Graph Network for Glycopeptide Identification in LCMS
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Solution Overview
Problem
Current database-dependent methods for glycopeptide identification in LCMS data fail to detect unexpected glycopeptides, do not visualize unidentified peaks, and may incorrectly assign sequences, leading to incomplete annotation and high false detection rates.
Innovation Solution
A graph theoretic analysis method that converts LCMS data into a network of nodes based on mass and retention time differences, allowing for the identification and prediction of glycopeptide compositions without relying solely on database matches, and providing a visual representation of the data for exploration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If database-dependent algorithms are used for glycopeptide identification, then identification speed and computational efficiency are improved, but the ability to detect unexpected glycopeptides and completeness of annotation deteriorate
Solution Approach 1:
The patent segments the identification process into two independent parts: (1) using database-dependent algorithms for rapid identification of known glycopeptides, and (2) using graph theoretic analysis to detect unexpected glycopeptides by analyzing mass and retention time differences between nodes in a graph network. This segmentation allows each method to operate optimally without compromising the other, resolving the contradiction between speed and adaptability.
Solution Approach 2:
The patent merges database-dependent algorithms with graph theoretic analysis into a unified identification system. The graph network integrates multiple data dimensions (mass, retention time, intensity) to complement database searches, enabling the system to maintain high productivity while simultaneously discovering unexpected glycopeptides that database-dependent methods alone would miss.
2Device complexity
If database-dependent software is used, then computational resources are reduced, but the ability to visualize unidentified peaks and explore data comprehensively deteriorates
Solution Approach 1:
The patent introduces a graph network as an intermediary data structure that mediates between raw LCMS data and identification results. This graph representation serves as an informative visualization that displays unidentified peaks and data exploration capabilities without requiring extensive computational resources, as it processes and displays only the essential relationships between nodes rather than processing every possible database combination.
3Reliability
If theoretical databases are used for glycopeptide identification, then identification confidence for known peptides is improved, but false detection rates and incomplete annotation deteriorate
Solution Approach 1:
The patent implements feedback mechanisms where the graph theoretic analysis continuously evaluates and refines identification results. By analyzing mass and retention time differences in the graph network, the system provides feedback to identify and correct false detections, adjusting the identification confidence scores and reducing false detection rates while maintaining high reliability for known glycopeptides.
Data Source
AI summary
A method identifies glycopeptides in a sample. The method includes converting a mass spectrum of MS1 precursors of the sample into a plurality of nodes in a graph, each node corresponding to one mass and one retention time of a glycopeptide to be identified in the sample; calculating differences in the mass and/or retention time between all combinations of pairs of the nodes; generating a graph theoretic network of the nodes; and predicting compositions of the glycopeptides in the sample based on the graph theoretic network of the nodes so as to identify the glycopeptides.