antiSMASH - Rapid Identification, Annotation and Analysis of Secondary Metabolite Biosynthesis Gene Clusters

antiSMASH - Rapid Identification, Annotation and Analysis of Secondary Metabolite Biosynthesis Gene Clusters


We are looking at an interesting paper from M. H. Medema et al. on finding gene clusters in bacterial genomes.

antiSMASH: rapid identification, annotation and analysis of secondary metabolite biosynthesis gene clusters in bacterial and fungal genome sequences

Bacterial and fungal secondary metabolism is a rich source of novel bioactive compounds with potential pharmaceutical applications as antibiotics, anti-tumor drugs or cholesterol-lowering drugs. To find new drug candidates, microbiologists are increasingly relying on sequencing genomes of a wide variety of microbes. However, rapidly and reliably pinpointing all the potential gene clusters for secondary metabolites in dozens of newly sequenced genomes has been extremely challenging, due to their biochemical heterogeneity, the presence of unknown enzymes and the dispersed nature of the necessary specialized bioinformatics tools and resources. Here, we present antiSMASH (antibiotics & Secondary Metabolite Analysis Shell), the first comprehensive pipeline capable of identifying biosynthetic loci covering the whole range of known secondary metabolite compound classes (polyketides, non- ribosomal peptides, terpenes, aminoglycosides, aminocoumarins, indolocarbazoles, lantibiotics, bacteriocins, nucleosides, beta-lactams, butyrolactones, siderophores, melanins and others). It aligns the identified regions at the gene cluster level to their nearest relatives from a database containing all other known gene clusters, and integrates or cross-links all previously available secondary-metabolite specific gene analysis methods in one interactive view.

The software is available here -

Link

The pipeline for the program is fairly simple. From the NAR paper -

Capture

Why is it important? It is because the above work and similar research connects bioinformatics of next-gen sequencing (especially metagenomics) to drug discovery. We will add more on the topic in future commentaries.



Written by M. //