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dc.contributor.authorYadav, Gitanjali-
dc.contributor.authorGokhale, Rajesh S.-
dc.contributor.authorMohanty, Debasisa-
dc.date.accessioned2014-02-13T10:28:23Z-
dc.date.available2014-02-13T10:28:23Z-
dc.date.issued2009-
dc.identifier.citationPLoS Comp. Biol., 5(4): e1000351en_US
dc.identifier.urihttp://hdl.handle.net/123456789/133-
dc.description.abstractSequence data arising from an increasing number of partial and complete genome projects is revealing the presence of the polyketide synthase (PKS) family of genes not only in microbes and fungi but also in plants and other eukaryotes. PKSs are huge multifunctional megasynthases that use a variety of biosynthetic paradigms to generate enormously diverse arrays of polyketide products that posses several pharmaceutically important properties. The remarkable conservation of these gene clusters across organisms offers abundant scope for obtaining novel insights into PKS biosynthetic code by computational analysis. We have carried out a comprehensive in silico analysis of modular and iterative gene clusters to test whether chemical structures of the secondary metabolites can be predicted from PKS protein sequences. Here, we report the success of our method and demonstrate the feasibility of deciphering the putative metabolic products of uncharacterized PKS clusters found in newly sequenced genomes. Profile Hidden Markov Model analysis has revealed distinct sequence features that can distinguish modular PKS proteins from their iterative counterparts. For iterative PKS proteins, structural models of iterative ketosynthase (KS) domains have revealed novel correlations between the size of the polyketide products and volume of the active site pocket. Furthermore, we have identified key residues in the substrate binding pocket that control the number of chain extensions in iterative PKSs. For modular PKS proteins, we describe for the first time an automated method based on crucial intermolecular contacts that can distinguish the correct biosynthetic order of substrate channeling from a large number of non-cognate combinatorial possibilities. Taken together, our in silico analysis provides valuable clues for formulating rules for predicting polyketide products of iterative as well as modular PKS clusters. These results have promising potential for discovery of novel natural products by genome mining and rational design of novel natural products.en_US
dc.description.sponsorshipThe work has been supported by grants to the National Institute of Immunology from Department of Biotechnology (DBT), Government of India, and research grants to DM from BTIS project of DBT, India. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.en_US
dc.language.isoenen_US
dc.publisherPLOSen_US
dc.subjectIn Silico Analysisen_US
dc.subjectPolyketide Synthasesen_US
dc.titleTowards prediction of metabolic products of polyketide synthases: An in silico analysisen_US
dc.typeArticleen_US
dc.date.AcceptedDate9 March 2009en_US
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