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Towards prediction of metabolic products of polyketide synthases: An in silico analysis

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Title Towards prediction of metabolic products of polyketide synthases: An in silico analysis
 
Creator Yadav, Gitanjali
Gokhale, Rajesh S.
Mohanty, Debasisa
 
Subject In Silico Analysis
Polyketide Synthases
 
Description Sequence 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.
The 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.
 
Date 2014-02-13T10:28:23Z
2014-02-13T10:28:23Z
2009
9 March 2009
 
Type Article
 
Identifier PLoS Comp. Biol., 5(4): e1000351
http://hdl.handle.net/123456789/133
 
Language en
 
Publisher PLOS