| Title |
Controlling gene expression with deep generative design of regulatory DNA |
| Authors |
Zrimec, Jan ; Fu, Xiaozhi ; Muhammad, Azam Sheikh ; Skrekas, Christos ; Jauniškis, Vykintas ; Speicher, Nora K ; Börlin, Christoph S ; Verendel, Vilhelm ; Chehreghani, Morteza Haghir ; Dubhashi, Devdatt ; Siewers, Verena ; David, Florian ; Nielsen, Jens ; Železniak, Aleksej |
| DOI |
10.1038/s41467-022-32818-8 |
| Full Text |
|
| Is Part of |
Nature communications.. Berlin : Nature Portfolio. 2022, vol. 13, iss. 1, art. no. 5099, p. [1-17].. eISSN 2041-1723 |
| Keywords [eng] |
gene expression ; regulatory DNA ; deep generative design |
| Abstract [eng] |
Design of de novo synthetic regulatory DNA is a promising avenue to control gene expression in biotechnology and medicine. Using mutagenesis typically requires screening sizable random DNA libraries, which limits the designs to span merely a short section of the promoter and restricts their control of gene expression. Here, we prototype a deep learning strategy based on generative adversarial networks (GAN) by learning directly from genomic and transcriptomic data. Our ExpressionGAN can traverse the entire regulatory sequence-expression landscape in a gene-specific manner, generating regulatory DNA with prespecified target mRNA levels spanning the whole gene regulatory structure including coding and adjacent non-coding regions. Despite high sequence divergence from natural DNA, in vivo measurements show that 57% of the highly-expressed synthetic sequences surpass the expression levels of highly-expressed natural controls. This demonstrates the applicability and relevance of deep generative design to expand our knowledge and control of gene expression regulation in any desired organism, condition or tissue. |
| Published |
Berlin : Nature Portfolio |
| Type |
Journal article |
| Language |
English |
| Publication date |
2022 |
| CC license |
|
