Protein Graph Neural Network for Structure Prediction
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Solution Overview
Problem
Current methods for predicting protein structures are inefficient and resource-intensive, often requiring extensive computational resources and time due to iterative search processes, which hinders drug development and biochemical research.
Innovation Solution
A protein structure prediction system utilizing protein graphs and multiple sequence alignment (MSA) graphs, processed by graph neural networks to enrich embeddings and predict protein structures with high accuracy through a single forward pass, reducing computational requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If iterative search processes are used for protein structure prediction, then prediction accuracy can be achieved, but computational resources and time consumption increase significantly
Solution Approach 1:
The patent replaces traditional iterative search processes (mechanical/computational search) with a graph neural network model that directly predicts protein structures from sequences. The GNN processes protein sequences and their evolutionary relationships (MSA) to directly output structural information, eliminating the need for time-consuming iterative searching while maintaining prediction accuracy.
Solution Approach 2:
The patent changes the fundamental parameters of the prediction approach by using graph neural networks with multiple sequence alignment (MSA) data instead of traditional iterative search methods. This parameter change enables the system to achieve both high accuracy and computational efficiency by transforming the prediction problem into a direct neural network inference task.
2Measurement precision
If iterative search processes are used for protein structure prediction, then prediction accuracy can be achieved, but computational resources increase significantly
Solution Approach 1:
The patent replaces computationally intensive iterative search processes with a graph neural network model that directly computes protein structures. The GNN architecture processes input sequences and MSA data through neural network layers to directly generate structural predictions, significantly reducing the computational resources required compared to traditional iterative methods.
Solution Approach 2:
The patent changes the computational approach by using deep learning models with graph neural networks that process sequence and evolutionary information in a single forward pass. This parameter change in the prediction methodology enables high accuracy while minimizing computational resource consumption compared to iterative search processes.
3Loss of information
If traditional protein structure prediction methods are used, then structural information can be obtained, but the process is resource-intensive and time-consuming
Solution Approach 1:
The patent replaces traditional resource-intensive prediction methods with a graph neural network system that efficiently extracts structural information from protein sequences. The GNN model processes sequence data and multiple sequence alignment information to directly generate structural predictions, achieving both information completeness and high prediction speed.
Solution Approach 2:
The patent performs preliminary processing by pre-computing and storing multiple sequence alignment (MSA) data and evolutionary information before the actual prediction. This preliminary action enables the neural network to make rapid predictions by querying pre-processed data, significantly improving prediction speed while maintaining structural information accuracy.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining a predicted structure of a protein. According to one aspect, there is provided a method comprising maintaining graph data representing a graph of the protein; obtaining a respective pair embedding for each edge in the graph; processing the pair embeddings using a sequence of update blocks, wherein each update block performs operations comprising, for each edge in the graph: generating a respective representation of each of a plurality of cycles in the graph that include the edge by, for each cycle, processing embeddings for edges in the cycle in accordance with the values of the update block parameters of the update block to generate the representation of the cycle; and updating the pair embedding for the edge using the representations of the cycles in the graph that include the edge.