# US 2023/0274792 A1 · System and method for prime editing efficiency prediction using deep learning > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2023/0274792 A1 - Document ID: US20230274792A1 - Status: Published application - Kind: A1 - Application number: 18/007,241 - Filed: 2021-07-28 - Published: 2023-08-31 - Earliest claimed: 2020-07-29 - Assignees: Industry-Academic Cooperation Foundation, Yonsei University - Applicants: Industry-Academic Cooperation Foundation, Yonsei University - Inventors: Hyongbum Henry KIM; Hui Kwon KIM; Goo Sang YU - Web page: https://patentgrep.com/patent/US20230274792A1 ## CPC - G16B 25/00: ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression - C12N 15/1082: {Preparation or screening gene libraries by chromosomal integration of polynucleotide sequences, HR-, site-specific-recombination, transposons, viral vectors} - C12N 15/1089: {Design, preparation, screening or analysis of libraries using computer algorithms} - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/09: Supervised learning - G16B 30/10: Sequence alignment; Homology search - G16B 35/00: ICT specially adapted for in silico combinatorial libraries of nucleic acids, proteins or peptides - G16B 40/20: Supervised data analysis - G06N 3/048: Activation functions - G06N 3/082: modifying the architecture, e.g. adding, deleting or silencing nodes or connections - G06N 3/092: Reinforcement learning - G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound - G06N 5/045: Explanation of inference; Explainable artificial intelligence [XAI]; Interpretable artificial intelligence - G06N 20/20: Ensemble learning - G16B: BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY - C12N: MICROORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING, OR MAINTAINING MICROORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA (microbiological testing media C12Q1/00) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## 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. ## Claims ### Claim 1 A system for predicting prime editing efficiency by using deep learning, comprising: 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. ### Claim 2 (depends on 1) The system for predicting prime editing efficiency by using deep learning of claim 1, wherein the prime editor is prime editor 2. ### Claim 3 (depends on 1) The system for predicting prime editing efficiency by using deep learning of claim 1, wherein the prime editing efficiency is represented by a rate of occurrence of intended edits by a prime editor and pegRNA at a target sequence without generation of an unintended mutant. ### Claim 4 (depends on 1) The system for predicting prime editing efficiency by using deep learning of claim 1, wherein the data on prime editing efficiency is obtained by performing a method comprising: introducing a prime editor into a cell library including an oligonucloeitde including a nucleotide sequence encoding pegRNA and a target nucleotide sequence targeted by the pegRNA; performing deep sequencing by using DNA obtained from the cell library into which the prime editor has been introduced; and analyzing prime editing efficiency from data obtained by the deep sequencing. ### Claim 5 (depends on 4) The system for predicting prime editing efficiency by using deep learning of claim 4, wherein the oligonucleotide further comprises a barcode sequence. ### Claim 6 (depends on 1) The system for predicting prime editing efficiency by using deep learning of claim 1, wherein the features affecting prime editing efficiency are extracted from information about pegRNA and a target sequence. ### Claim 7 (depends on 6) The system for predicting prime editing efficiency by using deep learning of claim 6, wherein the information about the pegRNA and the target sequence comprises at least one of information about a reverse transcriptase (RT) template sequence, information about a primer binding site (PBS) sequence, and information about the target sequence. ### Claim 8 (depends on 1) The system for predicting prime editing efficiency by using deep learning of claim 1, wherein the predictive model generator comprises a feature extraction module for extracting features affecting prime editing efficiency from information about pegRNA and a target sequence. ### Claim 9 (depends on 1) The system for predicting prime editing efficiency by using deep learning of claim 1, wherein the predictive model generator performs deep learning based on a convolutional neural network (CNN) or a multilayer perceptron (MLP). ### Claim 10 (depends on 1) The system for predicting prime editing efficiency by using deep learning of claim 1, wherein the candidate target sequence comprises a protospacer adjacent motif (PAM), and a protospacer sequence. ### Claim 11 (depends on 1) The system for predicting prime editing efficiency by using deep learning of claim 1, wherein the efficiency predictor predicts prime editing efficiency of candidate target sequences by a prime editor and pegRNA. ### Claim 12 (depends on 1) The system for predicting prime editing efficiency by using deep learning of claim 1, further comprising an output unit for outputting the prime editing efficiency predicted by the efficiency predictor. ### Claim 13 A method of building a system for predicting prime editing efficiency by using deep learning, comprising: obtaining a prime editing efficiency data set of a prime editor; and 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 prime editing efficiency data set. ### Claim 14 (depends on 13) The method of building a system for predicting prime editing efficiency by using deep learning of claim 13, wherein the obtaining the efficiency data set comprises: introducing a prime editor into a cell library including an oligonucloeitde including a nucleotide sequence encoding pegRNA and a target nucleotide sequence targeted by the pegRNA; performing deep sequencing by using DNA obtained from the cell library into which the prime editor has been introduced; and analyzing prime editing efficiency from data obtained by the deep sequencing. ### Claim 15 (depends on 13) The method of building a system for predicting prime editing efficiency by using deep learning of claim 13, wherein the prime editing efficiency is calculated by a rate of occurrence of intended edits by a prime editor and pegRNA at a target sequence without generation of an unintended mutant. ### Claim 16 (depends on 13) The method of building a system for predicting prime editing efficiency by using deep learning of claim 13, wherein the features affecting prime editing efficiency are extracted from information about pegRNA and a target sequence. ### Claim 17 (depends on 16) The method of building a system for predicting prime editing efficiency by using deep learning of claim 16, wherein the information about the pegRNA and the target sequence comprises at least one of information about an RT template sequence, information about a PBS sequence, and information about the target sequence. ### Claim 18 (depends on 13) The method of building a system for predicting prime editing efficiency by using deep learning of claim 13, wherein in the generating of the predictive models, deep learning is performed based on a convolutional neural network (CNN) or a multilayer perceptron (MLP). ### Claim 19 (depends on 1) A method of predicting prime editing efficiency, comprising: designing candidate target sequences for prime editing; and predicting prime editing efficiency by applying the designed candidate target sequences to the system for predicting prime editing efficiency of claim 1. ### Claim 20 (depends on 19) A computer-readable recording medium on which a program for executing the method according to claim 19 on a computer is recorded.