AI Protein Interaction Prediction via Structural Feature Extraction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing models for predicting protein interaction and immunogenicity face challenges due to noise and high costs, making it difficult to accurately predict the physical structure of protein complexes that can bind with T cell receptors.
Innovation Solution
A device and method using artificial intelligence to predict protein interaction by extracting the structure of a protein complex from a given protein sequence, utilizing positional encoding, and performing learning to predict interactions between protein complexes and external proteins.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing learning models are used to predict protein complex structures, then prediction capability is provided, but noise and high cost occur during the learning process making it difficult to directly utilize these models for immunogenicity prediction
Solution Approach 1:
The patent introduces an intermediary processing step that extracts structural features from protein complex predictions and transforms them into a format suitable for immunogenicity prediction. This intermediary layer filters out noise from existing models while preserving useful structural information, enabling reliable immunogenicity prediction without directly using the noisy raw outputs of complex learning models.
Solution Approach 2:
The patent extracts only the necessary structural features (such as spatial coordinates, residue positions, and binding interface characteristics) from the full protein complex structure predictions. By taking out only the relevant structural information needed for immunogenicity assessment, the system avoids processing unnecessary data that contributes to noise and computational cost.
2Measurement precision
If protein complex structure prediction is performed to improve immunogenicity prediction, then structure information is obtained, but the process requires repeated learning and computation increasing time and resource consumption
Solution Approach 1:
The patent performs preliminary structure prediction and extracts structural features in advance, storing them for subsequent immunogenicity predictions. By preparing structural information beforehand and caching relevant features, the system avoids repeating the entire structure prediction process for each immunogenicity assessment, significantly reducing computation time while maintaining accuracy.
Solution Approach 2:
The patent divides the prediction process into separate modular stages: structure prediction, feature extraction, and immunogenicity prediction. Each stage can be independently optimized and executed, allowing parallel processing and efficient resource utilization. This segmentation enables the system to reuse structure predictions across multiple immunogenicity predictions without redundant computation.
Data Source
Figure 1
Figure 2
Figure 3~4
AI summary
A device for predicting protein interaction to predict whether or not protein interact based on artificial intelligence comprises memory; a communication unit; and at least one processor operably connected to the memory and the communication unit. The at least one processor is configured to identify data related to at least one protein sequence from the memory, predict a structure of a first protein complex based on the data related to the at least one protein sequence, determine coordinate information of the first protein complex based on predetermined positional encoding operation, and perform learning to predict interaction between the first protein complex and an external protein based on the coordinate information of the first protein complex.