Abstract
BackgroundA 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.ConclusionsThe 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.
Highlights
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
Prediction of protein function Understanding structural similarity between proteins within a homologous family as well as between distant or even unrelated proteins is a common task in molecular biology
In this work we describe a novel web server for ‘‘sieving’’ of protein structures, based on the multiple structural alignment program MUSTANG
Summary
Prediction of protein function Understanding structural similarity between proteins within a homologous family as well as between distant or even unrelated proteins is a common task in molecular biology. The prediction of protein function remains a serious challenge, but the observation that the active sites of proteins are often the best preserved regions in a divergent family offers a robust method of classifying proteins of unknown function by structural alignment. This has been exploited by a ‘‘sieving’’ procedure that iteratively identifies matching residues in a multiple structural alignment that fit below a threshold root-mean-square-deviation (RMSD [1,2]). This paper reports a novel web server for ‘‘sieving’’ of protein structures, based on the multiple structural alignment program MUSTANG
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