MUSTANG-MR Structural Sieving Server: Applications in Protein Structural Analysis and Crystallography
AuthorKonagurthu, AS; Reboul, CF; Schmidberger, JW; Irving, JA; Lesk, AM; Stuckey, PJ; Whisstock, JC; Buckle, AM
Source TitlePLoS One
PublisherPUBLIC LIBRARY SCIENCE
AffiliationComputer Science and Software Engineering
Document TypeJournal Article
CitationsKonagurthu, 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 StatusOpen Access
Open Access at PMChttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC2850368
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.
KeywordsArtificial Intelligence and Image Processing
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