Abstract: A system for predicting an activity of small Cas9 using deep learning, including a sequence input unit receiving input data on a guide sequence and target sequence of small Cas9, a predictive model generator generating a small Cas9 activity predictive model by performing deep learning for learning a relationship between small Cas9 activity data obtained from the input data on the guide sequence and target sequence of small Cas9 received from the sequence input unit and features that affect small Cas9 activity, a candidate target sequence input unit receiving candidate target sequence of small Cas9, and an activity predictor predicting small Cas9 activity by applying candidate target sequence input in the candidate target sequence input unit to the predictive model generated in the predictive model generator.

Abstract: According to a system for predicting the efficiency and an outcome of a base editor by using deep learning, it is possible to select a base editor from among 63 base editors with various protospacer adjacent motif (PAM) compatibilities and sgRNA for efficient base editing, without extensive experiments. Therefore, the system may be usefully used in all fields where gene editing is applied, such as disease treatment by gene editing.

Abstract: Provided is a genome editing composition for prime editing including a prime editor protein and a prime editing guide RNA, for editing a Z-type mutation of alpha-1 antitrypsin deficiency. The composition according to an aspect includes both a pegRNA sequence capable of effectively editing the SERPINA1 gene and prime editor 2 (PE 2), and thus, may effectively deliver the prime editor to a cell, and act specifically for a target sequence of the SERPINA1 gene, enabling genome editing with high accuracy, and therefore, may be useful as a SERPINA1 gene editing platform. In addition, the composition is capable of correcting the Z-type mutation of the SERPINA1 gene, and may be used for treatment or prevention of alpha-1 antitrypsin deficiency.

Abstract: A system for predicting prime editing efficiency by using deep learning, including: an information input unit that receives an input of data on prime editing efficiency of a prime editor, a predictive model generator for generating prime editing efficiency predictive models by performing deep learning to learn a relationship between features affecting prime editing efficiency and prime editing efficiency, by using the data received from the information input unit, a candidate sequence input unit that receives an input of a candidate target sequence for prime editing; and an efficiency predictor for predicting prime editing efficiency by applying the candidate target sequence input into the candidate sequence input unit to an efficiency predictive model generated in the predictive model generator.

Abstract: The disclosure relates to a method of evaluating functions of cancer mutations using base editors and guide RNAs, an evaluation system for mutations, and a computer-readable recording medium in which is recorded a program for executing the method by a computer.