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    MUSTANG-MR Structural Sieving Server: Applications in Protein Structural Analysis and Crystallography

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    MUSTANG-MR Structural Sieving Server: Applications in Protein Structural Analysis and Crystallography (1.056Mb)

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    Author
    Konagurthu, AS; Reboul, CF; Schmidberger, JW; Irving, JA; Lesk, AM; Stuckey, PJ; Whisstock, JC; Buckle, AM
    Date
    2010-04-06
    Source Title
    PLOS ONE
    Publisher
    PUBLIC LIBRARY SCIENCE
    University of Melbourne Author/s
    Stuckey, Peter; KONAGURTHU, ARUN
    Affiliation
    Computer Science and Software Engineering
    Metadata
    Show full item record
    Document Type
    Journal Article
    Citations
    Konagurthu, A. S., Reboul, C. F., Schmidberger, J. W., Irving, J. A., Lesk, A. M., Stuckey, P. J., Whisstock, J. C. & Buckle, A. M. (2010). MUSTANG-MR Structural Sieving Server: Applications in Protein Structural Analysis and Crystallography. PLOS ONE, 5 (3), https://doi.org/10.1371/journal.pone.0010048.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/29331
    DOI
    10.1371/journal.pone.0010048
    Open Access at PMC
    http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2850368
    Abstract
    BACKGROUND: A central tenet of structural biology is that related proteins of common function share structural similarity. This has key practical consequences for the derivation and analysis of protein structures, and is exploited by the process of "molecular sieving" whereby a common core is progressively distilled from a comparison of two or more protein structures. This paper reports a novel web server for "sieving" of protein structures, based on the multiple structural alignment program MUSTANG. METHODOLOGY/PRINCIPAL FINDINGS: "Sieved" models are generated from MUSTANG-generated multiple alignment and superpositions by iteratively filtering out noisy residue-residue correspondences, until the resultant correspondences in the models are optimally "superposable" under a threshold of RMSD. This residue-level sieving is also accompanied by iterative elimination of the poorly fitting structures from the input ensemble. Therefore, by varying the thresholds of RMSD and the cardinality of the ensemble, multiple sieved models are generated for a given multiple alignment and superposition from MUSTANG. To aid the identification of structurally conserved regions of functional importance in an ensemble of protein structures, Lesk-Hubbard graphs are generated, plotting the number of residue correspondences in a superposition as a function of its corresponding RMSD. The conserved "core" (or typically active site) shows a linear trend, which becomes exponential as divergent parts of the structure are included into the superposition. CONCLUSIONS: The application addresses two fundamental problems in structural biology: first, the identification of common substructures among structurally related proteins--an important problem in characterization and prediction of function; second, generation of sieved models with demonstrated uses in protein crystallographic structure determination using the technique of Molecular Replacement.
    Keywords
    Artificial Intelligence and Image Processing

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