Abstract
The protein fragment libraries play an important role in a wide variety of structural biology applications. In this work, we present the use of a spectral clustering algorithm to analyze the fixed-length protein backbone fragment sets derived from the continuously growing Protein Data Bank (PDB) to construct libraries of protein fragments. Incorporating the rank-revealing randomized singular value decomposition algorithm into spectral clustering to fast approximate the dominant eigenvectors of the fragment affinity matrix enables the clustering algorithm to handle large-scale fragment sample sets. Compared to the popularly used protein fragment libraries developed by Kolodny et al., the fragments in our new libraries exhibit better representability across diverse protein structures in PDB. Moreover, using much larger fragment sample sets, libraries of longer fragments with length up to 20 residues are also generated. Our fragment libraries can be found at http://hpcr.cs.odu.edu/frag/.
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Acknowledgements
Y. Li acknowledges support from National Science Foundation through Grant No. CCF-1066471. W. Elhefnawy acknowledges support from Old Dominion University Modeling and Simulation Fellowship.
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Elhefnawy, W., Li, M., Wang, J., Li, Y. (2017). Construction of Protein Backbone Fragments Libraries on Large Protein Sets Using a Randomized Spectral Clustering Algorithm. In: Cai, Z., Daescu, O., Li, M. (eds) Bioinformatics Research and Applications. ISBRA 2017. Lecture Notes in Computer Science(), vol 10330. Springer, Cham. https://doi.org/10.1007/978-3-319-59575-7_10
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