Programmatic design and editing of cis-regulatory elements

The development of modern genome editing and DNA synthesis has enabled researchers to edit DNA sequences with high precision but has left unsolved the problem of designing these edits. We introduce Ledidi, a computational method that rephrases the discrete design task of choosing which edits to make as an easily solvable continuous optimization problem. Ledidi can use any pre-trained deep learning model to guide the optimization, yielding an edited sequence that exhibits the desired outcome while explicitly minimizing the number of edits used. When applied in dozens of settings, we find that Ledidis designs can precisely control transcription factor binding, chromatin accessibility, transcription, and enhancer activity in silico. By using several deep learning models simultaneously, we design cell type-specific enhancers and experimentally validate them in cellulo. Finally, we introduce the concept of an "affinity catalog", where the design task is repeated multiple times across continuous variants of the design target. We demonstrate how these catalogs can be used to interpret deep learning models and the impact of starting template sequences, and also to design regulatory elements that control transcriptional dosage while maintaining cell type-specificity.