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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...

Tandem Mass Spectrometry

Tandem Mass Spectrometry

Tandem mass spectrometry is a technique that uses multiple mass analyzers in series to obtain a higher selectivity and reduce chemical noise during analyte detection. Instruments with multiple analyzers separated by an interaction cell enable secondary fragmentation and selected study of the fragment ions.Secondary fragmentations occur in the interaction cell and can be induced by various factors. Fragmentation induced by collision with inert gases, such as N2, Ar, He, etc., is called...

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Related Experiment Video

Updated: Jan 1, 2026

Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools

Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools

Published on: August 19, 2025

MS2CNN: predicting MS/MS spectrum based on protein sequence using deep convolutional neural networks.

Yang-Ming Lin¹, Ching-Tai Chen², Jia-Ming Chang³

¹Department of Computer Science, National Chengchi University, 11605, Taipei City, Taiwan.

|December 26, 2019

Summary

This summary is machine-generated.

MS2CNN, a deep learning model, accurately predicts tandem mass spectrometry spectra for peptide identification. This advanced tool enhances spectral library searches, improving protein and peptide discovery in mass spectrometry data.

Keywords:

Deep convolutional neural networks Deep learning Machine learning Mass spectrum Peptide Protein identification Spectral library search Tandem mass spectrometry

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Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools

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High-Resolution Complexome Profiling by Cryoslicing BN-MS Analysis

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Published on: October 15, 2019

Area of Science:

Proteomics
Computational Biology
Biotechnology

Background:

Tandem mass spectrometry (MS2) is vital for identifying and quantifying proteins via peptide sequences.
Spectral library search offers higher sensitivity than database search but is limited to known peptides.
Accurate MS2 spectrum prediction is crucial for expanding spectral libraries and improving peptide identification coverage.

Purpose of the Study:

To develop an accurate tandem mass spectrum prediction tool using deep learning.
To enhance the coverage and sensitivity of spectral library searches in proteomics.

Main Methods:

Proposed MS2CNN, a non-linear regression model utilizing deep convolutional neural networks.
Engineered features include amino acid composition, predicted secondary structure, and physicochemical properties.
Trained and validated using large-scale human HCD MS2 datasets from Orbitrap LC-MS/MS.

Main Results:

MS2CNN demonstrated superior cosine similarity and Pearson correlation coefficients compared to MS2PIP.
Performance was comparable to pDeep, another advanced prediction tool.
MS2CNN showed significant improvements for complex 3+ peptide spectra.

Conclusions:

MS2CNN generates highly accurate MS2 spectra for Orbitrap LC-MS/MS experiments.
The model aids significantly in protein and peptide identification, enhancing spectral library searches.
Further improvements may be achieved by incorporating larger datasets for deep learning model training.