Deep flanking sequence engineering for efficient promoter design using DeepSEED

Designing promoters with desirable properties is essential in synthetic biology. Human experts are skilled at identifying strong explicit patterns in small samples, while deep learning models excel at detecting implicit weak patterns in large datasets. Biologists have described the sequence patterns of promoters via transcription factor binding sites (TFBSs). However, the flanking sequences of cis-regulatory elements, have long been overlooked and often arbitrarily decided in promoter design. To address this limitation, we introduce DeepSEED, an AI-aided framework that efficiently designs synthetic promoters by combining expert knowledge with deep learning techniques. DeepSEED has demonstrated success in improving the properties of Escherichia coli constitutive, IPTG-inducible, and mammalian cell doxycycline (Dox)-inducible promoters. Furthermore, our results show that DeepSEED captures the implicit features in flanking sequences, such as k-mer frequencies and DNA shape features, which are crucial for determining promoter properties. Designing promoters with desired properties is crucial in synthetic biology. Here, authors introduce DeepSEED, an AI-aided flanking sequence optimisation framework which combines expert knowledge with deep learning techniques to efficiently design promoters in both eukaryotic and prokaryotic cells.

Automated summary

Designing promoters with desirable properties is crucial in synthetic biology. DeepSEED, an AI-aided framework that combines expert knowledge with deep learning techniques, has been successful in efficiently designing synthetic promoters and capturing implicit features in flanking sequences.