Triplex Oligonucleotide Affinity Prediction With Pre-Trained Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional methods for measuring the binding affinity of triplex forming oligonucleotides (TFOs) to triplex target sites (TTSs) in double-stranded DNA are time-consuming and resource-intensive, requiring precise experimental efforts.
Innovation Solution
A method using a pre-trained machine learning model that processes sequence features of TFOs and TTSs to estimate binding affinity, incorporating nucleotide frequencies, structural features, and biophysical measurements to predict binding strength without physical measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional experimental methods are used to measure binding affinity, then measurement precision is improved, but time consumption and resource consumption increase
Solution Approach 1:
The patent creates a computational model that copies and processes sequence information of TFOs and TTSs to predict binding affinity, replacing physical experimental measurements with in silico simulations. This allows rapid prediction without time-consuming laboratory work while maintaining predictive accuracy through trained machine learning algorithms.
Solution Approach 2:
The patent substitutes the mechanical experimental measurement system with a computational information processing system. Instead of physically measuring binding affinity through experiments, the system processes sequence features and structural parameters through machine learning models to generate predictions, eliminating the need for physical measurement apparatus and time-consuming experimental procedures.
2Measurement precision
If traditional experimental methods are used to measure binding affinity, then measurement precision is improved, but resource consumption increases
Solution Approach 1:
The patent uses computational copying of sequence information and structural features to predict binding affinity, eliminating the need for physical consumption of reagents, materials, and experimental resources. The system processes digital representations of TFO and TTS sequences to generate predictions without requiring physical substances.
Solution Approach 2:
The patent replaces resource-intensive experimental measurement systems with a computational system that processes sequence data and structural parameters. This substitution eliminates consumption of physical resources such as reagents, equipment, and laboratory materials while maintaining the ability to assess binding affinity with high precision.
3Loss of time
If computational model is used to estimate binding affinity, then time consumption is reduced, but measurement precision may deteriorate
Solution Approach 1:
The patent performs preliminary training of the machine learning model using experimentally determined binding affinities for many TFO-TTS pairs. This preliminary action creates a trained computational model that can rapidly predict binding affinity for new sequences without requiring time-consuming experimental measurements, while maintaining precision through the learned patterns from training data.
Solution Approach 2:
The patent copies and processes sequence features, structural parameters, and biophysical measurements through the trained machine learning model to generate binding affinity predictions. This computational copying and processing enables rapid estimation while maintaining precision by incorporating multiple relevant features and their interactions into the prediction model.
Data Source
Figure 1A~1E
Figure 2A~2D
Figure 3A~3C
AI summary
The present invention provides a method for estimating a binding affinity of a triplex forming oligonucleotide, TFO, at a triplex target site, TTS, of a double-stranded DNA, dsDNA, the method comprising: - obtaining a plurality of TFO features based on sequence information of the TFO and a plurality of TTS features based on sequence information of the TTS, and - processing the plurality of TFO features and the plurality of TTS features in a pre-trained model to estimate the binding affinity.