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A resource for the in silico identification of fungal polyketide synthases from predicted fungal proteomes.

ABSTRACT: The goal of this study was to develop a tool specifically designed to identify iterative polyketide synthases (iPKSs) from predicted fungal proteomes. A fungi-based PKS prediction model, specifically for fungal iPKSs, was developed using profile hidden Markov models (pHMMs) based on two essential iPKS domains, the ?-ketoacyl synthase (KS) domain and acyltransferase (AT) domain, derived from fungal iPKSs. This fungi-based PKS prediction model was initially tested on the well-annotated proteome of Fusarium graminearum, identifying 15 iPKSs that matched previous predictions and gene disruption studies. These fungi-based pHMMs were subsequently applied to the predicted fungal proteomes of Alternaria brassicicola, Fusarium oxysporum f.sp. lycopersici, Verticillium albo-atrum and Verticillium dahliae. The iPKSs predicted were compared against those predicted by the currently available mixed-kingdom PKS models that include both bacterial and fungal sequences. These mixed-kingdom models have been proven previously by others to be better in predicting true iPKSs from non-iPKSs compared with other available models (e.g. Pfam and TIGRFAM). The fungi-based model was found to perform significantly better on fungal proteomes than the mixed-kingdom PKS model in accuracy, sensitivity, specificity and precision. In addition, the model was capable of predicting the reducing nature of fungal iPKSs by comparison of the bit scores obtained from two separate reducing and nonreducing pHMMs for each domain, which was confirmed by phylogenetic analysis of the KS domain. Biological confirmation of the predictions was obtained by polymerase chain reaction (PCR) amplification of the KS and AT domains of predicted iPKSs from V. dahliae using domain-specific primers and genomic DNA, followed by sequencing of the PCR products. It is expected that the fungi-based PKS model will prove to be a useful tool for the identification and annotation of fungal PKSs from predicted proteomes.

SUBMITTER: Delgado JA

PROVIDER: S-EPMC6638892 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature

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A resource for the in silico identification of fungal polyketide synthases from predicted fungal proteomes.

Delgado Javier A JA Al-Azzam Omar O Denton Anne M AM Markell Samuel G SG Goswami Rubella S RS

Molecular plant pathology 20111123 5

The goal of this study was to develop a tool specifically designed to identify iterative polyketide synthases (iPKSs) from predicted fungal proteomes. A fungi-based PKS prediction model, specifically for fungal iPKSs, was developed using profile hidden Markov models (pHMMs) based on two essential iPKS domains, the β-ketoacyl synthase (KS) domain and acyltransferase (AT) domain, derived from fungal iPKSs. This fungi-based PKS prediction model was initially tested on the well-annotated proteome of ...[more]

PMID: 22112245

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Project 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.

Dataset Information

A resource for the in silico identification of fungal polyketide synthases from predicted fungal proteomes.

Publications

A resource for the in silico identification of fungal polyketide synthases from predicted fungal proteomes.

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