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    MUSTANG: A multiple structural alignment algorithm

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    Author
    Konagurthu, AS; Whisstock, JC; Stuckey, PJ; Lesk, AM
    Date
    2006-08-15
    Source Title
    PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
    Publisher
    WILEY
    University of Melbourne Author/s
    Stuckey, Peter; KONAGURTHU, ARUN SIDDHARTHA
    Affiliation
    Computer Science and Software Engineering
    Metadata
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    Document Type
    Journal Article
    Citations
    Konagurthu, A. S., Whisstock, J. C., Stuckey, P. J. & Lesk, A. M. (2006). MUSTANG: A multiple structural alignment algorithm. PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS, 64 (3), pp.559-574. https://doi.org/10.1002/prot.20921.
    Access Status
    This item is currently not available from this repository
    URI
    http://hdl.handle.net/11343/29336
    DOI
    10.1002/prot.20921
    Abstract
    Multiple structural alignment is a fundamental problem in structural genomics. In this article, we define a reliable and robust algorithm, MUSTANG (MUltiple STructural AligNment AlGorithm), for the alignment of multiple protein structures. Given a set of protein structures, the program constructs a multiple alignment using the spatial information of the C(alpha) atoms in the set. Broadly based on the progressive pairwise heuristic, this algorithm gains accuracy through novel and effective refinement phases. MUSTANG reports the multiple sequence alignment and the corresponding superposition of structures. Alignments generated by MUSTANG are compared with several handcurated alignments in the literature as well as with the benchmark alignments of 1033 alignment families from the HOMSTRAD database. The performance of MUSTANG was compared with DALI at a pairwise level, and with other multiple structural alignment tools such as POSA, CE-MC, MALECON, and MultiProt. MUSTANG performs comparably to popular pairwise and multiple structural alignment tools for closely related proteins, and performs more reliably than other multiple structural alignment methods on hard data sets containing distantly related proteins or proteins that show conformational changes.
    Keywords
    Artificial Intelligence and Image Processing

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