Machine learning prediction of prime editing efficiency across diverse chromatin contexts
Nicolas Mathis(University of Zurich), Ahmed Allam(University of Zurich), András Tálas(University of Zurich), Lucas Kissling(University of Zurich), Elena Benvenuto(University of Zurich), Lukas Schmidheini(University of Zurich), Ruben Schep(The Netherlands Cancer Institute), Tanav Damodharan(University of Zurich), Zsolt Balázs(University of Zurich), Sharan Janjuha(University of Zurich), Eleonora I. Ioannidi(University of Zurich), Desirée Böck(University of Zurich), Bas van Steensel(The Netherlands Cancer Institute), Michael Krauthammer(University of Zurich), Gerald Schwank(University of Zurich)
Cited by 53Open Access
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.