X., Kuwahara, H., Li, S. C., & Gao, X. (2015). Compare local pocket and global protein structure models by small structure patterns. Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics - BCB ’15. doi:10.1145/2808719.2808756

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

DOI
10.1145/2808719.2808756

Additional Links
http://dl.acm.org/citation.cfm?doid=2808719.2808756

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