From Context to Code: Rational De Novo DNA Design and Predicting Cross-Species DNA Functionality Using Deep Learning Transformer Models

Abstract

Synthetic biology currently operates under a framework dominated by trial-and-error approaches, which hinders the effective engineering of organisms and the expansion of large-scale biomanufacturing. Motivated by the success of computational designs in areas like architecture and aeronautics, we aspire to transition to a more efficient and predictive methodology in synthetic biology. In this study, we report a DNA Design Platform that relies on the predictive power of Transformer-based deep learning architectures. The platform transforms the conventional paradigms in synthetic biology by enabling the context-sensitive and host-specific engineering of 5' regulatory elements--promoters and 5' untranslated regions (UTRs) along with an array of codon-optimised coding sequence (CDS) variants. This allows us to generate context-sensitive 5' regulatory sequences and CDSs, achieving an unparalleled level of specificity and adaptability in different target hosts. With context-aware design, we significantly broaden the range of possible gene expression profiles and phenotypic outcomes, substantially reducing the need for laborious high-throughput screening efforts. Our context-aware, AI-driven design strategy marks a significant advancement in synthetic biology, offering a scalable and refined approach for gene expression optimisation across a diverse range of expression hosts. In summary, this study represents a substantial leap forward in the field, utilising deep learning models to transform the conventional design, build, test, learn-cycle into a more efficient and predictive framework.