In-silico phenotype prediction by normal mode variant analysis in TUBB4A-related disease

Computational normal mode analysis accurately replicates the activity and specificity profiles of CRISPR-Cas9 and high-fidelity variants

The CRISPR-Cas system has transformed the field of gene-editing and created opportunities for novel genome engineering therapeutics. The field has significantly progressed, and recently, CRISPR-Cas9 was utilized in clinical trials to target disease-causing mutations. Existing tools aim to predict the on-target efficacy and potential genome-wide off-targets by scoring a particular gRNA according to an array of gRNA design principles or machine learning algorithms based on empirical results of large numbers of gRNAs. However, such tools are unable to predict the editing outcome by variant Cas enzymes and can only assess potential off-targets related to reference genomes. Here, we employ normal mode analysis (NMA) to investigate the structure of the Cas9 protein complexed with its gRNA and target DNA and explore the function of the protein. Our results demonstrate the feasibility and validity of NMA to predict the activity and specificity of SpyCas9 in the presence of mismatches by comparison to empirical data. Furthermore, despite the absence of their exact structures, this method accurately predicts the enzymatic activity of known high-fidelity engineered Cas9 variants.