Mechanistic machine learning for prediction of prime editing outcomes

Prime editing (PE) can make specific local changes to genomic DNA in living systems but its efficient application currently requires extensive optimization of PE guide RNA (pegRNA) sequences. Here we present OptiPrime, a machine learning model of PE efficiency based on current understanding of PE mechanisms. OptiPrime achieves state-of-the-art accuracy on PE efficiency prediction and enables prediction of nicking guide RNA (PE3) and dual pegRNA (twinPE) outcomes. We validate that OptiPrime has learned the determinants of mammalian mismatch repair (MMR) and is well suited for nominating MMR-evasive silent edits that improve PE efficiency. We demonstrate the use of OptiPrime in a variety of prospective therapeutic contexts in primary human and mouse cells. Lastly, we show that OptiPrime can be used to achieve streamlined and efficient in vivo correction of a pathogenic mutation in the brain of a mouse model of KIF1A-associated neurological disorder. We provide a webserver for OptiPrime ( https://optipri.me/ ) as a community resource.