ProPr54 web server: predicting σ promoters and regulon with a hybrid convolutional and recurrent deep neural network.

σ serves as an unconventional sigma factor with a distinct mechanism of transcription initiation, which depends on the involvement of a transcription activator. This unique sigma factor σ is indispensable for orchestrating the transcription of genes crucial to nitrogen regulation, flagella biosynthesis, motility, chemotaxis and various other essential cellular processes. Currently, no comprehensive tools are available to determine σ promoters and regulon in bacterial genomes. Here, we report a σ promoter prediction method ProPr54, based on a convolutional neural network trained on a set of 446 validated σ binding sites derived from 33 bacterial species. Model performance was tested and compared with respect to bacterial intergenic regions, demonstrating robust applicability. ProPr54 exhibits high performance when tested on various bacterial species, highly surpassing other available σ regulon identification methods. Furthermore, analysis on bacterial genomes, which have no experimentally validated σ binding sites, demonstrates the generalization of the model. ProPr54 is the first reliable method for predicting σ binding sites, making it a valuable tool to support experimental studies on σ. In conclusion, ProPr54 offers a reliable, broadly applicable tool for predicting σ promoters and regulon genes in bacterial genome sequences. A web server is freely accessible at http://propr54.molgenrug.nl.