Model-directed generation of CRISPR-Cas13a guide RNAs designs artificial sequences that improve nucleic acid detection

Sreekar Mantena; Priya P Pillai; Brittany A Petros; Nicole L Welch; Cameron Myhrvold; Pardis C Sabeti; Hayden C Metsky

doi:10.1101/2023.09.20.557569

Model-directed generation of CRISPR-Cas13a guide RNAs designs artificial sequences that improve nucleic acid detection

bioRxiv [Preprint]. 2023 Sep 20:2023.09.20.557569. doi: 10.1101/2023.09.20.557569.

Authors

Sreekar Mantena^{1

2

3}, Priya P Pillai¹, Brittany A Petros^{1

4

5

6}, Nicole L Welch¹, Cameron Myhrvold⁷, Pardis C Sabeti^{1

8

9

10}, Hayden C Metsky¹

Affiliations

¹ Broad Institute of MIT and Harvard, Cambridge, MA, USA.
² Department of Statistics, Harvard University, Cambridge, MA, USA.
³ Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA.
⁴ Division of Health Sciences and Technology, Harvard Medical School and Massachusetts Institute of Technology, Cambridge, MA, USA.
⁵ Harvard/Massachusetts Institute of Technology, MD-PhD Program, Boston, MA, USA.
⁶ Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
⁷ Department of Molecular Biology, Princeton University, Princeton, NJ, USA.
⁸ Howard Hughes Medical Institute, Chevy Chase, MD, USA.
⁹ Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA.
¹⁰ Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

Abstract

Generating maximally-fit biological sequences has the potential to transform CRISPR guide RNA design as it has other areas of biomedicine. Here, we introduce model-directed exploration algorithms (MEAs) for designing maximally-fit, artificial CRISPR-Cas13a guides-with multiple mismatches to any natural sequence-that are tailored for desired properties around nucleic acid diagnostics. We find that MEA-designed guides offer more sensitive detection of diverse pathogens and discrimination of pathogen variants compared to guides derived directly from natural sequences, and illuminate interpretable design principles that broaden Cas13a targeting.

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Abstract

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