Nature biotechnologyAug 12, 2026

Using machine learning to predict results of prime genome editing

CRISPR Gene Editing Weekly Brief ↗PubMed ↗DOI ↗OA ↗

Updated Aug 14, 2026

Abstract

OptiPrime achieves state-of-the-art accuracy in predicting prime editing efficiency.

A machine learning model was developed to optimize prime editing guide RNA sequences.
OptiPrime predicts outcomes for both nicking guide RNA and dual pegRNA configurations.
The model has learned factors affecting mammalian mismatch repair, which may enhance editing efficiency.
OptiPrime was validated in primary human and mouse cells for various therapeutic applications.
In a mouse model, OptiPrime facilitated efficient in vivo correction of a pathogenic mutation associated with a neurological disorder.

Simplified

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.