A computational framework to explore large-scale biosynthetic diversity

By J. Navarro-Muñoz, Nelly Sélem-Mojica, M. Mullowney, S. Kautsar, James H. Tryon, Elizabeth I. Parkinson, Emmanuel L. C. de los Santos, Marley Yeong, Pablo Cruz-Morales, Sahar Abubucker and 10 more. Nature Chemical Biology, 2019

819 citations recorded by the source corpus.

Genome mining has become a key technology to exploit natural product diversity. Although initially performed on a single-genome basis, the process is now being scaled up to mine entire genera, strain collections and microbiomes. However, no bioinformatic framework is currently available for effectively analyzing datasets of this size and complexity. In the present study, a streamlined computational workflow is provided, consisting of two new software tools: the ‘biosynthetic gene similarity clustering and prospecting engine’ (BiG-SCAPE), which facilitates fast and interactive sequence similarity network analysis of biosynthetic gene clusters and gene cluster families; and the ‘core analysis of syntenic orthologues to prioritize natural product gene clusters’ (CORASON), which elucidates phylogenetic relationships within and across these families. BiG-SCAPE is validated by correlating its output to metabolomic data across 363 actinobacterial strains and the discovery potential of CORASON is demonstrated by comprehensively mapping biosynthetic diversity across a range of detoxin/rimosamide-related gene cluster families, culminating in the characterization of seven detoxin analogues. Two bioinformatic tools, BiG-SCAPE and CORASON, enable sequence similarity network and phylogenetic analysis of gene clusters and their families across hundreds of strains and in large datasets, leading to the discovery of new natural products.

Subfields: Molecular Biology, Pharmacology

View Paper: https://www.semanticscholar.org/paper/c5b4df4a448657beea7600bec1a145a02314a357

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Citations in the corpus

Artificial Intelligence For Natural Product Drug Discovery (2023, background): “…as self-organizing maps (SOMs) (for example, for macromolecular target prediction 221 ) and clustering (for example, grouping gene cluster families 222 ); and deep learning (part c ), such as convolutional neural networks (for example, for chemical structure elucidation 47 ), computer vision (for…”
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”