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MSModDetector: A Tool for Detecting Mass Shifts and Post-Translational Modifications in Individual Ion Mass Spectrometry Data
Open accessPreprint — published as 10.1093/bioinformatics/btae335 · Paper page
Abstract
Motivation: Post-translational modifications (PTMs) on proteins regulate protein structures and functions. A single protein molecule can possess multiple modification sites that can accommodate various PTM types, leading to a variety of different patterns, or combinations of PTMs, on that protein. Different PTM patterns can give rise to distinct biological functions. To facilitate the study of multiple PTMs, top-down mass spectrometry (MS) has proven to be a useful tool to measure the mass of intact proteins, thereby enabling even widely separated PTMs to be assigned to the same protein molecule and allowing determination of how many PTMs are attached to a single protein. Results: We developed a Python module called MSModDetector that studies PTM patterns from individual ion mass spectrometry (I MS) data. I MS is an intact protein mass spectrometry approach that generates true mass spectra without the need to infer charge states. The algorithm first detects and quantifies mass shifts for a protein of interest and subsequently infers potential PTM patterns using linear programming. The algorithm is evaluated on simulated I MS data and experimental I MS data for the tumor suppressor protein p53. We show that MSModDetector is a useful tool for comparing a protein’s PTM pattern landscape across different conditions. An improved analysis of PTM patterns will enable a deeper understanding of PTM-regulated cellular processes. Availability: The source code is available at https://github.com/marjanfaizi/MSModDetector together with the scripts used for analyses and to generate the figures presented in this study.
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Metadata
- Authors
- Faizi, M., Fellers, R. T., Lu, D., Drown, B. S., Jambhekar, A., Lahav, G., Kelleher, N. L., & Gunawardena, J.
- Affiliations
- Harvard University (US)
- Northwestern University (US)
- Corresponding author
- Gunawardena, Jeremy
- ORCID
- Venue
- Bioinformatics
- Publisher
- openRxiv
- Published
- 2023-06-08
- Volume
- 40
- Issue
- 6
- ISSN
- 1367-4803 (ISSN-L)
- PMC
- PMC11157153
- OpenAlex
- W4399032435
- Cited by
- 4
- References
- 1
- Citations by year
- 2025: 2 · 2024: 2
- FWCI
- 0.50
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Bioinformatics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion)
- Funders
- Deutsche Forschungsgemeinschaft (445690853)
- National Institutes of Health (GM108569)
- National Institute on Drug Abuse (P30DA018310)
- National Cancer Institute (F32CA246894)
- National Institute of General Medical Sciences (P41GM108569, R01GM105375, R35GM139572)
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- post-translational modification, top-down mass spectrometry, linear programming, p53, protein modification sites
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