ProPr54 web server: predicting σ⁵⁴ promoters and regulon with a hybrid convolutional and recurrent deep neural network

σ⁵⁴ 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 σ⁵⁴ 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 σ⁵⁴ promoters and regulon in bacterial genomes. Here, we report a σ⁵⁴ promoter prediction method ProPr54, based on a convolutional neural network trained on a set of 446 validated σ⁵⁴ 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 σ⁵⁴ regulon identification methods. Furthermore, analysis on bacterial genomes, which have no experimentally validated σ⁵⁴ binding sites, demonstrates the generalization of the model. ProPr54 is the first reliable in silico method for predicting σ⁵⁴ binding sites, making it a valuable tool to support experimental studies on σ⁵⁴. In conclusion, ProPr54 offers a reliable, broadly applicable tool for predicting σ⁵⁴ promoters and regulon genes in bacterial genome sequences. A web server is freely accessible at http://propr54.molgenrug.nl.