Isotopic Distribution Analysis via Linear Equations
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Solution Overview
Problem
Current methods for analyzing the isotopic distribution of large molecules, such as polypeptides, in mass spectrometry are computationally intensive, time-consuming, and prone to numerical inaccuracies, making it difficult to efficiently identify and quantify the elemental composition and phosphorylation events.
Innovation Solution
A method is developed to efficiently compute the aggregated isotopic distribution of molecules using a recursive approach and linear equations, allowing direct deduction of the molecular formula from the isotopic distribution without trial-and-error methods, and specifically identifies mono-isotopic elements like phosphorus.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional trial-and-error methods are used to determine elemental composition from isotopic distribution, then identification accuracy can be achieved, but computational intensity and time consumption increase significantly
Solution Approach 1:
The patent inverts the traditional approach by instead of trying different compositions and comparing isotopic distributions, it directly calculates the elemental composition from the observed isotopic distribution using linear equations. This inversion transforms an iterative search problem into a direct calculation problem, resolving the contradiction between accuracy and computational efficiency.
Solution Approach 2:
The patent replaces the mechanical trial-and-error process with a mathematical computation system based on linear equations and matrix operations. By substituting the iterative mechanical approach with a direct mathematical solution, it achieves both high accuracy in elemental composition identification and significant improvement in computational efficiency.
2Measurement precision
If high-resolution mass spectrometry is used to obtain detailed isotopic distribution, then measurement precision improves, but data processing complexity and time increase
Solution Approach 1:
The patent extracts the essential information needed for elemental composition determination from the complex high-resolution isotopic distribution data. By focusing only on the peak intensities and applying linear equation methods, it separates the critical analytical information from the overwhelming data complexity, reducing processing burden while maintaining accuracy.
Solution Approach 2:
The patent transforms the complex isotopic distribution data into a simplified mathematical form suitable for linear equation solving. By changing the parameter representation from detailed spectral data to peak intensity values and applying mathematical transformations, it reduces data processing complexity while preserving the information needed for accurate elemental composition determination.
3Measurement precision
If complete elemental composition analysis is performed on large molecules like polypeptides, then identification accuracy improves, but computational resources and time requirements increase
Solution Approach 1:
The patent performs preliminary calculations by pre-computing the relationship between isotopic peak intensities and elemental composition coefficients. This preliminary action creates a ready-to-use mathematical framework that can be quickly applied to determine molecular formulas of large molecules like polypeptides, significantly reducing the time required for complete elemental composition analysis while maintaining high accuracy.
Data Source
AI summary
The current invention concerns a method for identifying the elemental composition of and/or quantifying the presence of mono-isotopic elements in a molecule in a sample by computing a solution of a system of linear equations ΣαEiαnα = Fi, whereby the set of numbers Fi comprises said set of relative peak heights from the aggregated isotopic distribution and the coefficients Eiα of said linear system comprise powers and/or power sums of roots rα,iα. The present invention also provides a method for analysing at least part of an isotopic distribution of a sample, comprising obtaining data comprising at least one probability qj with which a j'th aggregated isotopic variant of said molecule with mass number Aj occurs within said aggregated isotopic distribution; and computing a probability qi with which an i'th aggregated isotopic variant occurs within said aggregated isotopic distribution, by taking a linear combination of said at least one probability qj.