Center for Non-coding RNA in Technology and Health, Department of Veterinary and Animal Sciences, University of Copenhagen, Grønnegårdsvej 3, 1870 Frederiksberg, Denmark.
Genome Biol. 2018 Oct 26;19(1):177. doi: 10.1186/s13059-018-1534-x.
Recent experimental efforts of CRISPR-Cas9 systems have shown that off-target binding and cleavage are a concern for the system and that this is highly dependent on the selected guide RNA (gRNA) design. Computational predictions of off-targets have been proposed as an attractive and more feasible alternative to tedious experimental efforts. However, accurate scoring of the high number of putative off-targets plays a key role for the success of computational off-targeting assessment.
We present an approximate binding energy model for the Cas9-gRNA-DNA complex, which systematically combines the energy parameters obtained for RNA-RNA, DNA-DNA, and RNA-DNA duplexes. Based on this model, two novel off-target assessment methods for gRNA selection in CRISPR-Cas9 applications are introduced: CRISPRoff to assign confidence scores to predicted off-targets and CRISPRspec to measure the specificity of the gRNA. We benchmark the methods against current state-of-the-art methods and show that both are in better agreement with experimental results. Furthermore, we show significant evidence supporting the inverse relationship between the on-target cleavage efficiency and specificity of the system, in which introduced binding energies are key components.
The impact of the binding energies provides a direction for further studies of off-targeting mechanisms. The performance of CRISPRoff and CRISPRspec enables more accurate off-target evaluation for gRNA selections, prior to any CRISPR-Cas9 genome-editing application. For given gRNA sequences or all potential gRNAs in a given target region, CRISPRoff-based off-target predictions and CRISPRspec-based specificity evaluations can be carried out through our webserver at https://rth.dk/resources/crispr/ .
最近的 CRISPR-Cas9 系统实验研究表明,脱靶结合和切割是该系统的一个关注点,这在很大程度上取决于所选的向导 RNA(gRNA)设计。针对脱靶的计算预测已被提出作为一种有吸引力的、更可行的替代方案,以替代繁琐的实验工作。然而,准确地对大量潜在的脱靶进行评分对于计算性脱靶评估的成功起着关键作用。
我们提出了 Cas9-gRNA-DNA 复合物的近似结合能模型,该模型系统地结合了 RNA-RNA、DNA-DNA 和 RNA-DNA 双链体获得的能量参数。基于该模型,我们引入了两种新的 gRNA 选择的 CRISPR-Cas9 应用中的脱靶评估方法:CRISPRoff 用于为预测的脱靶分配置信分数,以及 CRISPRspec 用于测量 gRNA 的特异性。我们将这些方法与当前最先进的方法进行了基准测试,并表明它们与实验结果的一致性都更好。此外,我们还提供了支持系统的靶向切割效率和特异性之间的反比关系的重要证据,其中引入的结合能是关键组成部分。
结合能的影响为进一步研究脱靶机制提供了方向。CRISPRoff 和 CRISPRspec 的性能使 gRNA 选择的脱靶评估更加准确,在任何 CRISPR-Cas9 基因组编辑应用之前都可以进行。对于给定的 gRNA 序列或给定靶区中的所有潜在 gRNA,可以通过我们的网页服务器 https://rth.dk/resources/crispr/ 进行基于 CRISPRoff 的脱靶预测和基于 CRISPRspec 的特异性评估。