Top-Down Proteomics Sequencing via Log-Space Charge Pattern Alignment
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Solution Overview
Problem
Current top-down proteomic sequencing methods rely heavily on averagine-based deconvolution, leading to systematic mass errors and limitations in identifying protein sequences with post-translational modifications, especially when the monoisotopic peak is absent or misidentified, which complicates accurate molecular mass determination and database matching.
Innovation Solution
A computerized method that transforms mass spectrometer data into natural logarithmic space to align peaks based on charge state, allowing for accurate sequencing without monoisotopic mass assignment, and iterates residue mass differences to identify biological polymers and calibrate spectra internally.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If averagine-based deconvolution is used to estimate monoisotopic mass, then the process is convenient and fast, but systematic mass errors of 1-2 Da or more are introduced
Solution Approach 1:
The patent transforms the mass spectral data from linear m/z space to natural logarithmic space using the transformation ln(m/z - q), where q is the charge carrier mass. This parameter transformation causes peaks from the same analyte mass to align along predictable linear patterns defined by charge state, enabling accurate mass determination without averagine fitting assumptions
Solution Approach 2:
The patent replaces the mechanical/algorithmic averagine-based deconvolution process with a mathematical transformation approach. Instead of fitting spectra to averaged amino acid composition models, the method uses logarithmic transformation to directly reveal charge state patterns and determine masses through geometric relationships in transformed space
2Reliability
If database search tolerances are widened to accommodate mass errors, then more true matches are included, but the risk of false positives increases
Solution Approach 1:
The patent replaces the trial-and-error tolerance adjustment process with a deterministic mathematical transformation. The logarithmic transformation provides exact charge state alignment and mass determination, eliminating the need for tolerance widening and thereby removing the source of false positives while maintaining high sensitivity
3Quantity of substance
If monoisotopic peak is weak or undetectable in high mass proteins, then accurate molecular mass determination becomes difficult, but this is necessary for all top-down proteomic workflows
Solution Approach 1:
The patent moves the analysis from the traditional one-dimensional m/z domain to a transformed logarithmic domain. In this new dimensional space, the information about monoisotopic mass is redistributed across multiple peaks according to charge state, allowing mass determination through pattern recognition rather than relying on a single weak peak
Solution Approach 2:
The logarithmic transformation creates a universal pattern recognition approach that works across all charge states and mass ranges. Instead of relying on the monoisotopic peak's visibility, the method uses the universal charge state spacing pattern in log space that is always present and predictable, making the solution universally applicable regardless of protein mass or signal intensity
4Quantity of substance
If electrospray ionization generates multiple charge states, then signal intensity is split across overlapping m/z values, but this provides redundant information for accurate deconvolution
Solution Approach 1:
The patent applies logarithmic transformation to convert the complex overlapping charge state patterns in linear m/z space into simple, non-overlapping linear patterns in log space. The transformation ln(m/z - q) converts the hyperbolic charge state relationships into straight lines with predictable slopes, dramatically simplifying the deconvolution process
Solution Approach 2:
The patent uses each charge state as a redundant copy of the same mass information, encoded at different positions in the logarithmic space. By transforming to log space, these redundant copies become clearly distinguishable and can be easily correlated to determine the true mass, turning the complexity of multiple charge states into a beneficial redundancy
Data Source
AI summary
Computerized methods and systems of de novo sequencing from a mass spectrometer and identifying a biological polymer using mass invariant charge patterns in the spectrometer data by transforming spectra to a natural logarithmic space where peaks arising from the same analyte mass align along a predictable pattern defined solely by charge state. In some embodiments, the computerized method employs an operation that iterates the residue mass in the transformed natural logarithmic space, e.g., minimizing charge state difference errors between corresponding isotopologues assigned to different charge states. In some embodiments, the de novo sequencing of the present disclosure also allows for viewing the mass-to-charge (m/z) spectrum in a natural logarithmic manner (e.g., Equation 1—ln(m/z−q)) to provide confidence in any reassignment of peaks in an observed charge pattern vector.