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Nucleic Acids Research· 2015Q1

antiSMASH 3.0—a comprehensive resource for the genome mining of biosynthetic gene clusters

Tilmann Weber, Kai Blin, Srikanth Duddela, Daniel Krug et al.

Short summary

antiSMASH 3.0 significantly improves genome mining for microbial biosynthetic gene clusters by integrating the ClusterFinder algorithm for novel cluster detection and a new ClusterBlast module for similarity comparisons against 1172 known clusters.

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Key points

  • antiSMASH 3.0 integrates ClusterFinder for probabilistic detection of novel gene cluster types.
  • A new ClusterBlast module identifies similarities to 1172 known clusters with defined end products.
  • Active sites of key biosynthetic enzymes are pinpointed, and EC numbers are assigned to enzyme-coding genes.
  • Chemical structure prediction is improved by incorporating polyketide reduction states.
  • A new XML output module enables offline annotation editing in Geneious software.

AI-generated from the title and abstract; the full text is not read.

Abstract

Microbial secondary metabolism constitutes a rich source of antibiotics, chemotherapeutics, insecticides and other high-value chemicals. Genome mining of gene clusters that encode the biosynthetic pathways for these metabolites has become a key methodology for novel compound discovery. In 2011, we introduced antiSMASH, a web server and stand-alone tool for the automatic genomic identification and analysis of biosynthetic gene clusters, available at http://antismash.secondarymetabolites.org. Here, we present version 3.0 of antiSMASH, which has undergone major improvements. A full integration of the recently published ClusterFinder algorithm now allows using this probabilistic algorithm to detect putative gene clusters of unknown types. Also, a new dereplication variant of the ClusterBlast module now identifies similarities of identified clusters to any of 1172 clusters with known end products. At the enzyme level, active sites of key biosynthetic enzymes are now pinpointed through a curated pattern-matching procedure and Enzyme Commission numbers are assigned to functionally classify all enzyme-coding genes. Additionally, chemical structure prediction has been improved by incorporating polyketide reduction states. Finally, in order for users to be able to organize and analyze multiple antiSMASH outputs in a private setting, a new XML output module allows offline editing of antiSMASH annotations within the Geneious software.

The authors' abstract, as published at the source. Nucleic Acids Research, 2015 · DOI ↗

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Field: Pharmacology (Medicine)

PharmacologyMedicine