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Nucleic Acids Res. 2017 Jul 3;45(W1):W36-W41. doi: 10.1093/nar/gkx319.

antiSMASH 4.0-improvements in chemistry prediction and gene cluster boundary identification.

Author information

1
Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark.
2
Leibniz Institute for Natural Product Research and Infection Biology-Hans-Knöll-Institute, 07745 Jena, Germany.
3
Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI 53706, USA.
4
Bioinformatics Group, Wageningen University, 6708PB Wageningen, Netherlands.
5
Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
6
Warwick Integrative Synthetic Biology Centre, University of Warwick, Coventry CV4 7AL, UK.
7
Department of Chemical and Biomolecular Engineering & BioInformatics Research Center, Korea Advanced Institute of Science and Technology, Daejeon 34141, South Korea.
8
Faculty of Sciences, University of Lisbon, 1749-016 Lisbon, Portugal.
9
Kekulé-Institute of Organic Chemistry and Biochemistry, University of Bonn, 53121 Bonn, Germany.
10
Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
11
Manchester Synthetic Biology Research Centre (SYNBIOCHEM), Manchester Institute of Biotechnology, University of Manchester, Manchester M1 7DN, UK.

Abstract

Many antibiotics, chemotherapeutics, crop protection agents and food preservatives originate from molecules produced by bacteria, fungi or plants. In recent years, genome mining methodologies have been widely adopted to identify and characterize the biosynthetic gene clusters encoding the production of such compounds. Since 2011, the 'antibiotics and secondary metabolite analysis shell-antiSMASH' has assisted researchers in efficiently performing this, both as a web server and a standalone tool. Here, we present the thoroughly updated antiSMASH version 4, which adds several novel features, including prediction of gene cluster boundaries using the ClusterFinder method or the newly integrated CASSIS algorithm, improved substrate specificity prediction for non-ribosomal peptide synthetase adenylation domains based on the new SANDPUMA algorithm, improved predictions for terpene and ribosomally synthesized and post-translationally modified peptides cluster products, reporting of sequence similarity to proteins encoded in experimentally characterized gene clusters on a per-protein basis and a domain-level alignment tool for comparative analysis of trans-AT polyketide synthase assembly line architectures. Additionally, several usability features have been updated and improved. Together, these improvements make antiSMASH up-to-date with the latest developments in natural product research and will further facilitate computational genome mining for the discovery of novel bioactive molecules.

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