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Machine-Learning-Inferred and Energy-Landscape-Guided Analyses Reveal Kinetic Determinants of CRISPR/Cas9 Gene Editing

Lei Jin and Risi Liyanage

Dongsheng Duan

Shi-Jie Chen*

  • Department of Molecular Microbiology and Immunology and Department of Neurology, School of Medicine, University of Missouri, Columbia, Missouri 65211, USA; Department of Biomedical Sciences, College of Veterinary Medicine, University of Missouri, Columbia, Missouri 65211, USA; and Department of Bioengineering, University of Missouri, Columbia, Missouri 65211, USA

  • Department of Physics and Astronomy and Department of Biochemistry, Institute of Data Sciences and Informatics, University of Missouri, Columbia, Missouri 65211, USA

  • *Contact author: chenshi@missouri.edu
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PRX Life 4, 013028 – Published 4 March, 2026

DOI: https://doi.org/10.1103/rtxh-6fdl

Abstract

The CRISPR-Cas9 nuclease system is currently the most powerful tool in genome engineering. Existing data-driven learning and thermal equilibrium-based modeling cannot capture the kinetic mechanism of the gene cleavage process. This study integrates large-scale genome editing data with a random forest approach—a supervised ensemble learning algorithm—and free energy landscape analysis to uncover the kinetic pathways and pivotal steps that govern cleavage efficiency. Major findings highlight a direct correlation between CRISPR cleavage efficiency and R-loop unfolding rate, establishing the R-loop unfolding rate as the critical factor in gene editing performance. The kinetic model significantly improves over other methods in predicting both on- and off-target gene editing efficiencies. Furthermore, energy landscape analysis reveals a novel three-state mechanism and identifies key nucleotides essential for gene editing. These insights lay the biophysical groundwork for new strategies to enhance CRISPR-Cas9 accuracy and efficiency, offering new avenues for single guide RNA design and target site selection.

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PRX Life 4, 013028– Published 4 March, 2026
  • Received 26 March 2025
  • Accepted 4 February 2026
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