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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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Updated: Jul 2, 2025

2D-HELS MS Seq: A General LC-MS-Based Method for Direct and de novo Sequencing of RNA Mixtures with Different Nucleotide Modifications

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Bidirectional de novo peptide sequencing using a transformer model.

Sangjeong Lee¹, Hyunwoo Kim¹

¹Center for Biomedical Computing, Korea Institute of Science and Technology Information, Daejeon, Republic of Korea.

Plos Computational Biology

|February 28, 2024

Summary

NovoB improves de novo sequencing by predicting peptide sequences bidirectionally from both ends. This deep learning approach enhances accuracy in proteomics by leveraging tandem mass spectrometry data more effectively.

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Last Updated: Jul 2, 2025

2D-HELS MS Seq: A General LC-MS-Based Method for Direct and de novo Sequencing of RNA Mixtures with Different Nucleotide Modifications

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Peptide-based Identification of Functional Motifs and their Binding Partners

Peptide-based Identification of Functional Motifs and their Binding Partners

Published on: June 30, 2013

Area of Science:

Proteomics
Computational Biology
Bioinformatics

Background:

De novo sequencing is vital for identifying peptide sequences in proteomics.
Deep learning methods have recently emerged for de novo sequencing.
Existing methods typically predict sequences from the first amino acid.

Purpose of the Study:

To introduce a novel bidirectional deep learning approach for de novo peptide sequencing.
To address the limitations of unidirectional prediction in existing methods.
To improve the accuracy of peptide sequence identification from tandem mass spectra.

Main Methods:

Utilized a Transformer model for de novo sequencing.
Implemented bidirectional prediction, starting from both the first and last amino acids.
Leveraged b-ion and y-ion fragments present in tandem mass spectra.

Main Results:

The NovoB approach demonstrated improved performance over existing methods.
Achieved an approximate 9.8% increase in average peptide-level accuracy across all species.
Showcased the efficacy of bidirectional prediction in de novo sequencing.

Conclusions:

Bidirectional prediction is essential for accurate de novo peptide sequencing.
NovoB offers a significant advancement in proteomics data analysis.
The Transformer model effectively handles complex mass spectrometry data for improved sequence identification.