Dataset Information

PAR-3D: a server to predict protein active site residues.

ABSTRACT: PAR-3D (http://sunserver.cdfd.org.in:8080/protease/PAR_3D/index.html) is a web-based tool that exploits the fact that relative juxtaposition of active site residues is a conserved feature in functionally related protein families. The server uses previously calculated and stored values of geometrical parameters of a set of known proteins (training set) for prediction of active site residues in a query protein structure. PAR-3D stores motifs for different classes of proteases, the ten glycolytic pathway enzymes and metal-binding sites. The server accepts the structures in the pdb format. The first step during the prediction is the extraction of probable active site residues from the query structure. Spatial arrangement of the probable active site residues is then determined in terms of geometrical parameters. These are compared with stored geometries of the different motifs. Its speed and efficiency make it a beneficial tool for structural genomics projects, especially when the biochemical function of the protein has not been characterized.

SUBMITTER: Goyal K

PROVIDER: S-EPMC1933233 | biostudies-literature | 2007 Jul

REPOSITORIES: biostudies-literature

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PAR-3D: a server to predict protein active site residues.

Goyal Kshama K Mohanty Debasisa D Mande Shekhar C SC

Nucleic acids research 20070503 Web Server issue

PAR-3D (http://sunserver.cdfd.org.in:8080/protease/PAR_3D/index.html) is a web-based tool that exploits the fact that relative juxtaposition of active site residues is a conserved feature in functionally related protein families. The server uses previously calculated and stored values of geometrical parameters of a set of known proteins (training set) for prediction of active site residues in a query protein structure. PAR-3D stores motifs for different classes of proteases, the ten glycolytic p ...[more]

PMID: 17478506

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Project description:A new monotonicity-constrained maximum likelihood approach, called Partial Order Optimum Likelihood (POOL), is presented and applied to the problem of functional site prediction in protein 3D structures, an important current challenge in genomics. The input consists of electrostatic and geometric properties derived from the 3D structure of the query protein alone. Sequence-based conservation information, where available, may also be incorporated. Electrostatics features from THEMATICS are combined with multidimensional isotonic regression to form maximum likelihood estimates of probabilities that specific residues belong to an active site. This allows likelihood ranking of all ionizable residues in a given protein based on THEMATICS features. The corresponding ROC curves and statistical significance tests demonstrate that this method outperforms prior THEMATICS-based methods, which in turn have been shown previously to outperform other 3D-structure-based methods for identifying active site residues. Then it is shown that the addition of one simple geometric property, the size rank of the cleft in which a given residue is contained, yields improved performance. Extension of the method to include predictions of non-ionizable residues is achieved through the introduction of environment variables. This extension results in even better performance than THEMATICS alone and constitutes to date the best functional site predictor based on 3D structure only, achieving nearly the same level of performance as methods that use both 3D structure and sequence alignment data. Finally, the method also easily incorporates such sequence alignment data, and when this information is included, the resulting method is shown to outperform the best current methods using any combination of sequence alignments and 3D structures. Included is an analysis demonstrating that when THEMATICS features, cleft size rank, and alignment-based conservation scores are used individually or in combination THEMATICS features represent the single most important component of such classifiers.

Project description:In two-component signal transduction, response regulator proteins contain the catalytic machinery for their own covalent phosphorylation and can catalyze phosphotransfer from a partner sensor kinase or autophosphorylate using various small molecule phosphodonors. Although response regulator autophosphorylation is physiologically relevant and a powerful experimental tool, the kinetic determinants of the autophosphorylation reaction and how those determinants might vary for different response regulators and phosphodonors are largely unknown. We characterized the autophosphorylation kinetics of 21 variants of the model response regulator Escherichia coli CheY that contained substitutions primarily at nonconserved active site positions D + 2 (CheY residue 59) and T + 2 (CheY residue 89), two residues C-terminal to conserved D57 and T87, respectively. Overall, the CheY variants exhibited a >10(5)-fold range of rate constants (kphos/KS) for reaction with phosphoramidate, acetyl phosphate, or monophosphoimidazole, with the great majority of rates enhanced versus that of wild-type CheY. Although phosphodonor preference varied substantially, nearly all the CheY variants reacted faster with phosphoramidate than acetyl phosphate. Correlation between the increased positive charge of the D + 2 and T + 2 side chains and faster rates indicated electrostatic interactions are a kinetic determinant. Moreover, sensitivities of rate constants to ionic strength indicated that both long-range and localized electrostatic interactions influence autophosphorylation kinetics. The increased nonpolar surface area of the D + 2 and T + 2 side chains also correlated with an enhanced autophosphorylation rate, especially for reaction with phosphoramidate and monophosphoimidazole. Computer docking suggested that highly accelerated monophosphoimidazole autophosphorylation rates for CheY variants with a tyrosine at position T + 2 likely reflect structural mimicry of phosphotransfer from the sensor kinase histidyl phosphate.

Dataset Information

PAR-3D: a server to predict protein active site residues.

Publications

PAR-3D: a server to predict protein active site residues.

Similar Datasets

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