IP Library › Granted Patent US 12,633,507
Granted Patent B2
US 12,633,507 · App. 18/337,183 · Granted May 19, 2026

Bayesian decremental scheme for charge state deconvolution

Inventor: Paul R. Gazis (Mountain View, CA)
Assignee: Thermo Finnigan LLC
H01J49/0036G01N30/8631G06F18/24155
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Quick Facts
Patent No.
US 12,633,507
App. No.
18/337,183
Granted
May 19, 2026
Kind
B2
Abstract

Disclosed herein are charge state deconvolution systems, as well as related methods, computing devices, and computer-readable media. For example, in some embodiments, a charge state deconvolution apparatus includes first logic to identify peaks in a mass spectrum; second logic to deconvolve the masses of the identified peaks and identify clusters of deconvolved masses that have contiguous charge states; and third logic to calculate a Bayesian fitness measure and perform an iterative decremental procedure to perform charge state deconvolution.

Claims (23)

1 . A charge state deconvolution apparatus, comprising:

first logic to identify peaks in a mass spectrum obtained using a mass spectrometry platform;

second logic to deconvolve masses of the identified peaks and identify clusters of deconvolved masses that have contiguous charge states; and

third logic to calculate a Bayesian fitness measure for each identified cluster and perform an iterative decremental procedure to perform charge state deconvolution.

2 . The charge state deconvolution system of claim 1 , wherein the first logic, the second logic, and the third logic are implemented by a common computing device.

3 . The charge state deconvolution system of claim 1 , wherein at least one of the first logic, the second logic, and the third logic are implemented by a computing device remote from the mass spectrometry platform.

4 . The charge state deconvolution system of claim 1 , wherein at least one of the first logic, the second logic, and the third logic are implemented by a user computing device.

5 . The charge state deconvolution system of claim 1 , wherein at least one of the first logic, the second logic, and the third logic are implemented in the mass spectrometry platform.

6 . The charge state deconvolution system of claim 1 , wherein the second logic applies a sliding window to the deconvolved masses of the identified peaks to identify the clusters of deconvolved masses with contiguous charge states.

7 . The charge state deconvolution system of claim 1 , wherein the iterative decremental procedure of the third logic iteratively subtracts a fraction of intensities of a cluster with a highest Bayesian fitness measure from the deconvolved masses.

8 . The charge state deconvolution system of claim 1 , wherein the third logic repeats using a cluster with a next highest Bayesian fitness until an average intensity in a spectra is below a threshold.

9 . The charge state deconvolution system of claim 1 , wherein the third logic repeats using a cluster with a next highest Bayesian fitness until a largest peak in a spectra is below a threshold.

10 . A method for charge state deconvolution, comprising:

identifying peaks in a mass spectrum;

deconvolving a masses of the identified peaks,

identifying clusters of deconvolved masses that have contiguous charge states;

calculating a Bayesian fitness measure for each cluster of the identified clusters; and

performing an iterative decremental procedure to perform charge state deconvolution.

11 . The method of claim 10 , identifying clusters of deconvolved masses that have contiguous charge states includes applying a sliding window to the deconvolved masses of the identified peaks to identify the clusters of deconvolved masses with contiguous charge states.

12 . The method of claim 10 , performing the iterative decremental procedure includes iteratively subtracting a fraction of intensities of the cluster with the highest Bayesian fitness measure from the deconvolved masses of the identified peaks.

13 . The method of claim 10 , wherein the steps of calculating a Bayesian fitness measure and performing an iterative decremental procedure are repeated using a cluster with a next highest Bayesian fitness until a largest peak in a subtracted spectra is below a threshold.

14 . The method of claim 10 , wherein the steps of calculating a Bayesian fitness measure and performing an iterative decremental procedure are repeated using a cluster with a next highest Bayesian fitness until an average intensity in a subtracted spectra is below a threshold.

15 . One or more non-transitory computer readable media having instructions thereon that, when executed by one or more processing devices of a charge state deconvolution apparatus, cause the charge state deconvolution apparatus to perform the method of claim 10 .

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 19, 2023
From: GAZIS, PAUL R.
To: THERMO FINNIGAN LLC
Reel/Frame 063985/0335 →
Continuity (2)
Provisional Application 63393396 · Jul 29, 2022
Related Publication 20240038514A1 · Feb 1, 2024
References Cited (22)
US 8604421B2 · Skilling et al. · 2013 [cited by applicant]
US 9837255B2 · Stephenson, Jr. et al. · 2017 [cited by applicant]
US 11515136B2 · Richardson et al. · 2022 [cited by applicant]
US 11579144B2 · Mallick · 2023 [cited by applicant]
US 11640901B2 · Bern · 2023 [cited by examiner]
US 20130200258A1 · Skilling et al. · 2013 [cited by applicant]
US 20140303932A1 · Snow · 2014 [cited by examiner]
US 20170205425A1 · Yip · 2017 [cited by examiner]
US 20180301326A1 · Bern · 2018 [cited by examiner]
US 20190096645A1 · Richardson · 2019 [cited by examiner]
GB 2520758A · 2015 [cited by applicant]
WO 2022167796A1 · 2022 [cited by applicant]
Morgner et al., “Massign: An Assignment Strategy for Maximizing Information from the Mass Spectra of Heterogeneous Protein Assemblies” Supporting information Mar. 2012 (Year: 2012). [cited by examiner]
Sun Y, Zhang J, Braga-Neto U, Dougherty ER. BPDA—a Bayesian peptide detection algorithm for mass spectrometry. BMC Bioinformatics. Sep. 29, 2010;11:490. (Year: 2010). [cited by examiner]
Sun Y, Zhang J, Braga-Neto U, Dougherty ER. BPDA—a Bayesian peptide detection algorithm for mass spectrometry. BMC Bioinformatics. Sep. 29, 2010;11:490. doi: 10.1186/1471-2105-11-490. PMID: 20920238; PMCID: PMC3098078. … [cited by examiner]
Gazis, P.R. “A Bayesian Fitness Measure to Score the Results from a Charge State Deconvolution” 2004, 7 pages. [cited by applicant]
Marty et al. “Bayesian Deconvolution of Mass and Ion Mobility Spectra: From Binary Interactions to Polydisperse Ensembles” Analytical Chemistry, Apr. 21, 2015, vol. 87, No. 8, pp. 4370-4276. [cited by applicant]
Kostelic et al. “UniDecCD: Deconvolution of Charge Detection-Mass Spectrometry Data,” Nov. 9, 2021, vol. 93, No. 44, pp. 14722-14729. [cited by applicant]
Morgner et al. “Massign: An Assignment Strategy for Maximizing Information from the Mass Spectra of Heterogeneous Protein Assemblies”, Analytical Chemistry, vol. 84, No. 6, Mar. 2012, pp. 2939-2948. [cited by applicant]
Marty et al. “Bayesian Deconvolution of Mass and Ion Mobility Spectra: From Binary Interactions to Polydisperse Ensembles” Analytical Chemistry, vol. 87, No. 8, Apr. 2015, pp. 4370-4376. [cited by applicant]
Sun et al. “BPDA—A Bayesian peptide detection algorithm for mass spectrometry” BMC Bioinformatics, Biomed Central, London, GB, vol. 11, No. 1, Sep. 29, 2010, p. 490. [cited by applicant]
Millan-Martin et al. “Optimisation of the use of sliding window deconvolution for comprehensive charaterisation of trastuzumab and adalimumab charge variants by native high resolution mass spectrometry” European Journal… [cited by applicant]