ArticlePLoS computational biology2009
Towards prediction of metabolic products of polyketide synthases: an in silico analysis.
Article in PLoS computational biology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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36 citing papers in PubMed, 95 citations in OpenAlex.
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- antiSMASH 8.0: extended gene cluster detection capabilities and analyses of chemistry, enzymology, and regulation.Nucleic acids research · 2025Article
- A multi-omics reciprocal analysis for characterization of bacterial metabolism.Frontiers in molecular biosciences · 2025Article
- antiSMASH 7.0: new and improved predictions for detection, regulation, chemical structures and visualisation.Nucleic acids research · 2023Article
- The Ketosynthase Domain Controls Chain Length in Mushroom Oligocyclic Polyketide Synthases.Chembiochem : a European journal of chemical biology · 2023Article
- Structural basis of the complementary activity of two ketosynthases in aryl polyene biosynthesis.Scientific reports · 2021Article
- antiSMASH 6.0: improving cluster detection and comparison capabilities.Nucleic acids research · 2021Article
- SeMPI 2.0-A Web Server for PKS and NRPS Predictions Combined with Metabolite Screening in Natural Product Databases.Metabolites · 2020Article
- Predicting the chemical space of fungal polyketides by phylogeny-based bioinformatics analysis of polyketide synthase-nonribosomal peptide synthetase and its modification enzymes.Scientific reports · 2020Article
- DDAP: docking domain affinity and biosynthetic pathway prediction tool for type I polyketide synthases.Bioinformatics (Oxford, England) · 2020Article
- antiSMASH 5.0: updates to the secondary metabolite genome mining pipeline.Nucleic acids research · 2019Article
- SeMPI: a genome-based secondary metabolite prediction and identification web server.Nucleic acids research · 2017Article
- Metabolic engineering ofMetabolic engineering communications · 2016Article
- Article
- Phylogenetic and Structural Analysis of Polyketide Synthases in Aspergilli.Evolutionary bioinformatics online · 2016Article
- Ketide Synthase (KS) Domain Prediction and Analysis of Iterative Type II PKS Gene in Marine Sponge-Associated Actinobacteria Producing Biosurfactants and Antimicrobial Agents.Frontiers in microbiology · 2016Article
- Metabolic Network Modeling of Microbial Interactions in Natural and Engineered Environmental Systems.Frontiers in microbiology · 2016Review
- Bioinformatics Prediction of Polyketide Synthase Gene Clusters from Mycosphaerella fijiensis.PloS one · 2016Article
- Evolution of chemical diversity by coordinated gene swaps in type II polyketide gene clusters.Proceedings of the National Academy of Sciences of the United States of America · 2015Article
- Article
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3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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