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      antiSMASH 7.0: new and improved predictions for detection, regulation, chemical structures and visualisation

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          Abstract

          Microorganisms produce small bioactive compounds as part of their secondary or specialised metabolism. Often, such metabolites have antimicrobial, anticancer, antifungal, antiviral or other bio-activities and thus play an important role for applications in medicine and agriculture. In the past decade, genome mining has become a widely-used method to explore, access, and analyse the available biodiversity of these compounds. Since 2011, the ‘antibiotics and secondary metabolite analysis shell—antiSMASH’ ( https://antismash.secondarymetabolites.org/) has supported researchers in their microbial genome mining tasks, both as a free to use web server and as a standalone tool under an OSI-approved open source licence. It is currently the most widely used tool for detecting and characterising biosynthetic gene clusters (BGCs) in archaea, bacteria, and fungi. Here, we present the updated version 7 of antiSMASH. antiSMASH 7 increases the number of supported cluster types from 71 to 81, as well as containing improvements in the areas of chemical structure prediction, enzymatic assembly-line visualisation and gene cluster regulation.

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          Graphical Abstract

          antiSMASH 7 increases the number of supported cluster types from 71 to 81, as well as containing improvements in the areas of chemical structure prediction, enzymatic assembly-line visualisation and gene cluster regulation.

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          Most cited references41

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          Pfam: The protein families database in 2021

          Abstract The Pfam database is a widely used resource for classifying protein sequences into families and domains. Since Pfam was last described in this journal, over 350 new families have been added in Pfam 33.1 and numerous improvements have been made to existing entries. To facilitate research on COVID-19, we have revised the Pfam entries that cover the SARS-CoV-2 proteome, and built new entries for regions that were not covered by Pfam. We have reintroduced Pfam-B which provides an automatically generated supplement to Pfam and contains 136 730 novel clusters of sequences that are not yet matched by a Pfam family. The new Pfam-B is based on a clustering by the MMseqs2 software. We have compared all of the regions in the RepeatsDB to those in Pfam and have started to use the results to build and refine Pfam repeat families. Pfam is freely available for browsing and download at http://pfam.xfam.org/.
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            Natural Products as Sources of New Drugs over the Nearly Four Decades from 01/1981 to 09/2019

            This review is an updated and expanded version of the five prior reviews that were published in this journal in 1997, 2003, 2007, 2012, and 2016. For all approved therapeutic agents, the time frame has been extended to cover the almost 39 years from the first of January 1981 to the 30th of September 2019 for all diseases worldwide and from ∼1946 (earliest so far identified) to the 30th of September 2019 for all approved antitumor drugs worldwide. As in earlier reviews, only the first approval of any drug is counted, irrespective of how many "biosimilars" or added approvals were subsequently identified. As in the 2012 and 2016 reviews, we have continued to utilize our secondary subdivision of a "natural product mimic", or "NM", to join the original primary divisions, and the designation "natural product botanical", or "NB", to cover those botanical "defined mixtures" now recognized as drug entities by the FDA (and similar organizations). From the data presented in this review, the utilization of natural products and/or synthetic variations using their novel structures, in order to discover and develop the final drug entity, is still alive and well. For example, in the area of cancer, over the time frame from 1946 to 1980, of the 75 small molecules, 40, or 53.3%, are N or ND. In the 1981 to date time frame the equivalent figures for the N* compounds of the 185 small molecules are 62, or 33.5%, though to these can be added the 58 S* and S*/NMs, bringing the figure to 64.9%. In other areas, the influence of natural product structures is quite marked with, as expected from prior information, the anti-infective area being dependent on natural products and their structures, though as can be seen in the review there are still disease areas (shown in Table 2) for which there are no drugs derived from natural products. Although combinatorial chemistry techniques have succeeded as methods of optimizing structures and have been used very successfully in the optimization of many recently approved agents, we are still able to identify only two de novo combinatorial compounds (one of which is a little speculative) approved as drugs in this 39-year time frame, though there is also one drug that was developed using the "fragment-binding methodology" and approved in 2012. We have also added a discussion of candidate drug entities currently in clinical trials as "warheads" and some very interesting preliminary reports on sources of novel antibiotics from Nature due to the absolute requirement for new agents to combat plasmid-borne resistance genes now in the general populace. We continue to draw the attention of readers to the recognition that a significant number of natural product drugs/leads are actually produced by microbes and/or microbial interactions with the "host from whence it was isolated"; thus we consider that this area of natural product research should be expanded significantly.
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              antiSMASH 5.0: updates to the secondary metabolite genome mining pipeline

              Abstract Secondary metabolites produced by bacteria and fungi are an important source of antimicrobials and other bioactive compounds. In recent years, genome mining has seen broad applications in identifying and characterizing new compounds as well as in metabolic engineering. Since 2011, the ‘antibiotics and secondary metabolite analysis shell—antiSMASH’ (https://antismash.secondarymetabolites.org) has assisted researchers in this, both as a web server and a standalone tool. It has established itself as the most widely used tool for identifying and analysing biosynthetic gene clusters (BGCs) in bacterial and fungal genome sequences. Here, we present an entirely redesigned and extended version 5 of antiSMASH. antiSMASH 5 adds detection rules for clusters encoding the biosynthesis of acyl-amino acids, β-lactones, fungal RiPPs, RaS-RiPPs, polybrominated diphenyl ethers, C-nucleosides, PPY-like ketones and lipolanthines. For type II polyketide synthase-encoding gene clusters, antiSMASH 5 now offers more detailed predictions. The HTML output visualization has been redesigned to improve the navigation and visual representation of annotations. We have again improved the runtime of analysis steps, making it possible to deliver comprehensive annotations for bacterial genomes within a few minutes. A new output file in the standard JavaScript object notation (JSON) format is aimed at downstream tools that process antiSMASH results programmatically.
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                Author and article information

                Contributors
                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                05 July 2023
                04 May 2023
                04 May 2023
                : 51
                : W1
                : W46-W50
                Affiliations
                The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark , Kgs.Lyngby, Denmark
                The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark , Kgs.Lyngby, Denmark
                Molecular Biotechnology, Institute of Biology, Leiden University , Leiden, The Netherlands
                Bioinformatics Group, Wageningen University , Wageningen, The Netherlands
                Bioinformatics Group, Wageningen University , Wageningen, The Netherlands
                Bioinformatics Group, Wageningen University , Wageningen, The Netherlands
                Institute of Molecular Bio Science, Goethe-University Frankfurt , Frankfurt am Main, Germany
                LOEWE Center for Translational Biodiversity Genomics. Frankfurt am Main , Germany
                Bioinformatics Group, Wageningen University , Wageningen, The Netherlands
                Bioinformatics Group, Wageningen University , Wageningen, The Netherlands
                Institute of Technical Chemistry, Leibniz University Hannover , Hannover, Germany
                Bioinformatics Group, Wageningen University , Wageningen, The Netherlands
                Department of Microbiology, University of Illinois Urbana–Champaign , Urbana, IL, USA
                Institute for Genomic Biology, University of Illinois Urbana–Champaign , Urbana, IL, USA
                Institute of Molecular Bio Science, Goethe-University Frankfurt , Frankfurt am Main, Germany
                LOEWE Center for Translational Biodiversity Genomics. Frankfurt am Main , Germany
                Molecular Biotechnology, Institute of Biology, Leiden University , Leiden, The Netherlands
                Bioinformatics Group, Wageningen University , Wageningen, The Netherlands
                The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark , Kgs.Lyngby, Denmark
                Author notes
                To whom correspondence should be addressed. Tel: +45 93511306; Email: kblin@ 123456biosustain.dtu.dk
                Correspondence may also be addressed to Marnix H. Medema. Tel: +31 317482036; Email: marnix.medema@ 123456wur.nl
                Correspondence may also be addressed to Tilmann Weber. Tel: +45 24896132; Email: tiwe@ 123456biosustain.dtu.dk
                Author information
                https://orcid.org/0000-0003-3764-6051
                https://orcid.org/0000-0002-1862-6699
                https://orcid.org/0000-0003-1964-8221
                https://orcid.org/0000-0001-8420-1325
                https://orcid.org/0000-0002-0182-0671
                https://orcid.org/0000-0003-0341-1561
                https://orcid.org/0000-0002-8260-5120
                Article
                gkad344
                10.1093/nar/gkad344
                10320115
                37140036
                8c1bbf4a-350f-4515-ae4a-561e911d4957
                © The Author(s) 2023. 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 ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 26 April 2023
                : 31 March 2023
                : 24 February 2023
                Page count
                Pages: 5
                Funding
                Funded by: Novo Nordisk Foundation, DOI 10.13039/501100009708;
                Award ID: NNF20CC0035580
                Award ID: NNF16OC0021746
                Funded by: Danish National Research Foundation, DOI 10.13039/501100001732;
                Award ID: DNRF137
                Funded by: ERC Starting Grant;
                Award ID: 948770-DECIPHER
                Funded by: Novel Antibacterial Compounds and Therapies Antagonising Resistance program;
                Funded by: Dutch Research Council, DOI 10.13039/501100003246;
                Award ID: 16440
                Categories
                AcademicSubjects/SCI00010
                Web Server Issue

                Genetics
                Genetics

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