Mantena Sreekar, Pillai Priya P, Petros Brittany A, Welch Nicole L, Myhrvold Cameron, Sabeti Pardis C, Metsky Hayden C
Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Department of Statistics, Harvard University, Cambridge, MA, USA.
Nat Biotechnol. 2024 Oct 11. doi: 10.1038/s41587-024-02422-w.
CRISPR guide RNA sequences deriving exactly from natural sequences may not perform optimally in every application. Here we implement and evaluate algorithms for designing maximally fit, artificial CRISPR-Cas13a guides with multiple mismatches to natural sequences that are tailored for diagnostic applications. These guides offer more sensitive detection of diverse pathogens and discrimination of pathogen variants compared with guides derived directly from natural sequences and illuminate design principles that broaden Cas13a targeting.
完全源自天然序列的CRISPR引导RNA序列在每种应用中可能都无法达到最佳性能。在此，我们实施并评估了一些算法，用于设计与天然序列有多个错配的、最适配的人工CRISPR-Cas13a引导序列，这些序列是为诊断应用量身定制的。与直接源自天然序列的引导序列相比，这些引导序列能更灵敏地检测多种病原体并区分病原体变体，还阐明了拓宽Cas13a靶向范围的设计原则。
