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Bioinformatics. 2016 Feb 1;32(3):325-9. doi: 10.1093/bioinformatics/btv600. Epub 2015 Oct 14.

Computational discovery of specificity-conferring sites in non-ribosomal peptide synthetases.

Author information

1
NANORIPES - Centre for Natural Non-Ribosomal Peptide Synthesis, Aarhus University, Aarhus, Denmark Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark.
2
NANORIPES - Centre for Natural Non-Ribosomal Peptide Synthesis, Aarhus University, Aarhus, Denmark Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark.

Abstract

MOTIVATION:

By using a class of large modular enzymes known as Non-Ribosomal Peptide Synthetases (NRPS), bacteria and fungi are capable of synthesizing a large variety of secondary metabolites, many of which are bioactive and have potential, pharmaceutical applications as e.g. antibiotics. There is thus an interest in predicting the compound synthesized by an NRPS from its primary structure (amino acid sequence) alone, as this would enable an in silico search of whole genomes for NRPS enzymes capable of synthesizing potentially useful compounds.

RESULTS:

NRPS synthesis happens in a conveyor belt-like fashion where each individual NRPS module is responsible for incorporating a specific substrate (typically an amino acid) into the final product. Here, we present a new method for predicting substrate specificities of individual NRPS modules based on occurrences of motifs in their primary structures. We compare our classifier with existing methods and discuss possible biological explanations of how the motifs might relate to substrate specificity.

AVAILABILITY AND IMPLEMENTATION:

SEQL-NRPS is available as a web service implemented in Python with Flask at http://services.birc.au.dk/seql-nrps and source code available at https://bitbucket.org/dansondergaard/seql-nrps/.

CONTACT:

micknudsen@gmail.com or cstorm@birc.au.dk

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

PMID:
26471456
DOI:
10.1093/bioinformatics/btv600
[Indexed for MEDLINE]

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