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dc.contributor.authorKonagurthu, AS
dc.contributor.authorReboul, CF
dc.contributor.authorSchmidberger, JW
dc.contributor.authorIrving, JA
dc.contributor.authorLesk, AM
dc.contributor.authorStuckey, PJ
dc.contributor.authorWhisstock, JC
dc.contributor.authorBuckle, AM
dc.date.available2014-05-21T22:54:41Z
dc.date.available2010-03-16
dc.date.available2010-03-16
dc.date.available2010-03-16
dc.date.available2010-03-16
dc.date.available2010-03-16
dc.date.available2010-03-16
dc.date.available2010-03-16
dc.date.available2010-03-16
dc.date.available2010-03-16
dc.date.available2010-03-16
dc.date.available2010-03-16
dc.date.issued2010-04-06
dc.identifierhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000276420400017&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=d4d813f4571fa7d6246bdc0dfeca3a1c
dc.identifierARTN e10048
dc.identifier.citationKonagurthu, 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.
dc.identifier.issn1932-6203
dc.identifier.urihttp://hdl.handle.net/11343/29331
dc.description.abstractBACKGROUND: 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.
dc.languageEnglish
dc.publisherPUBLIC LIBRARY SCIENCE
dc.subjectArtificial Intelligence and Image Processing
dc.titleMUSTANG-MR Structural Sieving Server: Applications in Protein Structural Analysis and Crystallography
dc.typeJournal Article
dc.identifier.doi10.1371/journal.pone.0010048
melbourne.peerreviewPeer Reviewed
melbourne.affiliationThe University of Melbourne
melbourne.affiliation.departmentComputer Science and Software Engineering
melbourne.source.titlePLOS ONE
melbourne.source.volume5
melbourne.source.issue3
dc.rights.licenseCC BY
dc.description.pagestart1
melbourne.publicationid144067
melbourne.elementsid322146
melbourne.openaccess.pmchttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC2850368
melbourne.contributor.authorStuckey, Peter
melbourne.contributor.authorKONAGURTHU, ARUN
dc.identifier.eissn1932-6203
melbourne.accessrightsOpen Access


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