Automated identification of depsipeptide natural products by an informatic search algorithm.

Chembiochem

Department of Chemistry and Chemical Biology, Department of Biochemistry and Biomedical Sciences, M. G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, L8N 3Z5 (Canada).

Published: January 2015

AI Article Synopsis

  • * Researchers have developed an improved informatic search algorithm called iSNAP, which helps identify both known and predicted depsipeptides in complex microbial cultures.
  • * The study successfully identified new depsipeptides and gene clusters for future research, showcasing the effectiveness of chemoinformatic methods in discovering genetically encoded metabolites.

Article Abstract

Nonribosomal depsipeptides are a class of potent microbial natural products, which include several clinically approved pharmaceutical agents. Genome sequencing has revealed a large number of uninvestigated natural-product biosynthetic gene clusters. However, while novel informatic search methods to access these gene clusters have been developed to identify peptide natural products, depsipeptide detection has proven challenging. Herein, we present an improved version of our informatic search algorithm for natural products (iSNAP), which facilitates the detection of known and genetically predicted depsipeptides in complex microbial culture extracts. We validated this technology by identifying several depsipeptides from novel producers, and located a large number of novel depsipeptide gene clusters for future study. This approach highlights the value of chemoinformatic search methods for the discovery of genetically encoded metabolites by targeting specific areas of chemical space.

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Source
http://dx.doi.org/10.1002/cbic.201402434DOI Listing

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