Dataset Information

Analysis of the Sequences, Structures, and Functions of Product-Releasing Enzyme Domains in Fungal Polyketide Synthases.

ABSTRACT: Product-releasing enzyme (PRE) domains in fungal non-reducing polyketide synthases (NR-PKSs) play a crucial role in catalysis and editing during polyketide biosynthesis, especially accelerating final biosynthetic reactions accompanied with product offloading. However, up to date, the systematic knowledge about PRE domains is deficient. In the present study, the relationships between sequences, structures, and functions of PRE domains were analyzed with 574 NR-PKSs of eight groups (I-VIII). It was found that the PRE domains in NR-PKSs could be mainly classified into three types, thioesterase (TE), reductase (R), and metallo-?-lactamase-type TE (M?L-TE). The widely distributed TE or TE-like domains were involved in NR-PKSs of groups I-IV, VI, and VIII. The R domains appeared in NR-PKSs of groups IV and VII, while the physically discrete M?L-TE domains were employed by most NR-PKSs of group V. The changes of catalytic sites and structural characteristics resulted in PRE functional differentiations. The phylogeny revealed that the evolution of TE domains was accompanied by complex functional divergence. The diverse sequence lengths of TE lid-loops affected substrate specificity with different chain lengths. The volume diversification of TE catalytic pockets contributed to catalytic mechanisms with functional differentiations. The above findings may help to understand the crucial catalysis of fungal aromatic polyketide biosyntheses and govern recombination of NR-PKSs to obtain unnatural target products.

SUBMITTER: Liu L

PROVIDER: S-EPMC5591372 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Analysis of the Sequences, Structures, and Functions of Product-Releasing Enzyme Domains in Fungal Polyketide Synthases.

Liu Lu L Zhang Zheng Z Shao Chang-Lun CL Wang Chang-Yun CY

Frontiers in microbiology 20170904

Product-releasing enzyme (PRE) domains in fungal non-reducing polyketide synthases (NR-PKSs) play a crucial role in catalysis and editing during polyketide biosynthesis, especially accelerating final biosynthetic reactions accompanied with product offloading. However, up to date, the systematic knowledge about PRE domains is deficient. In the present study, the relationships between sequences, structures, and functions of PRE domains were analyzed with 574 NR-PKSs of eight groups (I-VIII). It wa ...[more]

PMID: 28928723

Similar Datasets

Project description: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.

Dataset Information

Analysis of the Sequences, Structures, and Functions of Product-Releasing Enzyme Domains in Fungal Polyketide Synthases.

Publications

Analysis of the Sequences, Structures, and Functions of Product-Releasing Enzyme Domains in Fungal Polyketide Synthases.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets