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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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Related Experiment Video

Updated: Apr 17, 2026

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

Published on: May 22, 2018

D-Flow: Multi-modality Flow Matching for D-peptide Design.

Fang Wu, Shuting Jin, Xiangru Tang

IEEE Journal of Biomedical and Health Informatics

|April 15, 2026

Summary

This summary is machine-generated.

We developed D-Flow, a deep learning framework for designing D-peptides, which are stable therapeutic agents. D-Flow overcomes data scarcity by using a mirror-image algorithm and protein language models for effective de novo D-peptide design.

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Last Updated: Apr 17, 2026

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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:

Computational biology
Biotechnology
Drug discovery

Background:

Proteins are vital for biological functions, with therapeutic peptides showing promise.
D-peptides offer advantages like proteolysis resistance and enhanced in vivo stability.
Limited D-protein data hinders deep learning model application in D-peptide design.

Purpose of the Study:

To introduce D-Flow, a novel full-atom flow-based framework for de novo D-peptide design.
To address the challenge of limited D-protein data using innovative computational strategies.
To enhance D-peptide design by integrating structural information with protein language models.

Main Methods:

D-Flow utilizes structural representations (backbone frames, side-chain angles, amino acid types) for receptor binding.
A mirror-image algorithm converts L-receptor chirality to overcome D-protein data scarcity.
Protein language models are integrated with structural awareness via a lightweight adapter for improved learning.

Main Results:

D-Flow effectively designs D-peptides with improved sequence identity (10.2% over baseline) and high affinity scores (top 24.31%).
Generated D-peptides exhibit closer alignment with native sequences and structures.
The framework demonstrates successful transition from general to targeted binder design.

Conclusions:

D-Flow shows significant potential for advancing D-peptide design.
The framework facilitates the development of bioorthogonal, stable molecular tools and diagnostics.
D-Flow offers a viable solution for overcoming data limitations in D-peptide discovery.