Insights into secondary metabolism from a global analysis of prokaryotic biosynthetic gene clusters.

By Peter Cimermancic, Marnix H. Medema, J. Claesen, Kenji L. Kurita, Laura C. Wieland Brown, Konstantinos Mavrommatis, Amrita Pati, P. Godfrey, M. Koehrsen, Jon Clardy and 5 more. Cell, 2014

897 citations recorded by the source corpus.

Although biosynthetic gene clusters (BGCs) have been discovered for hundreds of bacterial metabolites, our knowledge of their diversity remains limited. Here, we used a novel algorithm to systematically identify BGCs in the extensive extant microbial sequencing data. Network analysis of the predicted BGCs revealed large gene cluster families, the vast majority uncharacterized. We experimentally characterized the most prominent family, consisting of two subfamilies of hundreds of BGCs distributed throughout the Proteobacteria; their products are aryl polyenes, lipids with an aryl head group conjugated to a polyene tail. We identified a distant relationship to a third subfamily of aryl polyene BGCs, and together the three subfamilies represent the largest known family of biosynthetic gene clusters, with more than 1,000 members. Although these clusters are widely divergent in sequence, their small molecule products are remarkably conserved, indicating for the first time the important roles these compounds play in Gram-negative cell biology.

Subfields: Molecular Biology, Pharmacology

View Paper: https://www.semanticscholar.org/paper/033cca37b3d332f1927d93440dac5f1ad9a42a1e

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Citations in the corpus

A deep learning genome-mining strategy for biosynthetic gene cluster prediction (2019, methodology): “One such widely used machine learning approach named ClusterFinder (16) employs a Hidden Markov Model (HMM) instead of the multiple sequence alignment based profile-HMM (17) methods seen in other approaches such as AntiSMASH (ANTIbiotics & Secondary Metabolite Analysis SHell) (14) and PRISM (18).”
antiSMASH 6.0: improving cluster detection and comparison capabilities (2021, methodology): “While tools like ARTS (16) and BiG-SCAPE (19) sit downstream of antiSMASH and consume anti-SMASH results, other tools like ClusterFinder (33) and DeepBGC (34) offer alternative, machine-learning based methods to predict gene clusters and thus function more in parallel to antiSMASH’s rule based clust”
A systematic analysis of biosynthetic gene clusters in the human microbiome reveals a common family of antibiotics (2014, methodology): “Most of the BGCs from nonhuman environmental isolates are harbored by members of the abundant genera Streptomyces, Bacillus, Pseudomonas, Burkholderia, and Myxococcus (Cimermancic et al., 2014); in contrast, most human-associated BGCs are harbored by members of the abundant human-associated genera B”
Small Molecules from the Human Microbiota (2015, positive): “We have used ClusterFinder (7) to identify biosynthetic gene clusters in the humanmicrobiome as a way to assess the metabolic potential of the humanmicrobiota (10).”
A computational framework to explore large-scale biosynthetic diversity (2019, background): “13), including detoxin N1 (2), detoxin N2 (3) and its acetoxylated analogue, detoxin N3 (4).”
Artificial Intelligence For Natural Product Drug Discovery (2023, methodology): “For example, the hidden Markov model-based method ClusterFinder 15 , the deep learning approaches DeepBGC 16 , GECCO 17 and SanntiS 18 , and several genome mining algorithms for ribosomally synthesized and post-translationally modified peptides (RiPPs) 19–22 each use deep learning”
antiSMASH 4.0—improvements in chemistry prediction and gene cluster boundary identification (2017, methodology): “Additionally, for both bacterial and fungal genomes, the user can now choose to use the ClusterFinder algorithm (16) to estimate cluster boundaries based on frequencies of locally encoded protein domains detected by Pfam (17) (based on these being either more or less BGClike).”