Generative machine learning of ADAR substrates for precise and efficient RNA editing

ABSTRACT

Adenosine Deaminase Acting on RNA (ADAR) converts adenosine to inosine within certain double-stranded RNA structures. However, ADAR’s promiscuous editing and poorly understood specificity hinder therapeutic applications. We present an integrated approach combining high-throughput screening (HTS) with generative deep learning to rapidly engineer efficient and specific guide RNAs (gRNAs) to direct ADAR’s activity to any target. Our HTS quantified ADAR-mediated editing across millions of unique gRNA sequences and structures, identifying key determinants of editing outcomes. We leveraged these data to develop DeepREAD (Deep learning forRNAEditing byADARDesign), a diffusion-based model that elucidates complex design rules to generate novel gRNAs outperforming existing design heuristics. DeepREAD’s gRNAs achieve highly efficient and specific editing, including challenging multi-site edits. We demonstrate DeepREAD’s therapeutic potential by designing gRNAs targeting the MECP2R168Xmutation associated with Rett syndrome, achieving both allelic specificity and species cross-reactivity. This approach significantly accelerates the development of ADAR-based RNA therapeutics for diverse genetic diseases.