Synthetic promoter design in Escherichia coli based on a deep generative network

Ye Wang, Haochen Wang, Lei Wei, Shuailin Li, Liyang Liu, and Xiaowo Wang

Nucleic Acids Res (2020), gkaa325, doi:10.1093/nar/gkaa325

Obtaining new genetic regulatory elements has tremendous usage in metabolic engineering and synthetic biology applications. This work introduced an artificial intelligence (AI) framework to create new promoter sequences in E. coli. The authors trained adversarial deep neural networks to learn critical features from natural sequences, and then used the model to generate brand new promoters. These AI designed sequences showed large difference from the E. coli natural genome, but a high proportion of them were demonstrated to be functional. In theory, the model can generate thousands or even millions of new promoters. This work demonstrated the potential ability of AI to explore the huge potential combinations of nucleotide sequences in silico to obtain new optimized genetic elements.

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