More Like this

1. MIBiG 3.0: a community-driven effort to annotate experimentally validated biosynthetic gene clusters

   https://doi.org/10.1093/nar/gkac1049  

   Terlouw, Barbara R; Blin, Kai; Navarro-Muñoz, Jorge C; Avalon, Nicole E; Chevrette, Marc G; Egbert, Susan; Lee, Sanghoon; Meijer, David; Recchia, Michael J; Reitz, Zachary L; et al (November 2022, Nucleic Acids Research)

   Abstract With an ever-increasing amount of (meta)genomic data being deposited in sequence databases, (meta)genome mining for natural product biosynthetic pathways occupies a critical role in the discovery of novel pharmaceutical drugs, crop protection agents and biomaterials. The genes that encode these pathways are often organised into biosynthetic gene clusters (BGCs). In 2015, we defined the Minimum Information about a Biosynthetic Gene cluster (MIBiG): a standardised data format that describes the minimally required information to uniquely characterise a BGC. We simultaneously constructed an accompanying online database of BGCs, which has since been widely used by the community as a reference dataset for BGCs and was expanded to 2021 entries in 2019 (MIBiG 2.0). Here, we describe MIBiG 3.0, a database update comprising large-scale validation and re-annotation of existing entries and 661 new entries. Particular attention was paid to the annotation of compound structures and biological activities, as well as protein domain selectivities. Together, these new features keep the database up-to-date, and will provide new opportunities for the scientific community to use its freely available data, e.g. for the training of new machine learning models to predict sequence-structure-function relationships for diverse natural products. MIBiG 3.0 is accessible online at https://mibig.secondarymetabolites.org/. 

   more » « less
2. Nerpa: A Tool for Discovering Biosynthetic Gene Clusters of Bacterial Nonribosomal Peptides

   https://doi.org/10.3390/metabo11100693  

   Kunyavskaya, Olga; Tagirdzhanov, Azat M.; Caraballo-Rodríguez, Andrés Mauricio; Nothias, Louis-Félix; Dorrestein, Pieter C.; Korobeynikov, Anton; Mohimani, Hosein; Gurevich, Alexey (October 2021, Metabolites)

   Microbial natural products are a major source of bioactive compounds for drug discovery. Among these molecules, nonribosomal peptides (NRPs) represent a diverse class of natural products that include antibiotics, immunosuppressants, and anticancer agents. Recent breakthroughs in natural product discovery have revealed the chemical structure of several thousand NRPs. However, biosynthetic gene clusters (BGCs) encoding them are known only for a few hundred compounds. Here, we developed Nerpa, a computational method for the high-throughput discovery of novel BGCs responsible for producing known NRPs. After searching 13,399 representative bacterial genomes from the RefSeq repository against 8368 known NRPs, Nerpa linked 117 BGCs to their products. We further experimentally validated the predicted BGC of ngercheumicin from Photobacterium galatheae via mass spectrometry. Nerpa supports searching new genomes against thousands of known NRP structures, and novel molecular structures against tens of thousands of bacterial genomes. The availability of these tools can enhance our understanding of NRP synthesis and the function of their biosynthetic enzymes. 

   more » « less
3. MIBiG 4.0: advancing biosynthetic gene cluster curation through global collaboration

   https://doi.org/10.1093/nar/gkae1115  

   Zdouc, Mitja M.; Blin, Kai; Louwen, Nico L_L; Navarro, Jorge; Loureiro, Catarina; Bader, Chantal D.; Bailey, Constance B.; Barra, Lena; Booth, Thomas J.; Bozhüyük, Kenan A_J; et al (December 2024, Nucleic Acids Research)

   Abstract Specialized or secondary metabolites are small molecules of biological origin, often showing potent biological activities with applications in agriculture, engineering and medicine. Usually, the biosynthesis of these natural products is governed by sets of co-regulated and physically clustered genes known as biosynthetic gene clusters (BGCs). To share information about BGCs in a standardized and machine-readable way, the Minimum Information about a Biosynthetic Gene cluster (MIBiG) data standard and repository was initiated in 2015. Since its conception, MIBiG has been regularly updated to expand data coverage and remain up to date with innovations in natural product research. Here, we describe MIBiG version 4.0, an extensive update to the data repository and the underlying data standard. In a massive community annotation effort, 267 contributors performed 8304 edits, creating 557 new entries and modifying 590 existing entries, resulting in a new total of 3059 curated entries in MIBiG. Particular attention was paid to ensuring high data quality, with automated data validation using a newly developed custom submission portal prototype, paired with a novel peer-reviewing model. MIBiG 4.0 also takes steps towards a rolling release model and a broader involvement of the scientific community. MIBiG 4.0 is accessible online at https://mibig.secondarymetabolites.org/. 

   more » « less
4. Computer-aided, resistance gene-guided genome mining for proteasome and HMG-CoA reductase inhibitors

   https://doi.org/10.1093/jimb/kuad045  

   Jenkinson, Cory B; Podgorny, Adam R; Zhong, Cuncong; Oakley, Berl R (January 2023, Journal of Industrial Microbiology and Biotechnology)

   Abstract  Secondary metabolites (SMs) are biologically active small molecules, many of which are medically valuable. Fungal genomes contain vast numbers of SM biosynthetic gene clusters (BGCs) with unknown products, suggesting that huge numbers of valuable SMs remain to be discovered. It is challenging, however, to identify SM BGCs, among the millions present in fungi, that produce useful compounds. One solution is resistance gene-guided genome mining, which takes advantage of the fact that some BGCs contain a gene encoding a resistant version of the protein targeted by the compound produced by the BGC. The bioinformatic signature of such BGCs is that they contain an allele of an essential gene with no SM biosynthetic function, and there is a second allele elsewhere in the genome. We have developed a computer-assisted approach to resistance gene-guided genome mining that allows users to query large databases for BGCs that putatively make compounds that have targets of therapeutic interest. Working with the MycoCosm genome database, we have applied this approach to look for SM BGCs that target the proteasome β6 subunit, the target of the proteasome inhibitor fellutamide B, or HMG-CoA reductase, the target of cholesterol reducing therapeutics such as lovastatin. Our approach proved effective, finding known fellutamide and lovastatin BGCs as well as fellutamide- and lovastatin-related BGCs with variations in the SM genes that suggest they may produce structural variants of fellutamides and lovastatin. Gratifyingly, we also found BGCs that are not closely related to lovastatin BGCs but putatively produce novel HMG-CoA reductase inhibitors. One-Sentence SummaryA new computer-assisted approach to resistance gene-directed genome mining is reported along with its use to identify fungal biosynthetic gene clusters that putatively produce proteasome and HMG-CoA reductase inhibitors. 

   more » « less
5. Genome mining of biosynthetic and chemotherapeutic gene clusters in Streptomyces bacteria

   https://doi.org/10.1038/s41598-020-58904-9  

   Belknap, Kaitlyn C.; Park, Cooper J.; Barth, Brian M.; Andam, Cheryl P. (February 2020, Scientific Reports)

   Abstract Streptomycesbacteria are known for their prolific production of secondary metabolites, many of which have been widely used in human medicine, agriculture and animal health. To guide the effective prioritization of specific biosynthetic gene clusters (BGCs) for drug development and targeting the most prolific producer strains, knowledge about phylogenetic relationships ofStreptomycesspecies, genome-wide diversity and distribution patterns of BGCs is critical. We used genomic and phylogenetic methods to elucidate the diversity of major classes of BGCs in 1,110 publicly availableStreptomycesgenomes. Genome mining ofStreptomycesreveals high diversity of BGCs and variable distribution patterns in theStreptomycesphylogeny, even among very closely related strains. The most common BGCs are non-ribosomal peptide synthetases, type 1 polyketide synthases, terpenes, and lantipeptides. We also found that numerousStreptomycesspecies harbor BGCs known to encode antitumor compounds. We observed that strains that are considered the same species can vary tremendously in the BGCs they carry, suggesting that strain-level genome sequencing can uncover high levels of BGC diversity and potentially useful derivatives of any one compound. These findings suggest that a strain-level strategy for exploring secondary metabolites for clinical use provides an alternative or complementary approach to discovering novel pharmaceutical compounds from microbes. 

   more » « less