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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: Jun 9, 2026

Semi-automated Biopanning of Bacterial Display Libraries for Peptide Affinity Reagent Discovery and Analysis of Resulting Isolates

Semi-automated Biopanning of Bacterial Display Libraries for Peptide Affinity Reagent Discovery and Analysis of Resulting Isolates

Published on: December 6, 2017

High-PepBinder: A pLM-Guided Latent Diffusion Framework for Affinity-Aware Target-Specific Peptide Design.

Qingyi Mao¹, Silong Zhai², Sen Cao²

¹College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, China.

Journal of Chemical Information and Modeling

|April 4, 2026

Summary

This summary is machine-generated.

High-PepBinder is a new AI framework for designing therapeutic peptides. It generates target-specific peptides using only protein sequences, improving drug discovery for challenging targets.

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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

Area of Science:

Computational biology
Drug discovery
Bioinformatics

Background:

Peptide therapeutics offer advantages for targeting complex protein surfaces.
Rational peptide design is hindered by vast sequence space and traditional method limitations.

Purpose of the Study:

To introduce High-PepBinder, a sequence-only conditional diffusion framework for target-specific peptide generation.
To address limitations in current peptide design strategies.

Main Methods:

Developed High-PepBinder, a dual-encoder diffusion model integrating protein language models (pLMs).
Utilized a target protein sequence to guide peptide generation.
Incorporated an affinity classifier and joint optimization for enhanced binding features.
Created the comprehensive PepPBA dataset and a structure/physics-based screening pipeline.

Main Results:

High-PepBinder showed competitive performance in peptide generation and binding tasks.
Generated peptides for targets like KEAP1, XIAP, and EGFR maintained key binding geometries and interface patterns.
The model produced sequence-diverse peptides with favorable predicted properties.

Conclusions:

High-PepBinder offers a general, sequence-only strategy for peptide design.
This computational framework facilitates peptide discovery against challenging therapeutic targets.