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

Javier A Delgado, Omar Al-Azzam, Anne M Denton, Samuel G Markell, Rubella S Goswami

Department of Plant Pathology, North Dakota State University, Fargo, ND 58108, USA.

Molecular plant pathology 2012 Jun

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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 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. © 2011 The Authors. Molecular Plant Pathology © 2011 BSPP and Blackwell Publishing Ltd.

Citation

Javier A Delgado, Omar Al-Azzam, Anne M Denton, Samuel G Markell, Rubella S Goswami. A resource for the in silico identification of fungal polyketide synthases from predicted fungal proteomes. Molecular plant pathology. 2012 Jun;13(5):494-507