Monoisotopic Mass Determination via Double-Linear Model
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Solution Overview
Problem
Current mass spectrometry techniques face challenges in accurately determining the monoisotopic mass of large biomacromolecules like proteins, as the probability of encountering the monoisotopic variant is low, leading to inaccurate measurements due to sensitivity to fluctuations in isotope abundances and limited instrument resolution, especially for high-end instruments.
Innovation Solution
A double-linear model is developed to predict the monoisotopic mass based on the experimentally determined most abundant mass, using the equation MMono=α+βMMostAb+ε, where β is a scalar slope and ε is a scalar residue, allowing for accurate determination within the low parts-per-million range, combining the robustness of the most abundant mass with the optimal data processing characteristics of the monoisotopic mass.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the monoisotopic variant is used for mass determination, then the measurement is unambiguous and invariant, but the probability of encountering the monoisotopic variant becomes vanishingly small for macromolecules
Solution Approach 1:
The patent introduces an intermediary computational approach by using the most abundant isotope peak as a mediator to infer the monoisotopic mass. Instead of directly detecting the rare monoisotopic variant, the method uses the easily detectable most abundant peak as an intermediate reference point, applying mathematical models (Gaussian distribution fitting, polynomial regression) to bridge between the observable most abundant mass and the desired monoisotopic mass.
Solution Approach 2:
The patent replaces the direct physical detection mechanism (relying on random occurrence of monoisotopic variants) with a computational/mathematical system. By substituting the mechanical/stochastic process of isotopic occurrence with mathematical modeling and data processing algorithms, the method achieves reliable monoisotopic mass determination without depending on the rare physical event of monoisotopic variant detection.
2Ease of operation
If the average mass is used for mass determination, then the measurement is convenient and works with lower-resolution instruments, but the measurement is sensitive to fluctuations in relative isotope abundances
Solution Approach 1:
The patent creates a computational copy or model of the isotopic distribution pattern. By fitting Gaussian curves to the observed isotope peaks and using polynomial regression to model the relationship between most abundant mass and monoisotopic mass, the method generates a mathematical representation that captures the essential information while eliminating sensitivity to isotopic fluctuations.
Solution Approach 2:
The patent transforms the mass determination parameter from directly measuring the monoisotopic peak (which is rarely observed) to measuring the most abundant peak (which is easily observed), and then uses mathematical transformation to convert between these parameters. This parameter substitution approach maintains ease of measurement while achieving the desired precision through computational correction.
3Reliability
If the most abundant mass is used for mass determination, then the measurement is robust toward fluctuations in isotopic abundances, but specialized software is required to compute the most abundant mass and it does not directly give the monoisotopic mass
Solution Approach 1:
The patent segments the complex problem of monoisotopic mass determination into distinct computational steps: (1) identifying the most abundant peak, (2) fitting Gaussian curves to isotope peaks, (3) calculating the most abundant mass, and (4) applying polynomial regression to convert to monoisotopic mass. This segmentation makes the computational process more manageable and systematic.
Solution Approach 2:
The patent performs preliminary computational actions by first determining the most abundant mass and establishing the mathematical relationship between most abundant mass and monoisotopic mass before final identification. The polynomial regression model is pre-established from training data, allowing rapid conversion once the most abundant peak is identified, thus simplifying the overall workflow.
Data Source
AI summary
The present invention provides a method for the determination of the monoisotopic mass of a macromolecule from a mass Mmono spectrometry spectrum of said macromolecule based on the experimentally determined most abundant mass, with accuracy in the low parts-per-million (ppm) range. The method uses a simple, double-linear model for predicting the monoisotopic mass based on the experimentally determined most abundant mass, comprising the steps of (a) deriving the most abundant mass MMostAb from the spectrum; and (b) calculating the monoisotopic mass MMono from the most abundant mass MMostAb, using MMono=a+βMMOSTAB+ε; wherein β is a scalar slope obtainable by fitting the slope of monoisotopic mass versus most abundant mass for a plurality of macromolecules from a macromolecule database; and ε is a scalar residue of the form ε=εint+sfrac, εint being an integer, and εfrac being a sawtooth function of MMostAb.