antiSMASH 3.0—a comprehensive resource for the genome mining of biosynthetic gene clusters
By Tilmann Weber, Kai Blin, Srikanth Duddela, D. Krug, Hyun Uk Kim, R. Bruccoleri, Sang Yup Lee, Michael A. Fischbach, R. Müller, Wolfgang Wohlleben and 3 more. Nucleic Acids Res., 2015
1,741 citations recorded by the source corpus.
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.
Subfields: Molecular Biology, Pharmacology
View Paper: https://www.semanticscholar.org/paper/cd918c7a0a7c80b5f5b9a83c8f8e5e21a47db2c4
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Citations in the corpus
- PRISM 3: expanded prediction of natural product chemical structures from microbial genomes (2017, methodology): “Nonetheless, early methods were designed primarily to facilitate detection of biosynthetic gene clusters (4–11), whereas few methods exist to facilitate the prediction of natural product structures from microbial genomes (6,11).”
- A computational framework to explore large-scale biosynthetic diversity (2019, neutral): “A logical next step will be the unified classification of the millions of BGCs within publicly available genome sequences, and a Pfam-like database for the assignment of biosynthetic gene cluster families to known and unknown areas of natural product chemical diversity.”
- Natural product discovery: past, present, and future (2016, background): “It is now estimated that 10 % of SMGCs are expressed in sufficient quantities to be observed under routine fermentation analyses, and the others require special conditions or genetic manipulations to reveal their products [21, 89, 133, 1 3 134].”
- A new genome-mining tool redefines the lasso peptide biosynthetic landscape (2016, methodology): “Methods for mining RiPPs include single-input whole-genome analysis (e.g. antiSMASH and BAGEL3) 4, 12 and RiPPquest, a mass spectrometry-based method for connecting BGCs to molecules.”