Machine learning prediction of prime editing efficiency across diverse chromatin contexts.

Journal: Nature biotechnology
Published Date: Jun 21, 2024

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.

Authors

  • Nicolas Mathis
    Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland.
  • Ahmed Allam
    Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
  • András Tálas
    Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland.
  • Lucas Kissling
    Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland.
  • Elena Benvenuto
    Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland.
  • Lukas Schmidheini
    Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland.
  • Ruben Schep
    Oncode Institute, Netherlands Cancer Institute, Amsterdam, the Netherlands.
  • Tanav Damodharan
    Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland.
  • Zsolt Balázs
    Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
  • Sharan Janjuha
    Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland.
  • Eleonora I Ioannidi
    Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland.
  • Desirée Böck
    Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland.
  • Bas van Steensel
    Oncode Institute, Netherlands Cancer Institute, Amsterdam, the Netherlands.
  • Michael Krauthammer
    Yale School of Medicine, New Haven, CT.
  • Gerald Schwank
    Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland. schwank@pharma.uzh.ch.

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