Protein Structure Prediction Using Sequence Feature Augmentation
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Solution Overview
Problem
Existing protein structure prediction methods face inefficiencies due to the need for large databases, leading to prolonged query times and reduced accuracy, and do not adequately account for positional information and biological evolution in amino acid sequences.
Innovation Solution
A method using a sequence feature augmentation model trained on both a first and second database, where the second database has a larger data scale, to enhance the initial sequence features, allowing for accurate prediction of protein structure information with reduced database query times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a database with a larger data scale is used to extract sequence features, then the accuracy of sequence feature extraction is improved, but the query operation takes a long time, leading to lower prediction efficiency
Solution Approach 1:
The patent pre-trains a sequence feature augmentation model using multi-sequence alignment data from a large database to learn evolutionary patterns and positional information. This preliminary action allows the model to generate augmented sequence features quickly during prediction without requiring real-time queries to the large database, thus resolving the contradiction between extraction accuracy and query time.
Solution Approach 2:
The patent uses the sequence feature augmentation model to copy and generate augmented sequence features based on the initial sequence features extracted from a smaller database. This copying mechanism allows the system to leverage patterns learned from large-scale data without requiring actual access to the large database during prediction, achieving both accuracy and efficiency.
2Measurement precision
If a database with a larger data scale is used to extract sequence features, then the accuracy of protein structure information prediction is improved, but the prediction efficiency is reduced
Solution Approach 1:
The sequence feature augmentation model is pre-trained in advance using data from a large database to capture evolutionary patterns and positional information. This preliminary training enables the model to generate accurate augmented sequence features during prediction without requiring real-time access to the large database, thus improving prediction accuracy while maintaining high prediction efficiency.
Solution Approach 2:
The sequence feature augmentation model acts as an intermediary between the initial sequence features and the final prediction. It processes the initial features to generate augmented features that incorporate evolutionary patterns learned from large-scale data, enabling accurate predictions without direct access to the large database during the prediction phase.
3Device complexity
If traditional methods are used that do not account for positional information and biological evolution, then the method complexity is reduced, but the prediction accuracy is insufficient
Solution Approach 1:
The patent transforms the initial sequence features into augmented sequence features by incorporating evolutionary patterns and positional information as additional parameters. The sequence feature augmentation model learns to generate these enhanced features during pre-training, improving prediction accuracy while maintaining relatively simple prediction-time operations.
Solution Approach 2:
The model copies and enhances sequence features by generating augmented versions that include positional and evolutionary information. This copying process enriches the feature representation without requiring complex real-time calculations during prediction, balancing accuracy and complexity.
Data Source
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AI summary
A method, device and apparatus for predicting protein structure information, and a storage medium, which relate to the technical field of biological information. The method comprises: performing a sequence alignment query according to an amino acid sequence of a protein in a first database to obtain multi-sequence alignment data, performing feature extraction on the multi-sequence alignment data to obtain an initial sequence feature, processing the initial sequence feature through a sequence feature amplification model to obtain an amplified sequence feature of the protein, and then predicting the structure information of the protein according to the amplified sequence feature. The described solution can improve the prediction efficiency of the protein structure information under the condition that the prediction accuracy of the protein structure information is guaranteed on the basis of the AI-predicted protein structure information.