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Obtaining new genetic regulatory elements has tremendous usage in metabolic engineering and synthetic biology applications. In this work, we introduced an artificial intelligence (AI) framework to create new promoter sequences in E. coli. We 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. Link: https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkaa325/5837049 NAR Breakthrough： http://www.narbreakthrough.com 2020-12-25 17:32:43.0 |
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