Selection of extended CRISPR RNAs with enhanced targeting and specificity.

As CRISPR effectors like Cas9 increasingly enter clinical trials for therapeutic gene editing, a future for personalized medicine will require efficient methods to protect individuals from the potential of off-target mutations that may also occur at specific sequences in their genomes that are similar to the therapeutic target. A Cas9 enzyme's ability to recognize their targets (and off-targets) are determined by the sequence of their RNA-cofactors (their guide RNAs or gRNAs). Here, we present a method to screen hundreds of thousands of gRNA variants with short, randomized 5' nucleotide extensions near its DNA-targeting segment-a modification that can increase gene editing specificity by orders of magnitude-to identify extended gRNAs (x-gRNAs) that effectively block any activity at those off-target sites while still maintaining strong activity at their intended targets. X-gRNAs that have been selected for specific target / off-target pairs can significantly out-perform other methods that reduce Cas9 off-target activity overall, like using Cas9 variants engineered for higher specificity in general, and we demonstrate their effectiveness in clinically-relevant gRNAs. Our streamlined approach to efficiently identify highly specific and active x-gRNAs provides a way to move beyond a one-size-fits-all model of high-fidelity CRISPR for safer and more effective personalized gene therapies.

Selection of Extended CRISPR RNAs with Enhanced Targeting and Specificity.

For a CRISPR guide RNA (gRNA) with a specific target but activity at known "off-target" sequences, we present a method to screen hundreds of thousands of gRNA variants with short, randomized 5' nucleotide extensions near its DNA-targeting segment-a modification that can increase Cas9 gene editing specificity by orders of magnitude with certain 5'- extension sequences, via some as-yet-unknown mechanism that makes de novo design of the extension sequence difficult to perform manually-to robustly identify extended gRNAs (x-gRNAs) that have been counter-selected against activity at those off-target sites and that exhibit significantly enhanced Cas9 specificity for their intended targets.

A Functional Approach to Solubility Parameter Computations.

The determination of solubility parameters for solutes represents a challenging mathematical problem of locating the central tendency of solvent affinity based on a limited set of data taken from experimental observations. At present, the most commonly used methods for computing solubility parameters of a solute require a binary classification of solvent affinity for the solute and employ a spherical/ellipsoidal compatibility region in the three-dimensional Hansen solubility parameter space. Utilizing a binary classification requires an arbitrary solubility threshold, and an ellipsoidal fitting model imposes a symmetry on the intermolecular forces that is rarely reflected by the experimental data. To overcome these issues, an approach that makes use of accurate solubility data to describe a three-dimensional solubility function, f, is introduced. The principles of the approach are discussed in detail and the procedures for constructing the solubility function and computing solubility parameters are described. An example using PCBM solubility data available in the literature demonstrates the new method. Lastly, a method that employs f as a predictor of solubility in arbitrary solvents with a proposed measure of reliability is presented.