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

• Published in: NAR genomics and bioinformatics Vol. 7; no. 1; p. lqae188
• Main Authors: Achterberg, Tristan, de Jong, Anne
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 07.01.2025
• Subjects: Binding sites; Chemotaxis; Flagella; Gene regulation; Genomes; Internet; Neural networks; Promoters; Sigma factor
• ISSN: 2631-9268; 2631-9268
• DOI: 10.1093/nargab/lqae188

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 insilico 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.