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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: May 31, 2025

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

Machine learning for antimicrobial peptide identification and design.

Fangping Wan^1,2,3,4,5,6, Felix Wong^7,8,9,6, James J Collins^7,8,9,10,11

¹Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

Nature Reviews Bioengineering

|January 24, 2025

Summary

This summary is machine-generated.

Artificial intelligence (AI) and machine learning (ML) accelerate antimicrobial peptide (AMP) development. These technologies address challenges in designing new antimicrobial therapies to combat resistance.

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Area of Science:

Biotechnology
Computational Biology
Drug Discovery

Background:

Antimicrobial resistance necessitates novel therapeutic strategies.
Antimicrobial peptides (AMPs) show promise but face clinical translation hurdles like toxicity and poor stability.
Traditional antibiotic development is slow and challenging.

Purpose of the Study:

To introduce the application of AI and ML in antimicrobial peptide (AMP) development.
To survey ML approaches for overcoming AMP limitations.
To highlight opportunities in data-driven peptide design for clinical translation.

Main Methods:

Review of current AI and ML methodologies applied to peptide modeling.
Analysis of ML's role in predicting biomolecular properties and generating novel molecules.
Exploration of data-driven approaches for peptide design.

Main Results:

AI and ML offer breakthroughs in predicting peptide properties and designing new molecules.
ML-based modeling can potentially mitigate disadvantages of traditional AMP drug discovery.
Emerging opportunities exist for data-driven design to accelerate AMP development.

Conclusions:

AI and ML are powerful tools to accelerate the development and clinical translation of antimicrobial peptides.
Addressing limitations in AMPs through data-driven design is crucial for combating antimicrobial resistance.
Further research into ML applications can enhance the broader adoption of AMPs in clinical practice.