New Research In
Articles by Topic
- Agricultural Sciences
- Applied Biological Sciences
- Biophysics and Computational Biology
- Cell Biology
- Developmental Biology
- Environmental Sciences
- Immunology and Inflammation
- Medical Sciences
- Plant Biology
- Population Biology
- Psychological and Cognitive Sciences
- Sustainability Science
- Systems Biology
快乐十分钟彩票:Unified energetics analysis unravels SpCas9 cleavage activity for optimal gRNA design
This article requires a subscription to view the full text. If you have a subscription you may use the login form below to view the article. Access to this article can also be purchased.
Evaluation of the cleavage efficacy, including the on-target activity and off-target effects, for individual guide RNA (gRNA) in silico can help optimize application of CRISPR/Cas9 systems. Many bioinformatics models based on data-processing algorithms have been developed, but discrepancies in the identified key features that determine cleavage efficacy inhibit deep understanding of the cleavage mechanism and preclude further optimization of gRNA design. Here, we present a physical framework with rigorous free-energy analysis to bridge the gap between experimental structural studies and cleavage-efficacy evaluations. This tool simultaneously considers on-target activity and off-target effects in a unified framework, improves the prediction power in both realms for diverse spCas9 cleavage efficacy datasets, and is readily transferred to other CRISPR/Cas9 systems.
While CRISPR/Cas9 is a powerful tool in genome engineering, the on-target activity and off-target effects of the system widely vary because of the differences in guide RNA (gRNA) sequences and genomic environments. Traditional approaches rely on separate models and parameters to treat on- and off-target cleavage activities. Here, we demonstrate that a free-energy scheme dominates the Cas9 editing efficacy and delineate a method that simultaneously considers on-target activities and off-target effects. While data-driven machine-learning approaches learn rules to model particular datasets, they may not be as transferrable to new systems or capable of producing new mechanistic insights as principled physical approaches. By integrating the energetics of R-loop formation under Cas9 binding, the effect of the protospacer adjacent motif sequence, and the folding stability of the whole single guide RNA, we devised a unified, physical model that can apply to any cleavage-activity dataset. This unified framework improves predictions for both on-target activities and off-target efficiencies of spCas9 and may be readily transferred to other systems with different guide RNAs or Cas9 ortholog proteins.
- ?1To whom correspondence should be addressed. Email: .
Author contributions: D.Z. and S.-J.C. designed research; D.Z. performed research; D.Z., T.H., D.D., and S.-J.C. analyzed data; and D.Z., T.H., and S.-J.C. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
Data deposition: The source code for the uCRISPR algorithm and the original datasets have been deposited in GitHub, https://github.com/Vfold-RNA/uCRISPR.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1820523116/-/DCSupplemental.
Published under the PNAS license.