Machine learning prediction of prime editing efficiency across diverse chromatin contexts
Nicolas Mathis, Ahmed Allam, András Tálas, Lucas Kissling, Elena Benvenuto, Lukas Schmidheini, Ruben Schep, Tanav Damodharan, Zsolt Balázs, Sharan Janjuha, Eleonora I. Ioannidi, Desirée Böck, Bas van Steensel, Michael Krauthammer, Gerald Schwank
Abstract
The success of prime editing depends on the prime editing guide RNA (pegRNA) design and target locus. Here, we developed machine learning models that reliably predict prime editing efficiency. PRIDICT2.0 assesses the performance of pegRNAs for all edit types up to 15 bp in length in mismatch repair-deficient and mismatch repair-proficient cell lines and in vivo in primary cells. With ePRIDICT, we further developed a model that quantifies how local chromatin environments impact prime editing rates. A machine learning model for prime editing efficiency prediction takes into account chromatin context.