Deep learning-driven de novo design of ribosome binding sites in Paracoccus denitrificans

Abstract

Paracoccus denitrificans is a heterotrophic nitrifying–aerobic denitrifying bacterium widely used in wastewater treatment and as a model system for studying electron transport chains, making it a promising chassis for environmental synthetic biology. Precise translational control of gene expression is essential for engineering desired traits in this organism, yet the design of functional ribosome binding sites (RBS) in P. denitrificans remains hindered by limited understanding of their sequence–activity relationships. To address this gap, we systematically profiled 1335 native RBS sequences via integrated transcriptomic and proteomic analyses. We found that RBS strength is predominantly governed by a purine-rich Shine–Dalgarno motif located 5–8 bp upstream of the start codon, with specific A/G patterns in this region serving as key determinants. Building on this dataset, we developed and compared CNN, BiLSTM, and Transformer models for RBS strength prediction; among them, the CNN achieved the highest predictive correlation (Pearson r = 0.68). Additionally, a WGAN-GP framework was implemented to generate novel RBS sequences, which were subsequently filtered and evaluated by the prediction framework to enable the design of RBSs with user-specified strengths. Experimental validation showed a relatively strong correlation between predicted and measured strengths (Pearson r = 0.75). This work establishes the first deep learning-enabled RBS design platform for P. denitrificans, offering a robust tool for precise translational regulation and advancing synthetic biology applications in environmental biotechnology.