Multi-Modal Protein Prediction Model Integrating Embeddings and Contact Maps
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Solution Overview
Problem
Current methods for predicting protein functions and interactions are limited as they often focus on a single modality, such as amino acid sequences or physiochemical features, failing to capture the comprehensive nature of protein characteristics.
Innovation Solution
A multi-modal prediction model that combines embedded amino acid sequences, contact map predictions, and physiochemical features to generate a prediction score for protein functions or interactions, utilizing a feed-forward neural network and attention-based mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single modality (amino acid sequence or physiochemical features) is used for prediction, then the model complexity is low, but the prediction accuracy and comprehensiveness deteriorate
Solution Approach 1:
The patent merges multiple modalities (amino acid sequences, contact map predictions, and physiochemical features) into a unified multi-modal prediction model. This integration allows the system to leverage complementary information from different data types, thereby improving prediction accuracy while managing complexity through structured feature fusion mechanisms.
Solution Approach 2:
The prediction model is designed to handle multiple modalities simultaneously, making it a multi-functional system that can process diverse protein data types. This universal approach enables the model to adapt to different prediction tasks (protein functions, interactions, or both) without requiring separate specialized models for each modality.
2Reliability
If multiple modalities are integrated, then the comprehensive nature of protein characteristics is captured, but the computational complexity and data processing requirements increase
Solution Approach 1:
The system segments the multi-modal input data into distinct processing streams for amino acid sequences, contact map predictions, and physiochemical features. Each modality is processed independently through dedicated encoding mechanisms before being integrated, which maintains reliability by preserving modality-specific characteristics while managing system complexity through modular architecture.
Solution Approach 2:
The patent introduces embedded representations as intermediary structures that bridge different modalities. These embeddings serve as a common language that allows the system to integrate diverse protein characteristics without direct complex interactions between raw data types, thereby improving reliability while controlling computational complexity.
Data Source
AI summary
A method of predicting protein functions and interactions, including identifying a first protein and a second protein for analysis, generating a first embedded representation of a first amino acid sequence of the first protein and a second embedded representation of a second amino acid sequence of the second protein, generating a first contact map prediction for the first protein based on the first amino acid sequence and a second contact map prediction for the second protein based on the second amino acid sequence, generating first physiochemical features associated with the first protein, and second physiochemical features associated with the second protein, and generating a prediction score for a protein function or interaction between the first protein and the second protein based on the first embedded representation, the second embedded representation, the first contact map prediction, the second contact map prediction, the first physiochemical features and the second physiochemical features.