Antigen Prediction Model Integrating Receptor Features
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Solution Overview
Problem
Current methods lack an efficient way to predict antigens that can specifically bind to immune cell receptors, which is crucial for understanding the immune system and advancing immune therapies and vaccine design.
Innovation Solution
An antigen prediction method and apparatus that input genetic, sequence, and three-dimensional structure features of immune cell receptors into a model to extract and integrate features, perform full connection and normalization, and determine the probability of antigen binding, thereby identifying target antigens.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional experimental methods are used to identify antigens that bind to immune cell receptors, then the reliability of antigen identification is improved, but the time consumption and experimental cost increase significantly
Solution Approach 1:
The patent performs preliminary feature extraction from genetic information, sequence information, and three-dimensional structure features before actual antigen prediction. By pre-processing and encoding these features into comprehensive receptor representations, the system reduces the computational burden during the actual prediction phase, enabling faster antigen identification while maintaining accuracy through pre-computed structural and sequence characteristics.
Solution Approach 2:
The patent creates a computational model that replicates the biological binding interaction between immune cell receptors and antigens. Instead of performing physical experiments to test each antigen-receptor pair, the system uses a trained prediction model that copies the essential binding characteristics, allowing virtual screening of multiple candidate antigens against a single receptor without time-consuming wet lab experiments.
2Measurement precision
If comprehensive feature extraction from genetic, sequence, and structural information is performed, then the prediction accuracy of antigen binding is improved, but the computational complexity increases
Solution Approach 1:
The patent segments the antigen prediction task into three distinct feature extraction modules: genetic information processing, sequence information analysis, and three-dimensional structure feature extraction. Each module independently processes its specific input type and produces specialized features, which are then integrated. This segmentation allows each component to be optimized separately and simplifies the overall model architecture by dividing the complex prediction task into manageable, specialized sub-tasks.
Solution Approach 2:
The patent merges the outputs from the three separate feature extraction modules (genetic features, sequence features, and structural features) into a comprehensive receptor feature representation. By integrating these diverse feature types through concatenation or fusion operations, the model creates a unified and rich input representation that captures multiple aspects of receptor characteristics, thereby improving prediction accuracy without requiring a fundamentally more complex model architecture.
3Adaptability or versatility
If the antigen prediction model processes multiple candidate antigens for each receptor, then the completeness of antigen identification is improved, but the computational resources required increase
Solution Approach 1:
The patent replaces the mechanical process of physical antigen-screening experiments with a computational prediction system. Instead of using laboratory equipment and reagents to test each antigen-receptor interaction, the system uses a trained machine learning model that performs predictions through mathematical computations. This substitution dramatically reduces energy consumption while enabling the screening of multiple candidate antigens, as computational predictions require minimal energy compared to repeated wet lab experiments.
Data Source
AI summary
An antigen prediction method including inputting genetic information, sequence information, and three-dimensional structure features of an immune cell receptor into an antigen prediction model, performing, by the antigen prediction model, feature extraction on the genetic information and the sequence information to obtain genetic features and sequence features of the immune cell receptor, integrating, by the antigen prediction model, the genetic features, the sequence features, and the three-dimensional structure features to obtain receptor features of the immune cell receptor, performing, by the antigen prediction model, full connection and normalization on the receptor features to output a probability of the immune cell receptor being associated with each candidate antigen of a plurality of candidate antigens, and determining, based on the probability of the immune cell receptor being associated with each candidate of the plurality of candidate antigens, an antigen binding to the immune cell receptor from the plurality of candidate antigens.