antiSMASH 3.0—a comprehensive resource for the genome mining of biosynthetic gene clusters

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.

Antibiotic drug discovery

“…The specificity of individual biosynthetic enzymes determines which building blocks will be incorporated into the compound and how it will be modified. The knowledge on enzyme specificity can be used to predict the resultant compounds (Weber et al ., 2015). …”

“…The vast amount of DNA data available these days provides a large pool of potential compounds encoded in these genomes that, given the bioinformatics tools that exist, are relatively fast and easy to screen for almost no costs. Moreover, sophisticated web‐based tools, such as anti‐SMASH (Medema et al ., 2011; Blin et al ., 2013, 2014; Weber et al ., 2015), PRISM (Skinnider et al ., 2015) or NaPDoS (Ziemert et al ., 2012), are easily accessible and do not require extended expertise in natural product biosynthesis or bioinformatics. Mining bacteria for their genetic potential also revealed that many more bacteria have the ability to produce natural products than previously thought and that more chemical diversity is waiting for discovery.…”

Molecular beacons to identify gifted microbes for genome mining

“…16 The analysis of gifted status among a wide range of microbes with finished genome sequences was carried out by using the standard antiSMASH 3.0 algorithm. 16,31 However, the quality and reliability of antiSMASH 3.0 analysis is limited by the quality of genome seqeuences analyzed. In this report, I demonstrate that the large SMGCs encoding daptomycin, spinosad and tylosin were assembled incorrectly in draft genome sequences, and that draft genomes generally tend to have fragmented assemblies of NRPS-PKS-I gene clusters, resulting overestimation of cluster numbers by antiSMASH 3.0.…”

“…4 This major shortcoming limits the utility of antiSMASH 3.0 31 and other bioinformatics tools that require high-quality finished genome sequences for reliable predictions, 16 as further demonstrated below.…”

“…8,[21][22][23][24][25][26][27][28][29][30] For microbial genome mining to become a robust methodology to drive the discovery of new and novel NPs for drug discovery, it is important to have a set of bioinformatics tools that can predict which microorganisms are the most 'gifted' for SM production. antiSMASH 3.0 31 is particularly useful to identify the numbers and types of SMs encoded by microbes, and has been used to survey a wide range of bacteria and archaea for those microorganisms most gifted for SM production. 16 Among the most gifted are bacteria with genomes 48.0 Mb, including many actinomycetes.…”