Graph Neural Network Mapping Genomic Data to 3D Protein Structures
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Solution Overview
Problem
Current methods for analyzing genomic sequence data struggle to effectively incorporate three-dimensional protein structures and genetic interactions, leading to challenges in clinical decision-making, particularly in small patient cohorts and in accurately predicting disease subtypes and treatment responses.
Innovation Solution
A computer-implemented method that maps genomic sequence data onto three-dimensional protein structures using graph neural networks, trained on data from disease-affected cohorts, to derive diagnostic, prognostic, and predictive conclusions, incorporating prior knowledge of genomic interactions and protein-protein interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If simplified models with preselected mutations or summary statistics are used, then training becomes feasible with small patient cohorts, but the ability to effectively assess the collective impact of gene mutations and incorporate prior knowledge on genomic interactions is reduced
Solution Approach 1:
The patent transforms the analysis from one-dimensional linear sequence data to three-dimensional protein structure space. By mapping genomic mutations onto 3D protein structures, the system captures spatial relationships between amino acids that are distant in sequence but close in structure, enabling assessment of epistatic patterns and collective mutation impacts that linear models miss.
Solution Approach 2:
The patent incorporates prior knowledge by pre-selecting protein structures and their three-dimensional coordinates before training. This preliminary structuring of genomic data based on known protein architectures enables the model to leverage existing biological knowledge about protein folding and interaction, improving training efficiency with small cohorts while preserving interaction information.
2Device complexity
If positions distant in gene sequence are considered independently, then computational complexity is reduced, but the ability to capture epistatic patterns and mutual exclusivity/co-occurrence of mutations is lost
Solution Approach 1:
The patent merges the analysis of multiple genomic positions by mapping them onto a unified three-dimensional protein structure framework. Mutations at different sequence positions are combined in 3D space, allowing the model to capture spatial proximity and epistatic interactions. The graph neural network aggregates information from neighboring nodes representing spatially close amino acids, enabling simultaneous consideration of multiple mutation positions and their interactions.
Solution Approach 2:
The patent introduces three-dimensional protein structure coordinates as an intermediary representation between raw genomic sequence data and the predictive model. This intermediary 3D structure framework mediates the relationship between distant genomic positions, translating linear sequence distances into spatial distances that reflect functional interactions. The graph neural network uses this intermediary representation to efficiently capture epistatic patterns without direct pairwise comparison of all mutation combinations.
3Loss of information
If the full genomic territory is analyzed, then comprehensive genomic information is utilized, but training with small patient cohorts becomes infeasible due to high dimensionality
Solution Approach 1:
The patent applies local quality by focusing analysis on specific regions of the protein structure rather than treating all genomic positions uniformly. The graph neural network assigns different weights and attention to different spatial regions based on their structural importance. Amino acids in functionally critical regions (e.g., active sites, protein-protein interfaces) receive higher attention, allowing the model to prioritize informative local regions while maintaining comprehensive genomic input.
Solution Approach 2:
The patent segments the genomic data by mapping mutations to specific protein structures and their three-dimensional coordinates. This segmentation organizes the high-dimensional genomic space into structured 3D regions corresponding to functional domains. The graph neural network processes this segmented data by operating on local neighborhoods in 3D space, effectively dividing the comprehensive genomic analysis into manageable local computations that are feasible with small training cohorts.
Data Source
AI summary
A computer-implemented method for analyzing genomic sequence data comprises: obtaining genomic sequence data; obtaining data from three-dimensional protein structures; mapping the genomic sequence data on the protein structures; inputting the mapped genomic sequence data into a trained graph neural network; and deriving a diagnostic, prognostic and/or predictive conclusion output with respect to said disease or medical condition. The architecture of the graph neural network is based on the three-dimensional protein structure. The graph neural network is trained based on genomic sequence data from a cohort of subjects affected by a disease or medical condition mapped to the three-dimensional protein structures and corresponding diagnostic, prognostic and/or predictive conclusions in the context of the disease or medical condition.