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Issue No. 01 - January/February (2012 vol. 9)
ISSN: 1545-5963
pp: 286-293
Noah Daniels , Tufts University, Medford
Anoop Kumar , Tufts University, Medford
Lenore Cowen , Tufts University, Medford
Matt Menke , Tufts University, Medford
Using the Matt structure alignment program, we take a tour of protein space, producing a hierarchical clustering scheme that divides protein structural domains into clusters based on geometric dissimilarity. While it was known that purely structural, geometric, distance-based measures of structural similarity, such as Dali/FSSP, could largely replicate hand-curated schemes such as SCOP at the family level, it was an open question as to whether any such scheme could approximate SCOP at the more distant superfamily and fold levels. We partially answer this question in the affirmative, by designing a clustering scheme based on Matt that approximately matches SCOP at the superfamily level, and demonstrates qualitative differences in performance between Matt and DaliLite. Implications for the debate over the organization of protein fold space are discussed. Based on our clustering of protein space, we introduce the Mattbench benchmark set, a new collection of structural alignments useful for testing sequence aligners on more distantly homologous proteins.
Proteins, Indexes, Benchmark testing, Clustering algorithms, Measurement, Training, Bioinformatics

N. Daniels, A. Kumar, L. Cowen and M. Menke, "Touring Protein Space with Matt," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. 1, pp. 286-293, 2012.
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