Model-directed generation of artificial CRISPR–Cas13a guide RNA sequences improves nucleic acid detection

• Published in: Nature biotechnology Vol. 43; no. 8; pp. 1266 - 1273
• Main Authors: Mantena, Sreekar, Pillai, Priya P., Petros, Brittany A., Welch, Nicole L., Myhrvold, Cameron, Sabeti, Pardis C., Metsky, Hayden C.
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.08.2025; Nature Publishing Group
• Subjects: 631/114/1305; 631/326/2521; Agriculture; Algorithms; Base Sequence; Bioinformatics; Biomedical and Life Sciences; Biomedical Engineering/Biotechnology; Biomedicine; Biotechnology; Brief Communication; Clustered Regularly Interspaced Short Palindromic Repeats - genetics; CRISPR; CRISPR-Cas Systems - genetics; Dengue fever; Design; Gene sequencing; Genomes; Life Sciences; Nucleic acids; Pathogens; Polymorphism; Ribonucleic acid; RNA; RNA, Guide, CRISPR-Cas Systems - genetics
• ISSN: 1087-0156; 1546-1696; 1546-1696
• 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. Model-directed generative design is applied to CRISPR–Cas13a guide RNAs, outperforming natural sequences.