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dc.contributor.authorZhang, Yang
dc.contributor.authorSkolnick, Jeffrey
dc.date.accessioned2009-01-28T16:15:27Z
dc.date.available2009-01-28T16:15:27Z
dc.date.issued2005-04-22
dc.identifier.citationNucleic Acids Research 2005 33(7):2302-2309
dc.identifier.issn0305-1048
dc.identifier.urihttp://hdl.handle.net/1853/26730
dc.description©The Author 2005. Published by Oxford University Press. All rights reserved.. The definitive version is available online at: http://nar.oxfordjournals.org/cgi/content/full/33/7/2302en
dc.descriptiondoi:10.1093/nar/gki524
dc.description.abstractWe have developed TM-align, a new algorithm to identify the best structural alignment between protein pairs that combines the TM-score rotation matrix and Dynamic Programming (DP). The algorithm is ~4 times faster than CE and 20 times faster than DALI and SAL. On average, the resulting structure alignments have higher accuracy and coverage than those provided by these most often-used methods. TM-align is applied to an all-against-all structure comparison of 10 515 representative protein chains from the Protein Data Bank (PDB) with a sequence identity cutoff,95%: 1996 distinct folds are found when a TM-score threshold of 0.5 is used. We also use TM-align to match the models predicted by TASSER for solved non-homologous proteins in PDB. For both folded and misfolded models, TM-align can almost always find close structural analogs, with an average root mean square deviation, RMSD, of 3 A° and 87% alignment coverage. Nevertheless, there exists a significant correlation between the correctness of the predicted structure and the structural similarity of the model to the other proteins in the PDB. This correlation could be used to assist in model selection in blind protein structure predictions.en
dc.language.isoen_USen
dc.publisherGeorgia Institute of Technologyen
dc.subjectTM-alignen
dc.subjectAlgorithmsen
dc.subjectProtein structure comparisonen
dc.subjectProtein structure predictionen
dc.titleTM-align: a protein structure alignment algorithm based on the TM-scoreen
dc.typeArticleen
dc.contributor.corporatenameState University of New York at Buffalo. Center of Excellence in Bioinformatics
dc.publisher.originalOxford University Press


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