Design prokaryotic cis-regulatory elements using language model.

ABSTRACT: Deep learning has successfully been applied to design cis-regulatory elements (CREs) for a few species, but a broadly applicable platform for generating functional promoters for thousands of prokaryotes remains lacking. In this study, we introduce a language model for prokaryotic CREs, referred to as PromoGen2, to design CREs without prior experimental data. PromoGen2 was pretrained on CREs derived from 17 000 prokaryotic genomes. It achieved the highest zero-shot prediction correlation of promoter strength across species, improving the average Spearman correlation from 0.27 to 0.50 compared to the best baseline, while reducing the number of parameters by 103. Artificial CREs designed with PromoGen2 demonstrated a 100% success rate in Escherichia coli, Bacillus subtilis, Bacillus lichenifo

SUBMITTER: Xia Y 

PROVIDER: S-EPMC12907563 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature