Nucleic Acid Amplification Prediction Using Secondary Structure Models
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Solution Overview
Problem
Conventional methods fail to accurately predict how secondary structures in nucleic acid sequences affect nucleic acid amplification reactions, leading to inefficiencies in oligonucleotide design and target nucleic acid detection.
Innovation Solution
A method using a computing device to access a prediction model trained with nth-order structure analysis data, thermodynamic data, and amplification reaction results to predict the impact of secondary structures on nucleic acid amplification, allowing for improved oligonucleotide design and target nucleic acid detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to design oligonucleotides without considering secondary structures, then the design process is simple and fast, but the detection accuracy and amplification efficiency are reduced
Solution Approach 1:
The patent applies preliminary action by predicting secondary structures (hairpin loops, G-quadruplexes, etc.) and evaluating their thermodynamic stability before finalizing oligonucleotide design. This advance analysis prevents amplification failures and improves detection accuracy without significantly complicating the overall design workflow
Solution Approach 2:
The patent introduces an intermediary computational evaluation system that assesses the impact of secondary structures on amplification reactions. This intermediary layer bridges the gap between simple sequence design and complex experimental optimization, providing quantitative predictions of amplification efficiency and inhibition risks
2Reliability
If secondary structure analysis is incorporated into oligonucleotide design, then amplification reaction prediction accuracy is improved, but the computational time and complexity increase
Solution Approach 1:
The patent segments the secondary structure analysis into distinct evaluable components (hairpin loops, G-quadruplexes, internal loops, bulge loops) with specific thermodynamic parameters. This segmentation allows for efficient computational handling of each structure type independently, reducing overall computational complexity while maintaining prediction accuracy
Solution Approach 2:
The patent utilizes thermodynamic parameters (free energy changes, stability constants) to quantify secondary structure impacts. By transforming structural complexity into comparable numerical parameters, the system enables rapid evaluation and comparison of different oligonucleotide designs without extensive computational simulations
3Measurement precision
If thermodynamic data for nth-order structures is used to predict amplification reactions, then prediction precision is enhanced, but the data processing complexity increases
Solution Approach 1:
The patent develops a universal computational framework that handles multiple types of secondary structures (hairpin loops, G-quadruplexes, internal loops, bulge loops) using consistent thermodynamic evaluation methods. This universal approach simplifies data processing by applying the same analytical principles across different structure types, reducing the need for separate complex analysis routines for each structure
Data Source
AI summary
Proposed is a method for obtaining a prediction result of a nucleic acid amplification reaction affected by an nth-order structure, which is performed by a computing device. The method may include accessing a prediction model learned using a plurality of training data Each training data may include a first analysis data for an nth-order structure in a nucleic acid sequence and an amplification reaction result for the nucleic acid sequence, where n is an integer not less than 2. The method may also include obtaining an input data comprising a second analysis data for an nth-order structure in a target nucleic acid sequence. The method may further include providing the input data to the prediction model, and obtaining a prediction result of an amplification reaction for the target nucleic acid sequence from the prediction model.