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Showing 1–7 of 7 results
Advanced filters: Author: Satria Kautsar Clear advanced filters

  • BiG-SCAPE and BiG-SLiCE are computational tools that enable exploring the diversity of metabolic gene clusters across microbial genomes. Here, the authors present major updates to these tools, providing essential infrastructure for studying the diversity of microbial metabolism.

    • Arjan Draisma
    • Catarina Loureiro
    • Marnix H. Medema
    ResearchOpen Access
    Nature Communications
    P: 1-12

  • A comprehensive survey of secondary metabolites encoded in bacteria identifies large differences in biosynthetic diversity among genera and pinpoints those that can be targeted for novel chemistries provisionally suitable as antimicrobials.

    • Athina Gavriilidou
    • Satria A. Kautsar
    • Nadine Ziemert
    Research
    Nature Microbiology
    Volume: 7, P: 726-735

  • Global ocean microbiome survey reveals the bacterial family ‘Candidatus Eudoremicrobiaceae’, which includes some of the most biosynthetically diverse microorganisms in the ocean environment.

    • Lucas Paoli
    • Hans-Joachim Ruscheweyh
    • Shinichi Sunagawa
    ResearchOpen Access
    Nature
    Volume: 607, P: 111-118

  • BURP domains within lyciumin precursor peptides serve as autocatalytic peptide cyclases, enabling the discovery of other BURP-domain-derived products and development of a bioinformatic method to mine plants for precursor-peptide-encoding genes.

    • Desnor N. Chigumba
    • Lisa S. Mydy
    • Roland D. Kersten
    Research
    Nature Chemical Biology
    Volume: 18, P: 18-28

  • 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.

    • Jorge C. Navarro-Muñoz
    • Nelly Selem-Mojica
    • Marnix H. Medema
    Research
    Nature Chemical Biology
    Volume: 16, P: 60-68

  • Advances in computational omics technologies are enabling access to the hidden diversity of natural products, and artificial intelligence approaches are facilitating key steps in harnessing the therapeutic potential of such compounds, including biological activity prediction. This article discusses synergies between these fields to effectively identify drug candidates from the plethora of molecules produced by nature, and how to address the challenges in realizing the potential of these synergies.

    • Michael W. Mullowney
    • Katherine R. Duncan
    • Marnix H. Medema
    Reviews
    Nature Reviews Drug Discovery
    Volume: 22, P: 895-916