한빛사 논문
Nahye Kim1,2,10, Hui Kwon Kim 1,2,3,4,10, Sungtae Lee1, Jung Hwa Seo 2,5, Jae Woo Choi1, Jinman Park1,2, Seonwoo Min6, Sungroh Yoon 6,7, Sung-Rae Cho2,5,8 and Hyongbum Henry Kim1,2,3,4,8,9,*
1Department of Pharmacology, Yonsei University College of Medicine, Seoul, Republic of Korea. 2Brain Korea 21 Plus Project for Medical Sciences, Yonsei University College of Medicine, Seoul, Republic of Korea. 3Center for Nanomedicine, Institute for Basic Science (IBS), Seoul, Republic of Korea. 4Graduate Program of Nano Biomedical Engineering (NanoBME), Advanced Science Institute, Yonsei University, Seoul, Republic of Korea. 5Department and Research Institute of Rehabilitation Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea. 6Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea. 7Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea. 8Graduate Program of NanoScience and Technology, Yonsei University, Seoul, Republic of Korea. 9Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, Republic of Korea. 10These authors contributed equally: Nahye Kim, Hui Kwon Kim.
*Corresponding author
Abstract
Several Streptococcus pyogenes Cas9 (SpCas9) variants have been developed to improve an enzyme’s specificity or to alter or broaden its protospacer-adjacent motif (PAM) compatibility, but selecting the optimal variant for a given target sequence and application remains difficult. To build computational models to predict the sequence-specific activity of 13 SpCas9 variants, we first assessed their cleavage efficiency at 26,891 target sequences. We found that, of the 256 possible four-nucleotide NNNN sequences, 156 can be used as a PAM by at least one of the SpCas9 variants. For the high-fidelity variants, overall activity could be ranked as SpCas9 ≥ Sniper-Cas9 > eSpCas9(1.1) > SpCas9-HF1 > HypaCas9 ≈ xCas9 >> evoCas9, whereas their overall specificities could be ranked as evoCas9 >> HypaCas9 ≥ SpCas9-HF1 ≈ eSpCas9(1.1) > xCas9 > Sniper-Cas9 > SpCas9. Using these data, we developed 16 deep-learning-based computational models that accurately predict the activity of these variants at any target sequence.
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TOP52020년 후보
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