8952

ClusCo: clustering and comparison of protein models

Jamroz Michal, Kolinski Andrzej
Laboratory of Theory of Biopolymers, Faculty of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
arXiv:1302.4000 [q-bio.BM], (16 Feb 2013)
@article{2013arXiv1302.4000M,

   author={Micha{l}}, J. and {Andrzej}, K.},

   title={"{ClusCo: clustering and comparison of protein models}"},

   journal={ArXiv e-prints},

   archivePrefix={"arXiv"},

   eprint={1302.4000},

   primaryClass={"q-bio.BM"},

   keywords={Quantitative Biology – Biomolecules, Computer Science – Computational Engineering, Finance, and Science, Quantitative Biology – Quantitative Methods},

   year={2013},

   month={feb},

   adsurl={http://adsabs.harvard.edu/abs/2013arXiv1302.4000M},

   adsnote={Provided by the SAO/NASA Astrophysics Data System}

}

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BACKGROUND: The development, optimization and validation of protein modeling methods require efficient tools for structural comparison. Frequently, a large number of models need to be compared with the target native structure. The main reason for the development of Clusco software was to create a high-throughput tool for all-versus-all comparison, because calculating similarity matrix is the one of the bottlenecks in the protein modeling pipeline. RESULTS: Clusco is fast and easy-to-use software for high-throughput comparison of protein models with different similarity measures (cRMSD, dRMSD, GDT_TS, TM-Score, MaxSub, Contact Map Overlap) and clustering of the comparison results with standard methods: K-means Clustering or Hierarchical Agglomerative Clustering. CONCLUSIONS: The application was highly optimized and written in C/C++, including the code for parallel execution on CPU and GPU, which resulted in a significant speedup over similar clustering and scoring computation programs.
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