Deep Learning Base Editor Prediction System
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Solution Overview
Problem
The choice of base editor variants and Cas9 variants for genome editing is confusing due to the lack of extensive comparisons, affecting the efficiency and fidelity of base editing, particularly due to PAM compatibility issues.
Innovation Solution
A deep learning-based system, DeepCas9variants and DeepBE, predicts the efficiency and outcome of base editors by combining convolutional neural networks with extensive data sets of Cas9 and base editor variants, generating prediction scores for Cas9 activity and base editing efficiency and outcome.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple base editor variants and Cas9 variants are developed to improve base editing efficiency and PAM compatibility, then the versatility and adaptability of base editing are improved, but the device complexity and difficulty of selection increase
Solution Approach 1:
The patent creates comprehensive training datasets that copy and consolidate performance data from multiple base editor variants and Cas9 variants across numerous target sequences. This allows the deep learning model to learn from replicated patterns without requiring physical testing of each variant combination, reducing selection complexity while maintaining adaptability
Solution Approach 2:
The patent transforms the selection problem from choosing among many variants to predicting performance parameters (efficiency and outcome frequencies) for any given variant-target combination. The deep learning model accepts variant identity and target sequence as inputs and outputs predicted efficiency metrics, converting complexity into predictable parameters
2Reliability
If extensive experimental evaluations are performed to compare base editor variants, then the reliability of variant selection is improved, but the time consumption and productivity are worsened
Solution Approach 1:
The patent performs preliminary action by pre-training deep learning models on comprehensive datasets containing performance data from extensive experimental evaluations of multiple base editor variants and Cas9 variants. This preliminary training consolidates knowledge from many experiments into a model that can rapidly predict variant performance without requiring new experiments for each selection case
Solution Approach 2:
The patent introduces a deep learning model as an intermediary between the need for reliable variant comparison and the constraint of time. The model acts as a mediator that translates target sequence characteristics into predicted efficiency outcomes, replacing the need for direct experimental comparison while maintaining reliability through training on extensive experimental data
3Measurement precision
If deep learning models are trained on extensive datasets of base editor variants, then the measurement precision of efficiency prediction is improved, but the device complexity and computational requirements increase
Solution Approach 1:
The patent segments the prediction task into two separate deep learning models: DeepCas9variants for predicting Cas9 variant activity and DeepBE for predicting base editor efficiency and outcome frequencies. This segmentation allows each model to specialize in specific aspects of the prediction, improving measurement precision while managing complexity through modular architecture
Solution Approach 2:
The patent creates universal deep learning models that can predict the performance of multiple base editor variants and Cas9 variants across diverse target sequences using a single trained model. This universality achieves high measurement precision without requiring separate models for each variant, managing complexity through generalizable feature representations
Data Source
AI summary
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.