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Peptide Identification Using Tandem Mass Spectrometry

Peptide Identification Using Tandem Mass Spectrometry

Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...

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Selected Reaction Monitoring Mass Spectrometry for Absolute Protein Quantification

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Systematic Errors in Peptide and Protein Identification and Quantification by Modified Peptides.

Boris Bogdanow¹, Henrik Zauber¹, Matthias Selbach²

¹From the ‡Proteome Dynamics lab, Max Delbrück Center for Molecular Medicine, Robert-Rössle-Str.13, 13092 Berlin, Germany.

Molecular & Cellular Proteomics : MCP

|May 25, 2016

Summary

Modified peptides in shotgun proteomics cause significant false identifications and quantification biases. A new "cleaned search" strategy improves accuracy in peptide and protein analysis.

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Area of Science:

Proteomics
Biochemistry
Bioinformatics

Background:

Shotgun proteomics relies on accurate peptide-to-spectrum matching for protein identification and quantification.
Unassigned spectra in proteomic samples often originate from modified peptides.
The impact of modified peptides on identification accuracy is not well-understood.

Purpose of the Study:

To systematically investigate the impact of modified peptides on false positive rates in shotgun proteomics.
To develop and validate a strategy to mitigate errors caused by modified peptides.
To enhance the accuracy and reliability of proteomic data analysis.

Main Methods:

Utilized combinations of different database search algorithms to analyze peptide-spectrum assignments.
Quantified the contribution of modified peptides to false positive identifications.
Developed and implemented a "cleaned search" strategy for improved data analysis.

Main Results:

Modified peptides were found to account for 20-50% of false positive identifications in deep proteomic datasets.
False positive peptide assignments, particularly from modified peptides, exhibited higher scores and intensities.
These errors led to numerous false protein identifications and systematic quantification biases.

Conclusions:

Modified peptides are a significant source of systematic errors in peptide and protein identification and quantification.
The "cleaned search" strategy substantially enhances the sensitivity and specificity of proteomic data.
Considering modified peptides is crucial for improving the quality of proteomic data annotation.