Abstract
Protein structure clustering is an important and essential step in protein 3D structure prediction. However, two issues limited current methods. But the large-scale candidate models in the decoy and undistinguished metric limit current methods to identify the near-native models. In this paper we proposed a novel method based on parallel multiple K-means cluster algorithms to identify the near-native structures. Parallel is introduced to reduce the memory and time consumption and multiple K-means to fusion different metrics of protein 3D similarity. Tested on 56 proteins, MK-means can well identify 33(58.9 %) proteins which are better or the same to SPICKER selected and 10 of the 33 proteins is the same results to the SPICKER. It indicates the performance of MK-means is similar to the top protein clustered tools SPICKER.
This paper is supported by grants no. 61540058, 61202290 under the National Natural Science Foundation of China (http://www.nsfc.gov.cn) and grants no. BK20131154 under Natural Science Foundation of Jiangsu Province.
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References
Huang, D.S., Zhang, L., Han, K., et al.: Prediction of protein-protein interactions based on protein-protein correlation using least squares regression. Curr. Protein Pept. Sci. 15(6), 553–560 (2014)
Wu, H., Lü, Q., Quan, L., et al.: patGPCR: a multitemplate approach for improving 3D structure prediction of transmembrane helices of G-protein-coupled receptors. Comput. Math. Methods Med. 2013(1), 151–164 (2013)
Yang, J., Yan, R., Roy, A., et al.: The I-TASSER suite: protein structure and function prediction. Nat. Methods 12(1), 7–8 (2014)
You, Z.H., Lei, Y.K., Zhu, L., et al.: Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis. BMC Bioinformatics 14(8), 69–75 (2013)
Ravinder, A., Bray, J.K., Goddard, W.A.: Bihelix: towards de novo structure prediction of an ensemble of G-protein coupled receptor conformations. Proteins Struct. Funct. Bioinformatics 80(2), 505–518 (2012)
Roy, A., Xu, D., Poisson, J., et al.: A protocol for computer-based protein structure and function prediction. J. Visualized Exp. 57(57), e3259–e3259 (2012)
Zhang, Y., Skolnick, J.: SPICKER: a clustering approach to identify near-native protein folds. J. Comput. Chem. 25(25), 865–871 (2004)
Jamroz, M., Kolinski, A.: ClusCo: Clustering and comparison of protein models. BMC Bioinformatics 14(1), 898 (2013)
Francois, B., Rojan, S., Yong, Z., et al.: Durandal: fast exact clustering of protein decoys. J. Comput. Chem. 33(4), 471–474 (2012)
Zhu, L., Huang, D.S.: A Rayleigh-Ritz style method for large-scale discriminant analysis. Pattern Recogn. 47(4), 1698–1708 (2014)
Tim, H., Mikael, B., Wouter, B., et al.: Fast large-scale clustering of protein structures using Gauss integrals. Bioinformatics 28(4), 510–515 (2012)
Zhang, J., Xu, D.: Fast Algorithm for Clustering a Large Number of Protein Structural Decoys. In: Proceedings of the 2011 IEEE International Conference on Bioinformatics and Biomedicine, pp. 30–36. IEEE Computer Society (2011)
Zhang, Y., Skolnick, J.: Scoring function for automated assessment of protein structure template quality. Proteins Struct. Funct. Bioinformatics 68(4), 702–710 (2007)
Shatabda, S., Newton, M.A., Rashid, M.A., et al.: How good are simplified models for protein structure prediction? Adv. Bioinformatics 2014, 867179 (2014)
Zhou, J., Wishart, D.S.: An improved method to detect correct protein folds using partial clustering. BMC Bioinformatics 14(1), 101 (2013)
Tan, C.W., Jones, D.T.: Using neural networks and evolutionary information in decoy discrimination for protein tertiary structure prediction. BMC Bioinformatics 9(4), 1–23 (2008)
Wu, S., Skolnick, J., Zhang, Y.: Ab initio modeling of small proteins by iterative TASSER simulations. BMC Biol. 5, 17 (2007)
Kabsch, W.: A solution for the best rotation to relate two sets of vectors. Acta Cryst A32, 922–923 (1976)
Kabsch, W.: A discussion of the solution for the best rotation to relate two sets of vectors. Acta Cryst A34, 827–828 (1978)
Dehzangi, A., Paliwal, K., Lyons, J., et al.: Proposing a highly accurate protein structural class predictor using segmentation-based features. BMC Genom. 15(Suppl 1), 133–139 (2014)
Levitt, M., Gerstein, M.: A unified statistical framework for sequence comparison and structure comparison. Proc. Nat. Acad. Sci. U.S.A. 95(11), 5913–5920 (1998)
Zhang, J., Xu, D.: Fast algorithm for population-based protein structural model analysis. Proteomics 13(2), 221–229 (2013)
Dagum, L., Menon, R.: OpenMP: an industry-standard API for shared-memory programming. IEEE Comput. Sci. Eng. 5(1), 46–55 (1998)
Jain, Anil K.: Data Clustering: 50 Years Beyond K-means. Pattern Recogn. Lett. 31(8), 651–666 (2015)
Huang, T., Lu, D.T., Li, X., et al.: GPU-based SNESIM implementation for multiple-point statistical simulation. Comput. Geosci. 54(4), 75–87 (2013)
Acknowledgments
This paper is supported by grants no. 61540058, 61202290 under the National Natural Science Foundation of China (http://www.nsfc.gov.cn) and grants no. BK20131154 under Natural Science Foundation of Jiangsu Province. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the paper. Chuang Wu and Longfei Song wrote the codes, paper and implemented the experiments, Hongjie Wu designed the algorithm, experiments and wrote the paper, Min Jiang prepared the datasets.
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Wu, H., Wu, C., cheng, C., Song, L., Jiang, M. (2016). A Parallel Multiple K-Means Clustering and Application on Detect Near Native Model. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9772. Springer, Cham. https://doi.org/10.1007/978-3-319-42294-7_78
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