Distribution and prediction of catalytic domains in 2-oxoglutarate dependent dioxygenases

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Date: Aug. 4, 2012
From: BMC Research Notes(Vol. 5, Issue 1)
Publisher: BioMed Central Ltd.
Document Type: Report
Length: 10,260 words
Lexile Measure: 1380L

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Abstract :

Background The 2-oxoglutarate dependent superfamily is a diverse group of non-haem dioxygenases, and is present in prokaryotes, eukaryotes, and archaea. The enzymes differ in substrate preference and reaction chemistry, a factor that precludes their classification by homology studies and electronic annotation schemes alone. In this work, I propose and explore the rationale of using substrates to classify structurally similar alpha-ketoglutarate dependent enzymes. Findings Differential catalysis in phylogenetic clades of 2-OG dependent enzymes, is determined by the interactions of a subset of active-site amino acids. Identifying these with existing computational methods is challenging and not feasible for all proteins. A clustering protocol based on validated mechanisms of catalysis of known molecules, in tandem with group specific hidden markov model profiles is able to differentiate and sequester these enzymes. Access to this repository is by a web server that compares user defined unknown sequences to these pre-defined profiles and outputs a list of predicted catalytic domains. The server is free and is accessible at the following URL (http://comp-biol.theacms.in/H2OGpred.html). Conclusions The proposed stratification is a novel attempt at classifying and predicting 2-oxoglutarate dependent function. In addition, the server will provide researchers with a tool to compare their data to a comprehensive list of HMM profiles of catalytic domains. This work, will aid efforts by investigators to screen and characterize putative 2-OG dependent sequences. The profile database will be updated at regular intervals. Keywords: Hidden Markov Model, Facial triad, Ferryl, Dioxygenase

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Gale Document Number: GALE|A534465827