Unified energetics analysis unravels SpCas9 cleavage activity for optimal gRNA design

Abstract

While CRISPR/Cas9 is a powerful tool in genome engineering, the ontarget 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 machinelearning 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 offtarget efficiencies of spCas9 and may be readily transferred to other systems with different guide RNAs or Cas9 ortholog proteins.RNA folding | free-energy landscape | folding stability | CRISPR | Cas9

Massively parallel CRISPRi assays reveal concealed thermodynamic determinants of dCas12a binding

“…Similarly, the presence of mismatches may alter the crRNA-DNA duplex energy by ∆ϵ * , which in turn also yields a e −β∆ϵ * change in relative binding probabilities. Hence, the relative binding affinity between two targets that have different PAM sites, or between an intended target and an off-target candidate, is simply given by the binding sites' Boltzmann weight Our framework shares similarities with the uCRISPR model recently developed by Zhang et al to describe SpCas9 cleavage activity [31]. However, instead of testing our model using in vivo indel measurements performed in human cells (which can be imprecise due to cellular physiological factors [28][29][30]), we use a massively parallel CRISPRi assay to directly measure the sequence-specific PAM binding energies and the energetic costs associated with crRNA-DNA mismatches in E. coli bacteria.…”

“…To elucidate determinants of CRISPR-Cas12 offtarget binding, we combine a thermodynamic model of dCas12a binding with a rationally designed CRISPRi assays to map the binding energy landscape of a type V CRISPR-Cas system from Francisella novicida (FnCas12a) as it inspects and binds to its DNA targets. Our approach, inspired by a recent theoretical framework that employs a unified energetic analysis to predict S. pyogenes Cas9 (SpCas9) cleavage activity [31] and recently developed massively parallel multiplexed assays [32][33][34][35], aims to directly measure the energetic and thermodynamic determinants of CRISPR-Cas binding. In other words, our assays excludes sources of variation in DNA cleavage activity caused by unknown physiological factors [28][29][30] by only focusing on the steps leading to final DNA cleavage step.…”

Applying CRISPR-Cas9 Off-Target Editing on DNA based Steganography

“…1) Off-target effect decreases when the number of mismatches (including both base mismatches and bulges) between sgRNA and target sequence increases; 2) Cas9 is less tolerant with mismatches proximal to PAM. Later methods incorporate Cas9 domain knowledge, especially energetics parameters, and therefore can achieve better predication results [24]. 3 | P a g e www.ijacsa.thesai.org…”

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

“…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.…”