Protein Domain Identification via 3D Structure Point Cloud Segmentation
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Solution Overview
Problem
Current methods for predicting protein domains based on amino acid sequences are limited by low accuracy due to the high time complexity of multi-sequence alignment and the assumption of collinearity in homologous sequences, which is often violated, especially in proteins with low sequence identity.
Innovation Solution
A method and system that utilize a point cloud segmentation model based on dynamic graph convolutional neural networks to identify protein domains from three-dimensional structure images, integrating global and local structural features to improve accuracy and overcome sequence alignment errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If amino acid sequence alignment methods are used to identify protein domains, then the identification can be performed based on sequence information, but the accuracy drops rapidly when amino acid sequence consistency is lower than a certain critical point
Solution Approach 1:
The patent transitions from one-dimensional sequence alignment to three-dimensional structural analysis. Instead of comparing linear amino acid sequences, the method uses 3D coordinate information of atoms in protein domains to identify structural similarities. This dimensional transformation allows accurate identification of remote homologous proteins whose sequences have diverged but whose 3D structures remain conserved.
Solution Approach 2:
The patent replaces the mechanical sequence alignment process with a computational 3D structural comparison system. By using coordinate-based distance calculations and dynamic time warping algorithms on 3D atomic positions, the system achieves accurate domain identification without relying on sequence collinearity assumptions.
2Measurement precision
If multi-sequence alignment calculation is performed to ensure accurate protein domain identification, then the identification quality improves, but the time complexity becomes very high
Solution Approach 1:
The patent extracts only the essential 3D coordinate information of atoms from the complete protein sequence data. By focusing on the spatial positions of atoms in potential domain regions rather than performing comprehensive multi-sequence alignment, the method achieves accurate identification with significantly reduced computational time and complexity.
Solution Approach 2:
The patent creates simplified 3D coordinate representations (copies) of protein domain structures that capture the essential structural features without containing the full complexity of the original sequences. These coordinate-based models enable rapid comparison and identification while preserving the structural information necessary for accurate domain detection.
3Ease of operation
If the assumption of collinearity in homologous protein sequences is made to simplify alignment, then the calculation process becomes easier, but this assumption is often violated in the real world leading to identification errors
Solution Approach 1:
Instead of assuming sequence collinearity and trying to align sequences linearly, the patent inverts the approach by directly comparing 3D structural coordinates without assuming any linear correspondence. This inversion eliminates the collinearity assumption problem by working in the structural domain where evolutionary conservation is more apparent.
Solution Approach 2:
The patent changes the fundamental parameters used for comparison from amino acid sequence identities to three-dimensional atomic coordinate distances. By transforming the data representation from sequential to spatial parameters, the method achieves reliable identification of homologous domains even when sequence collinearity is violated.
Data Source
AI summary
Disclosed is a method and system for identifying a protein domain based on a protein three-dimensional structure image. According to the present application, the protein domain is identified based on a structure similarity, the identification errors and omissions of the protein domain caused by protein multi-sequence alignment errors when sequence consistency is not high can be effectively solved. According to the present application, the point cloud segmentation model based on the dynamic graph convolutional neural network is constructed, and by integrating global structural features and local structural features, segmentation of the protein domain and acquisition of semantic labels of the protein domain can be completed at the same time.