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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

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An algorithm for identifying multiply modified endogenous proteins using both full-scan and high-resolution tandem

Matthew T Mazur¹, Ray Fyhr

¹Department of Proteomics, Merck & Co., Inc., 126 E. Lincoln Avenue, P.O. Box 2000, Rahway, NJ 07065, USA.

Rapid Communications in Mass Spectrometry : RCM

|November 19, 2011

Summary

A new Mass spectrometry Analysis (MAR) algorithm identifies peptides and proteins from complex biological samples. This tool aids in analyzing mass spectrometry data, especially for proteins with multiple modifications.

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Detection of Protein Ubiquitination Sites by Peptide Enrichment and Mass Spectrometry

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Detection of Protein Ubiquitination Sites by Peptide Enrichment and Mass Spectrometry

Detection of Protein Ubiquitination Sites by Peptide Enrichment and Mass Spectrometry

Published on: March 23, 2020

Area of Science:

Biochemistry
Proteomics
Analytical Chemistry

Background:

High-resolution tandem mass spectrometry (MS/MS) advances proteomics but identifying complex peptides remains a challenge.
Unsupervised identification of proteins with multiple, unpredicted processing events is particularly difficult in biological samples.
Current methods often struggle with the complexity of 'top-down' proteomics data.

Purpose of the Study:

To develop a novel search algorithm, MAR, for identifying peptides and proteins from MS/MS spectra.
To overcome limitations in identifying multiply processed proteins in complex proteomic samples.
To create a scalable and efficient tool for proteomic data analysis.

Main Methods:

Developed the Mass spectrometry Analysis (MAR) algorithm using a predefined list of differential modifications and a FASTA protein database.
Integrated a feature to automatically identify potential differential modifications from mass differences in full-scan MS spectra.
Applied the algorithm to identify polypeptides from human apolipoprotein enriched from high-density lipoprotein (HDL) particles.

Main Results:

Successfully identified 54 unique polypeptides from human apolipoprotein.
Demonstrated algorithm scalability with search benchmarks of 12 high-resolution MS/MS scans per minute.
The algorithm is parallelizable and does not require full-length protein constraints for identification.

Conclusions:

The MAR algorithm offers a robust solution for reliable identification of multiply processed proteins.
This tool enhances the conversion of high-quality MS/MS data into meaningful proteomic identifications.
MAR improves the analysis of complex biological samples in mass spectrometry-based proteomics.