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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: Jun 4, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

PepAnno: A structure-aware deep learning framework for bioactive peptide prediction, structural visualization, and

Enyan Liu¹, Yueming Hu¹, Liya Liu¹

¹Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, China.

Plos Computational Biology

|June 2, 2026

Summary

This summary is machine-generated.

PepAnno is a new web server for peptide annotation, offering a user-friendly interface and structure-aware deep learning for predicting bioactivities. It improves research efficiency for peptide drug discovery.

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Last Updated: Jun 4, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

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Published on: January 26, 2024

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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
Drug discovery
Bioinformatics

Background:

Peptides are promising therapeutic agents, but existing prediction tools have limitations.
Current methods often lack structural awareness, interpretability, and efficient workflows.

Purpose of the Study:

To develop a comprehensive, user-friendly web server for multi-functional peptide annotation.
To address limitations of existing tools by integrating structural and sequence information.

Main Methods:

Developed PepAnno, a web server utilizing a structure-aware, multi-view geometric deep learning framework.
Integrated pre-trained sequence embeddings with predicted 3D structural graphs using a dual-stream Transformer and GATv2 architecture.
Employed a cross-modal attention mechanism for fusing semantic and geometric representations.

Main Results:

PepAnno accurately predicts 7 key peptide bioactivities, including antimicrobial and anticancer properties.
Demonstrated robust and competitive performance, outperforming or matching existing methods.
Provides automated physicochemical property calculation, structure visualization, and access to integrated databases.

Conclusions:

PepAnno offers an efficient and interpretable solution for large-scale peptide analysis.
Facilitates downstream experimental design and accelerates peptide-based drug discovery.
Enhances research efficiency and reduces costs in bioactive peptide identification.