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      antiSMASH 4.0—improvements in chemistry prediction and gene cluster boundary identification

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          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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          Insights into secondary metabolism from a global analysis of prokaryotic biosynthetic gene clusters.

          Although biosynthetic gene clusters (BGCs) have been discovered for hundreds of bacterial metabolites, our knowledge of their diversity remains limited. Here, we used a novel algorithm to systematically identify BGCs in the extensive extant microbial sequencing data. Network analysis of the predicted BGCs revealed large gene cluster families, the vast majority uncharacterized. We experimentally characterized the most prominent family, consisting of two subfamilies of hundreds of BGCs distributed throughout the Proteobacteria; their products are aryl polyenes, lipids with an aryl head group conjugated to a polyene tail. We identified a distant relationship to a third subfamily of aryl polyene BGCs, and together the three subfamilies represent the largest known family of biosynthetic gene clusters, with more than 1,000 members. Although these clusters are widely divergent in sequence, their small molecule products are remarkably conserved, indicating for the first time the important roles these compounds play in Gram-negative cell biology. Copyright © 2014 Elsevier Inc. All rights reserved.
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            antiSMASH 2.0—a versatile platform for genome mining of secondary metabolite producers

            Microbial secondary metabolites are a potent source of antibiotics and other pharmaceuticals. Genome mining of their biosynthetic gene clusters has become a key method to accelerate their identification and characterization. In 2011, we developed antiSMASH, a web-based analysis platform that automates this process. Here, we present the highly improved antiSMASH 2.0 release, available at http://antismash.secondarymetabolites.org/. For the new version, antiSMASH was entirely re-designed using a plug-and-play concept that allows easy integration of novel predictor or output modules. antiSMASH 2.0 now supports input of multiple related sequences simultaneously (multi-FASTA/GenBank/EMBL), which allows the analysis of draft genomes comprising multiple contigs. Moreover, direct analysis of protein sequences is now possible. antiSMASH 2.0 has also been equipped with the capacity to detect additional classes of secondary metabolites, including oligosaccharide antibiotics, phenazines, thiopeptides, homo-serine lactones, phosphonates and furans. The algorithm for predicting the core structure of the cluster end product is now also covering lantipeptides, in addition to polyketides and non-ribosomal peptides. The antiSMASH ClusterBlast functionality has been extended to identify sub-clusters involved in the biosynthesis of specific chemical building blocks. The new features currently make antiSMASH 2.0 the most comprehensive resource for identifying and analyzing novel secondary metabolite biosynthetic pathways in microorganisms.
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              The evolution of genome mining in microbes - a review.

              Covering: 2006 to 2016The computational mining of genomes has become an important part in the discovery of novel natural products as drug leads. Thousands of bacterial genome sequences are publically available these days containing an even larger number and diversity of secondary metabolite gene clusters that await linkage to their encoded natural products. With the development of high-throughput sequencing methods and the wealth of DNA data available, a variety of genome mining methods and tools have been developed to guide discovery and characterisation of these compounds. This article reviews the development of these computational approaches during the last decade and shows how the revolution of next generation sequencing methods has led to an evolution of various genome mining approaches, techniques and tools. After a short introduction and brief overview of important milestones, this article will focus on the different approaches of mining genomes for secondary metabolites, from detecting biosynthetic genes to resistance based methods and "evo-mining" strategies including a short evaluation of the impact of the development of genome mining methods and tools on the field of natural products and microbial ecology.
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                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                03 July 2017
                28 April 2017
                28 April 2017
                : 45
                : Web Server issue
                : W36-W41
                Affiliations
                [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
                Author notes
                [* ]To whom correspondence should be addressed. Tel: +31 317484706; Email: marnix.medema@ 123456wur.nl . Correspondence may also be addressed to Tilmann Weber. Tel: +45 24896132; Email: tiwe@ 123456biosustain.dtu.dk
                Author information
                http://orcid.org/0000-0002-8260-5120
                http://orcid.org/0000-0002-2191-2821
                Article
                gkx319
                10.1093/nar/gkx319
                5570095
                28460038
                8f48573b-8bbb-485e-aeb8-8116e16c4780
                © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 13 April 2017
                : 07 April 2017
                : 25 February 2017
                Page count
                Pages: 6
                Categories
                Web Server Issue

                Genetics
                Genetics

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