CRISPR-Cas9 off-targeting assessment with nucleic acid duplex energy parameters

Abstract

BackgroundRecent 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.ResultsWe 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.ConclusionsThe 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/.Electronic supplementary materialThe online version of this article (10.1186/s13059-018-1534-x) contains supplementary material, which is available to authorized users.

CRISPR/Cas “non-target” sites inhibit on-target cutting rates

“…To date numerous studies have in effect sampled this context by testing different species, strains, cell lines etc,. While several studies have investigated the impact of additional "off-target" in some cases non-target sites, 30,31 This variability has made predicting the on-target activity of a given gRNA sequence difficult. While the above results demonstrate the importance of the non-target pool in impacting Cas9 activity, significant future work is needed to understand the specific impact of the number, accessibility (non-target sites in chromatin, for example may not be accessible for Cas9 binding), and similarity of any group of non-target sites.…”

Genetic editing of the virulence gene of Escherichia coli using the CRISPR system

“…The specificity of the CRISPR/Cas9 system depends mainly on sgRNA (Alkan et al, 2018;Fu et al, 2014). The designed sgRNA may form a mismatch with nontarget DNA sequences, resulting in unintended gene mutations, that are called off-target effects (Zhang et al, 2015).…”

Optimized sgRNA design by deep learning to balance the off-target effects and on-target activity of CRISPR/Cas9

“…Several computational methods already exist to predict off-target sites and/or evaluate the specificity of the sgRNAs (18,28,33,(41)(42)(43)(44)(45)(46)(47)(48)(49)(50). Two main features are used to predict the specificity of the sgRNA: number and loci of mismatches, binding energy between sgRNA and target DNA.…”

“…These tools are designed to assist researchers in the selection of best target sites by helping them exclude undesirable targets based on predicted low efficiency or a high potential for off-target effects. They could be broadly divided into two groups, on-target cleavage efficiency tools (6,(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39) and off-target activity tools (18,28,33,(40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50)(51). In the on-target cleavage efficiency evaluation tools, researchers focus on identifying the gRNA sequence features that contribute to target cleavage efficiency.…”

“…More recent studies also pay attention to non-sequence information, such as location of the sgRNA target site within the gene and the thermodynamic stability of the sgRNA (39,45,55,56).…”