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

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/.

Delving into the Bacillus cereus group biosynthetic gene clusters cosmos: a comparative-genomics-based classification framework

“…The classi cation of Biosynthetic Gene Clusters (BGCs) is an evolving eld that has gained signi cant attention in recent years. While some initial efforts have been made to classify BGCs, the eld is still relatively new and the classi cation methodologies are constantly being re ned and improved [22,85]. Our work is an objective proposal for a consistent and standardized approach to BGCs classi cation among the Bacillus cereus group, based on a reproducible strategy that can be extended to other taxa, allowing comparison and integration of data from different studies to expand the initial classi cation scheme that we proposed.…”

“…With recent developments in next-generation sequencing and advancements in genome mining tools, it became possible to computationally identify thousands of BGCs and draw a global map of BGCs within a group of bacteria that allow us to systematically explore those of interest [9].To overcome this challenge, researchers are increasingly using bioinformatics tools such as ClusterFinder [18], antiSMASH [19], and Big-scape [20], which can help automate the process of BGCs identi cation and classi cation. Additionally, efforts are being made to establish a standardized nomenclature for BGCs and to create a comprehensive database of BGCs, which lead to the establishment of the MIBiG database [21,22] which can help facilitate data sharing and comparison among researchers.…”

The 2023 Nucleic Acids Research Database Issue and the online molecular biology database collection

“…Elsewhere, the cornerstone resource in pathway analysis KEGG ( 41 ) contributes an update reporting on new genome and taxonomy browsers, while the equally foundational database STRING ( 42 ) reports on new co-expression data sources, improved interaction confidence estimates and the ability to process whole new genomes. Other well-used returning databases include MIBiG, whose update paper ( 43 ) has an interesting focus on annotation, including online ‘annotathons’; and SIGNOR ( 44 ) which has new data, a new interface, and new links to related projects focusing on diseases, including COVID-19. Finally, the new CovInter database ( 45 ) captures data on interactions between Coronavirus RNAs and host proteins.…”

Linking Lichen Metabolites to Genes: Emerging Concepts and Lessons from Molecular Biology and Metagenomics

“…The BGC-identifying algorithms, such as antiSMASH [ 45 , 56 ], predict the clusters present in the genomes and classify them according to the core gene that is present in them as an NRPS cluster, PKS cluster, terpene cluster, etc. AntiSMASH also compares the BGCs to the MiBiG [ 57 ], the database of BGCs characterized from plants, bacteria, and fungi, to identify those BGCs that are most structurally and functionally similar. This step already narrows down the candidate gene/cluster search substantially.…”