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

By Kai Blin, Thomas Wolf, M. Chevrette, Xiaowen Lu, Christopher J. Schwalen, S. Kautsar, H. S. Duran, E. L. D. L. Santos, Hyun Uk Kim, M. Nave and 8 more. Nucleic Acids Res., 2017

1,092 citations recorded by the source corpus.

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.

Subfields: Biotechnology, Molecular Biology, Pharmacology

View Paper: https://www.semanticscholar.org/paper/df09cb5ec92bf58c640be34f927de0f733ef0863

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Citations in the corpus

MGnify: the microbiome sequence data analysis resource in 2023 (2022, methodology): “In addition to the improved protein predictions from assembled sequences, they also allow us to provide higherlevel annotations, such as pathway predictions (KEGG (10), Genome Properties (11)) and prediction of biosynthetic gene clusters (BGC) using antiSMASH (12) and our inhouse tool for BGC predic”
Review on natural products databases: where to find data in 2020 (2020, methodology): “For the completeness of this list, it is also necessary to site two major tools for the discovery and prediction of NPs from protein sequence data: antiSMASH [74] and PRISM [75].”
MGnify: the microbiome analysis resource in 2020 (2019, methodology): “In addition, InterPro annotations are processed to generate a compendium of Genome Properties results, detailing whether a property is present, partially present or absent in the dataset. antiSMASH (45) is also run on the predicted protein set, providing annotation of biosynthetic gene clusters.”
IMG/M v.5.0: an integrated data management and comparative analysis system for microbial genomes and microbiomes (2018, methodology): “Another line of developments includes upgrading the content of the IMG database by introducing new reference databases and analysis tools, such as adding non-coding RNA prediction in metagenome sequences and assignment of metagenomic proteins to TIGRfams and some InterPro families, and including a n”
A new antibiotic selectively kills Gram-negative pathogens (2019, positive): “The resulting data were initially analyzed using antibiotic and secondary metabolite analysis shell (antiSMASH 39 ).”
AutoMLST: an automated web server for generating multi-locus species trees highlighting natural product potential (2019, positive): “The Biosynthetic Gene Cluster (BGC) coloring scheme illustrates conservative counts of secondary metabolite potential taken form an antiSMASH v4 (43) analysis of all reference genomes in the database.”
A computational framework to explore large-scale biosynthetic diversity (2019, positive): “Computational tools for the identification of BGCs and the prediction of their products’ chemical structures, such as antiSMASH 14-17 and PRISM 18-20 , have played a key role in the success of genome mining.”