Graph Encoding of Protein Structures for High-Throughput Analysis
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Solution Overview
Problem
Current methods for predicting protein function are limited by the need for pairwise structural alignments and complex homology calculations, making them computationally expensive and unsuitable for high-throughput analysis, especially as the number of proteins increases.
Innovation Solution
A system and method for graphically encoding protein secondary and tertiary structural information into a computer-readable format using a standardized squared distance matrix, allowing for scalable representation and analysis by machine learning techniques without the need for pairwise alignments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pairwise structural alignment methods are used for protein function prediction, then structural comparison accuracy is improved, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent segments the protein structure comparison task into independent graph encoding units. Each protein is represented as a graph where nodes are amino acids and edges represent spatial relationships. This segmentation allows parallel processing of multiple proteins without requiring pairwise alignment, thus reducing computational complexity while maintaining structural comparison accuracy through graph-based representation.
Solution Approach 2:
The patent creates simplified graph representations (copies) of complex protein structures. Instead of directly comparing full 3D structures through alignment, the invention generates graph encodings that capture essential structural features. These graph copies can be efficiently processed and compared using machine learning algorithms, reducing computational burden while preserving structural information.
2Reliability
If pairwise structural alignment is performed for all proteins in a database, then comprehensive structural comparison is achieved, but processing time and computational resources become prohibitive for high-throughput analysis
Solution Approach 1:
The patent divides the database-wide structural comparison into independent graph encoding tasks for each protein. Each protein is encoded as a standalone graph representation, enabling parallel processing across the entire database. This eliminates the need for O(n²) pairwise alignments while maintaining comprehensive structural analysis through batch processing of graph encodings.
Solution Approach 2:
The patent performs preliminary graph encoding of each protein structure before comparison. By pre-computing graph representations and storing them in a database, the system enables rapid querying and comparison without performing real-time alignment calculations. This preliminary action significantly accelerates high-throughput analysis while maintaining structural comparison reliability.
3Adaptability or versatility
If traditional sequence alignment methods are used, then homology-based function prediction is achieved, but the methods fail when sequence similarity is low despite structural homology
Solution Approach 1:
The patent transitions from sequence-space analysis to structure-space analysis by encoding proteins as graphs based on 3D spatial coordinates. This dimensional change from 1D sequences to 3D structure graphs enables detection of structural homology even when sequence similarity is low, improving function prediction accuracy for distantly related proteins while maintaining adaptability to diverse protein families.
Data Source
AI summary
The present invention is directed to a system and methods of predicting protein function through a process of encoding protein structural information into a computer readable format and the use of a convolutional neural network designed to recognize such encoded format.