한빛사 논문
Hui Kwon Kima,b,c,d, Sungtae Leea, Younggwang Kima,b, Jinman Parka,b, Seonwoo Mine, Jae Woo Choia,f, Tony P. Huangg,h,i, Sungroh Yoone,j, David R. Liug,h,i & Hyongbum Henry Kima,b,c,d,f,*
aDepartment of Pharmacology, Yonsei University College of Medicine, Seoul, Republic of Korea
bBrain Korea 21 Plus Project for Medical Sciences, Yonsei University College of Medicine, Seoul, Republic of Korea
cCenter for Nanomedicine, Institute for Basic Science (IBS), Seoul, Republic of Korea
dYonsei-IBS Institute, Yonsei University, Seoul, Republic of Korea
eElectrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
fSeverance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, Republic of Korea
gMerkin Institute of Transformative Technologies in Healthcare, Broad Institute of MIT and Harvard, Cambridge, MA, USA
hHoward Hughes Medical Institute, Harvard University, Cambridge, MA, USA
iDepartment of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
jInterdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
*Correspondence to Hyongbum Henry Kim
Abstract
The applications of clustered regularly interspaced short palindromic repeats (CRISPR)-based genome editing can be limited by a lack of compatible protospacer adjacent motifs (PAMs), insufficient on-target activity and off-target effects. Here, we report an extensive comparison of the PAM-sequence compatibilities and the on-target and off-target activities of Cas9 from Streptococcus pyogenes (SpCas9) and the SpCas9 variants xCas9 and SpCas9-NG (which are known to have broader PAM compatibility than SpCas9) at 26,478 lentivirally integrated target sequences and 78 endogenous target sites in human cells. We found that xCas9 has the lowest tolerance for mismatched target sequences and that SpCas9-NG has the broadest PAM compatibility. We also show, on the basis of newly identified non-NGG PAM sequences, that SpCas9-NG and SpCas9 can edit six previously unedited endogenous sites associated with genetic diseases. Moreover, we provide deep-learning models that predict the activities of xCas9 and SpCas9-NG at the target sequences. The resulting deeper understanding of the activities of xCas9, SpCas9-NG and SpCas9 in human cells should facilitate their use.
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