Silvia Ribeiro Monteiro, Augustin Rigolet, Clément Jeunehomme, Julianne Gathot, Yasmine Kerdel, Matthias Henry, Hannah E. Augustijn, Marnix H Medema, Gilles P van Wezel, Sébastien Rigali
Bacterial genomes contain thousands of biosynthetic gene clusters (BGCs) responsible for the production of structurally diverse natural products with applications in medicine, agriculture, and biotechnology. Expression of these BGCs is tightly regulated by transcription factors (TFs) responding to environmental cues, yet predicting which TFs regulate specific BGCs remains challenging. In particular, TF binding sites (TFBSs) within BGCs often diverge from canonical motifs, limiting the effectiveness of standard motif-scanning approaches and hindering systematic exploration of BGC regulation. Here, we present COMMBAT (COnditions for Microbial Metabolite Biosynthesis Activated Transcription), a framework for large-scale prediction of TF-BGC regulatory interactions across bacterial genomes. COMMBAT integrates motif matching with genomic context and gene function information to predict functional TFBSs. The COMMBAT web platform (https://www.commbat.uliege.be) enables users to (i) identify BGCs potentially regulated by a given TF, and (ii) predict candidate TFs that control a specific BGC. With over 4000 TF position weight matrices from four public repositories and more than 400 000 BGCs from MIBiG and antiSMASH DB, COMMBAT provides a scalable resource to predict regulatory inputs and guide/prioritize culture conditions and genetic engineering strategies for natural product discovery.