A mechanistic model improves off-target predictions and reveals the physical basis of Sp Cas9 fidelity
A mechanistic model improves off-target predictions and reveals the physical basis of Sp Cas9 fidelity
The SpCas9 endonuclease has become an important tool in gene-editing and basic science alike. Though easily programmed to target any sequence, SpCas9 also shows considerable activity over genomic off-targets. Many empirical facts regarding the targeting reaction have been established, but a comprehensive mechanistic description is still lacking—limiting fundamental understanding, our ability to predict off-target activity, and ultimately the safe adaptation of the SpCas9 toolkit for therapeutics. By mechanistically modelling the SpCas9 structure-function relationship, we simultaneously capture binding and cleavage dynamics for SpCas9 and Sp-dCas9 in terms of free-energies. When our model is trained on high-throughput data, we outperform state-of-the-art off-target prediction tools. Based on the biophysical parameters we extract, our model predicts the open, intermediate, and closed complex configurations described in single-molecule FRET experiments, and indicates that R-loop progression is tightly coupled to structural changes in the targeting complex. We further show that SpCas9 targeting kinetics are tuned for extended sequence specificity while maintaining on-target efficiency. Our approach can be used to characterize any other CRISPR derived nuclease, and contrasting future studies of high-fidelity variants with the SpCas9 benchmark we here provide will help elucidate the determinants of CRISPR fidelity and the path to increased specificity and efficiency in engineered systems.
Sanden Koen v.d.、Finkelstein Ilya J.、Depken Martin、Klein Misha、Jones Stephen K. Jr.、Eslami-Mossallam Behrouz、Smagt Constantijn v.d.、Hawkins John A.
Kavli Institute of NanoScience and Department of BioNanoScience, Delft University of TechnologyDepartment of Molecular Biosciences, University of Texas at Austin||Institute for Cellular and Molecular Biology, University of Texas at Austin||Center for Systems and Synthetic Biology, University of Texas at AustinKavli Institute of NanoScience and Department of BioNanoScience, Delft University of TechnologyKavli Institute of NanoScience and Department of BioNanoScience, Delft University of TechnologyDepartment of Molecular Biosciences, University of Texas at Austin||Institute for Cellular and Molecular Biology, University of Texas at Austin||Center for Systems and Synthetic Biology, University of Texas at AustinKavli Institute of NanoScience and Department of BioNanoScience, Delft University of TechnologyKavli Institute of NanoScience and Department of BioNanoScience, Delft University of TechnologyDepartment of Molecular Biosciences, University of Texas at Austin||Institute for Cellular and Molecular Biology, University of Texas at Austin||Center for Systems and Synthetic Biology, University of Texas at Austin||Oden Institute for Computational Engineering and Science, University of Texas at Austin
基础医学生物科学研究方法、生物科学研究技术分子生物学
Sanden Koen v.d.,Finkelstein Ilya J.,Depken Martin,Klein Misha,Jones Stephen K. Jr.,Eslami-Mossallam Behrouz,Smagt Constantijn v.d.,Hawkins John A..A mechanistic model improves off-target predictions and reveals the physical basis of Sp Cas9 fidelity[EB/OL].(2025-03-28)[2025-04-30].https://www.biorxiv.org/content/10.1101/2020.05.21.108613.点此复制
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