biostudies-literatureUnknown19(5)Rios-Martinez CNatural products are chemical compounds that form the basis of many therapeutics used in the pharmaceutical industry. In microbes, natural products are synthesized by groups of colocalized genes called biosynthetic gene clusters (BGCs). With advances in high-throughput sequencing, there has been an increase of complete microbial isolate genomes and metagenomes, from which a vast number of BGCs are undiscovered. Here, we introduce a self-supervised learning approach designed to identify and characterize BGCs from such data. To do this, we represent BGCs as chains of functional protein domains and train a masked language model on these domains. We assess the ability of our approach to detect BGCs and characterize BGC properties in bacterial genomes. We also demonstrate that our model can leaPLoS computational biologye1011162https://www.ebi.ac.uk/biostudies/studies/S-EPMC10241353biostudies-literatureDeep self-supervised learning for biosynthetic gene cluster detection and product classification.PMC10241353Yang KKBhattacharya NCrawford LAmini APRios-Martinez CfalseDeep self-supervised learning for biosynthetic gene cluster detection and product classification.Natural products are chemical compounds that form the basis of many therapeutics used in the pharmaceutical industry. In microbes, natural products are synthesized by groups of colocalized genes called biosynthetic gene clusters (BGCs). With advances in high-throughput sequencing, there has been an increase of complete microbial isolate genomes and metagenomes, from which a vast number of BGCs are undiscovered. Here, we introduce a self-supervised learning approach designed to identify and characterize BGCs from such data. To do this, we represent BGCs as chains of functional protein domains and train a masked language model on these domains. We assess the ability of our approach to detect BGCs and characterize BGC properties in bacterial genomes. We also demonstrate that our model can lea2023-01-01T00:00:00Z2023 May2025-04-04T14:39:59.32Z2025-04-04T14:39:59.32ZS-EPMC102413533722015110.1371/journal.pcbi.1011162