Species-specific design of artificial promoters by transfer-learning based generative deep-learning model.

ABSTRACT: Native prokaryotic promoters share common sequence patterns, but are species dependent. For understudied species with limited data, it is challenging to predict the strength of existing promoters and generate novel promoters. Here, we developed PromoGen, a collection of nucleotide language models to generate species-specific functional promoters, across dozens of species in a data and parameter efficient way. Twenty-seven species-specific models in this collection were finetuned from the pretrained model which was trained on multi-species promoters. When systematically compared with native promoters, the Escherichia coli- and Bacillus subtilis-specific artificial PromoGen-generated promoters (PGPs) were demonstrated to hold all distribution patterns of native promoters. A regression model

SUBMITTER: Xia Y 

PROVIDER: S-EPMC11194083 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature