MSModDetector: a Tool for Detecting Mass Shifts and Post-translational Modifications in Individual Ion Mass Spectrometry Data

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2024 May 25

PMID 38796681

Authors

Marjan Faizi

Ryan T Fellers

Dan Lu

Bryon S Drown

Ashwini Jambhekar

Galit Lahav

Neil L Kelleher

Jeremy Gunawardena

Affiliations

Soon will be listed here.

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 on the same protein molecule, top-down mass spectrometry (MS) has proven to be a useful tool to measure the mass of intact proteins, thereby enabling even PTMs at distant sites 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 (I2MS) data. I2MS 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 I2MS data and experimental I2MS 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 And Implementation: The source code is available at https://github.com/marjanfaizi/MSModDetector.

Citing Articles

Temporal regulation of gene expression through integration of p53 dynamics and modifications.

Lu D, Faizi M, Drown B, Simerzin A, Francois J, Bradshaw G Sci Adv. 2024; 10(43):eadp2229.

PMID: 39454005 PMC: 11506164. DOI: 10.1126/sciadv.adp2229.

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