Optimization of CRISPR-Cas9 via the synergy of MD simulation and machine learning

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dc.creatorLiu, Jin
dc.creatorWang, Duen-Shian
dc.creatorLiang, Ivy
dc.date.accessioned2021-04-30T17:39:34Z
dc.date.available2021-04-30T17:39:34Z
dc.date.issued2021
dc.descriptionResearch Appreciation Day Award Winner - 2021 UNT System College of Pharmacy Pharmaceutical Science Research Award - 1st Place
dc.descriptionResearch Appreciation Day Award Winner - 2021 UNT System College of Pharmacy Pharmaceutical Science Research Award - 1st Placeen_US
dc.description.abstractCRISPR-Cas9, a promising gene-editing tool, sheds light on gene therapy. The normal DNA cleavage of CRISPR-Cas9 is programmed by a guide RNA (gRNA) template. However, recent studies showed that Cas9 cleavage occurs even without guidance from the gRNA in the presence of Mn2+ ions, implying the issue of off-target effect of Cas9. Here, we report a mechanism of this RNA-independent off-target cleavage (RI-cleavage) elucidated by molecular dynamic (MD) simulations. We further used machine learning algorithms developed by our lab to facilitate the design of novel Cas9 variants to reduce such RI-cleavage. In this study, we revealed the possible mechanism of RI-cleavage and further engineered Cas9 to reduce RI-cleavage via the power of machine learning. Our research serves as an excellent example showing the potential in the synergy of MD simulation and machine learning to optimize CRISPR-Cas9.
dc.description.sponsorshipNIH R15 (9R15HL147265-01A1)
dc.identifier.urihttps://hdl.handle.net/20.500.12503/30609
dc.language.isoen
dc.titleOptimization of CRISPR-Cas9 via the synergy of MD simulation and machine learning
dc.typeposter
dc.type.materialtext

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Molecular Genetics