A Computational Screen for Type I Polyketide Synthases in Metagenomics Shotgun Data

By Konrad U. Foerstner, Tobias Doerks, Christopher J. Creevey, Anja Doerks, Peer Bork. 2008

33 citations recorded by the source corpus.

Background Polyketides are a diverse group of biotechnologically important secondary metabolites that are produced by multi domain enzymes called polyketide synthases (PKS). Methodology/Principal Findings We have estimated frequencies of type I PKS (PKS I) – a PKS subgroup – in natural environments by using Hidden-Markov-Models of eight domains to screen predicted proteins from six metagenomic shotgun data sets. As the complex PKS I have similarities to other multi-domain enzymes (like those for the fatty acid biosynthesis) we increased the reliability and resolution of the dataset by maximum-likelihood trees. The combined information of these trees was then used to discriminate true PKS I domains from evolutionary related but functionally different ones. We were able to identify numerous novel PKS I proteins, the highest density of which was found in Minnesota farm soil with 136 proteins out of 183,536 predicted genes. We also applied the protocol to UniRef database to improve the annotation of proteins with so far unknown function and identified some new instances of horizontal gene transfer. Conclusions/Significance The screening approach proved powerful in identifying PKS I sequences in large sequence data sets and is applicable to many other protein families.

Subfields: Molecular Biology, Pharmacology, Plant Science

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Peer Bork
2. Ivica Letunic
3. Tobias Doerks
4. Nadine Ziemert
5. Hongzhan Huang
6. Cathy H. Wu
7. Yuqi Wang
8. Eric E. Allen

In Pharmacology

1. Ivica Letunic
2. Peer Bork
3. P. Jensen
4. Nadine Ziemert
5. A. Harvey

In Plant Science

1. Peer Bork

Citations in the corpus

• UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches (2014, methodology): “…the functional effects of disease variants (Capriotti and Altman, 2011a, b; Sim et al., 2012), performing functional screening of metagenomics data (Foerstner et al., 2008; Wommack et al., 2012), developing large-scale hierarchical clustering algorithms (Loewenstein et al., 2008), studying gene…”
• The Natural Product Domain Seeker NaPDoS: A Phylogeny Based Bioinformatic Tool to Classify Secondary Metabolite Gene Diversity (2012, methodology): “Phylogenetics has also been used to successfully identify PKS sequences from complex metagenomic datasets [38].”
• SMART 6: recent updates and new developments (2008, methodology): “The selection of cut-offs for individual domains was based on a sophisticated tree-building procedure (6).”