Abstract
Molecular Dynamics-based simulations have been employed to study the protein folding process, in which a protein acquires its functional three-dimensional structure. This has resulted in a large number of protein folding trajectories. As a result, it becomes increasingly important to analyze such data to facilitate a deeper understanding of the protein folding mechanism. In this paper, we focus on identifying important 3D structural motifs in the folding data. We have proposed a multi-step algorithm that is not only computationally efficient but also captures the evolving nature of the folding process. Empirical evaluation demonstrates that such motifs are effective at characterizing a protein’s structural evolution in its folding process. We also show that such motifs can be utilized to address important folding issues such as detecting important folding events, and structurally characterizing a folding pathway.
This work was partially supported by a Microsoft e-science grant. Correspondence should be addressed to Hui Yang.
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References
Atallah, M.J.: A linear time algorithm for the hausdorff distance between convex polygons. Information Processing Letters 17, 207–209 (1983)
Baldwin, R.L., Rose, G.D.: Is protein folding hierarchical? local structure and peptide folding. Trends in Biochemical Sciences 24(1), 26–33 (1999)
Berrar, D., Stahl, F., et al.: Towards data warehousing and mining of protein unfolding simulation data. J. of clin. monit. & comp. 19(4-5), 307–317 (2005)
Ferreira, P.G., Silva, C.G., et al.: A closer look on protein unfolding simulations through hierarchical clustering. In: Proceedings of CIBCB (2007)
Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness (1979)
Germain, R.S., et al.: Blue matter on blue gene/l: massively parallel computation for biomolecular simulation. In: Proc.of the 3rd IEEE/ACM/IFIP int’l conf. on HW/SW codesign & sys. syn., pp. 207–212 (2005)
Kabsch, W., Sander, C.: Dictionary of protein secondary structures. Biopolymers 22, 2577–2637 (1983)
MacQueen, J.: Some methods for classification and analysis of multivariate observation. In: Proc. of the 5th Berkeley Symposium on Math. Stat. and Prob., vol. 1, pp. 281–297 (1967)
Parida, L., Zhou, R.: Combinatorial pattern discovery approach for the folding trajectory analysis of a beta-hairpin. PLoS Comput. Biol. 1 (June 2005)
Russel, D., Guibas, L.: Exploring protein folding trajectories using geometric spanners. In: Pacific Symposium on Biocomputing, pp. 40–51 (2005)
Snow, C.D., Nguyen, H., et al.: Absolute comparison of simulated and experimental protein-folding dynamics. Nature 420, 102–106 (2002)
Snow, C.D., Sorin, E.J., et al.: How well can simulation predict protein folding kinetics and thermodynamics? Ann. Rev. Biophys. BioMol. Struct. 34, 43–69 (2005)
Yang, H., Parthasarathy, S., et al.: Towards association based spatio-temporal reasoning. In: Proc. the 19th IJCAI Workshop on Spatio-temporal Reasoning (2005)
Yang, H., Marsolo, K., et al.: Discovering spatial relationships between approximately equivalent patterns. In: BIOKDD, pp. 62–71 (2004)
Yang, H., Parthasarathy, S., Ucar, D.: A spatio-temporal mining approach towards summarizing and analyzing protein folding trajectories. J. of A.M.B. 2 (2007)
Yang, H., Parthasarathy, S., Mehta, S.: A generalized framework for mining spatio-temporal patterns in scientific data. In: Proceeding of ACM SIGKDD, pp. 716–721 (2005)
Zagrovic, B., Snow, C.D., et al.: In simulation of folding of a small alpha-helical protein in atomistic detail using worldwide distributed computing. J. Mol. Biol. (2002)
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Yang, H., Han, L. (2008). An Effective Approach for Identifying Evolving Three-Dimensional Structural Motifs in Protein Folding Data. In: Tang, C., Ling, C.X., Zhou, X., Cercone, N.J., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2008. Lecture Notes in Computer Science(), vol 5139. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88192-6_32
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DOI: https://doi.org/10.1007/978-3-540-88192-6_32
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