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A Fast Neural Network for Isotopic Charge State Assignment
John G Pavek1, Nicholas E Bollis2, Josiah Grimes1
1Department of Chemistry and Biochemistry, University of Arizona, Tucson, Arizona 85721, United States.
Journal of the American Chemical Society
|June 10, 2025
Summary
A new neural network algorithm, IsoDec, rapidly and accurately assigns charge states in mass spectrometry, improving proteomic analysis. This advancement enhances feature identification and proteoform-spectrum matching in complex datasets.
Area of Science:
- Analytical Chemistry
- Biochemistry
- Computational Biology
Background:
- Electrospray ionization (ESI) mass spectrometry is crucial for chemical analysis.
- Accurate charge state assignment of analytes is vital for identification in ESI-MS.
- Current charge state assignment methods lack optimal speed and accuracy.
Purpose of the Study:
- To develop a fast and accurate algorithm for charge state assignment in ESI mass spectrometry.
- To evaluate the performance of the developed algorithm, IsoDec, on top-down proteomics data.
- To demonstrate the utility of IsoDec in large-scale proteomic data analysis.
Main Methods:
- Development of a fast neural network for isotopic envelope charge assignment.
- Testing IsoDec on top-down proteomics spectra from diverse instruments.
- Comparison of IsoDec performance against existing software tools.
- Application of IsoDec to large top-down proteomics datasets for database searching.
Main Results:
- IsoDec demonstrates improved speed and accuracy in charge state assignment compared to existing tools.
- The neural network approach directly contributes to IsoDec's performance enhancement.
- Database searching with IsoDec output yields superior proteoform-spectrum matches in terms of coverage and accuracy.
- IsoDec correctly assigns more features on complex individual spectra.
Conclusions:
- IsoDec offers a significant advancement in mass spectrometry data analysis.
- Lightweight neural networks show great potential for improving ESI-MS techniques.
- IsoDec enhances the identification and characterization of proteoforms in complex biological samples.
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