Guide-specific loss of efficiency and off-target reduction with Cas9 variants

Quantifying Protein-Nucleic Acid Interactions for Engineering Useful CRISPR-Cas9 Genome-Editing Variants

Numerous high-specificity Cas9 variants have been engineered for precision genome editing. These variants typically harbor multiple mutations designed to alter the Cas9-single guide RNA (sgRNA)-DNA complex interactions for reduced off-target cleavage. By dissecting the contributions of individual mutations, we attempt to derive principles for designing high-specificity Cas9 variants. Here, we computationally modeled the specificity harnessing mutations of the widely used Cas9 isolated from Streptococcus pyogenes (SpCas9) and investigated their individual mutational effects. We quantified the mutational effects in terms of energy and contact changes by comparing the wild-type and mutant structures. We found that these mutations disrupt the protein-protein or protein-DNA contacts within the Cas9-sgRNA-DNA complex. We also identified additional impacted amino acid sites via energy changes that constitute the structural microenvironment encompassing the focal mutation, giving insights into how the mutations contribute to the high-specificity phenotype of SpCas9. Our method outlines a strategy to evaluate mutational effects that can facilitate rational design for Cas9 optimization.

Recent advances of CRISPR-based genome editing for enhancing staple crops

An increasing population, climate change, and diminishing natural resources present severe threats to global food security, with traditional breeding and genetic engineering methods often falling short in addressing these rapidly evolving challenges. CRISPR/Cas systems have emerged as revolutionary tools for precise genetic modifications in crops, offering significant advancements in resilience, yield, and nutritional value, particularly in staple crops like rice and maize. This review highlights the transformative potential of CRISPR/Cas technology, emphasizing recent innovations such as prime and base editing, and the development of novel CRISPR-associated proteins, which have significantly improved the specificity, efficiency, and scope of genome editing in agriculture. These advancements enable targeted genetic modifications that enhance tolerance to abiotic stresses as well as biotic stresses. Additionally, CRISPR/Cas plays a crucial role in improving crop yield and quality by enhancing photosynthetic efficiency, nutrient uptake, and resistance to lodging, while also improving taste, texture, shelf life, and nutritional content through biofortification. Despite challenges such as off-target effects, the need for more efficient delivery methods, and ethical and regulatory concerns, the review underscores the importance of CRISPR/Cas in addressing global food security and sustainability challenges. It calls for continued research and integration of CRISPR with other emerging technologies like nanotechnology, synthetic biology, and machine learning to fully realize its potential in developing resilient, productive, and sustainable agricultural systems.

Machine learning-based prediction models to guide the selection of Cas9 variants for efficient gene editing

The increasing emergence of Cas9 variants has attracted broad interest, as these variants were designed to expand CRISPR applications. New Cas9 variants typically feature higher editing efficiency, improved editing specificity, or alternative PAM sequences. To select Cas9 variants and gRNAs for high-fidelity and efficient genome editing, it is crucial to systematically quantify the editing performances of gRNAs and develop prediction models based on high-quality datasets. Using synthetic gRNA-target paired libraries and next-generation sequencing, we compared the activity and specificity of gRNAs of four SpCas9 variants. The nucleotide composition in the PAM-distal region had more influence on the editing efficiency of HiFi Cas9 and LZ3 Cas9. We further developed machine learning models to predict the gRNA efficiency and specificity for the four Cas9 variants. To aid users from broad research areas, the machine learning models for the predictions of gRNA editing efficiency within human genome sites are available on our website.