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Related Concept Videos

Peptide Identification Using Tandem Mass Spectrometry01:33

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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.
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A peptide bond covalently attaches amino acids through a dehydration reaction. One amino acid's carboxyl group and another amino acid's amino group combine, releasing a water molecule. The resulting bond is the peptide bond. The products that such linkages form are peptides. As more amino acids join this growing chain, the resulting chain is a polypeptide. Each polypeptide has a free amino group at one end. This end has the N-terminal, or the amino-terminal, and the other end has a free...
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Split-and-pool Synthesis and Characterization of Peptide Tertiary Amide Library
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peptidy: a light-weight Python library for peptide representation in machine learning.

Rıza Özçelik1,2, Laura van Weesep1, Sarah de Ruiter1

  • 1Department of Biomedical Engineering, Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven 5612AZ, Netherlands.

Bioinformatics Advances
|April 2, 2025
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Summary
This summary is machine-generated.

Introducing peptidy, a Python library for converting peptide sequences into numerical formats for machine learning. This tool supports various encoding strategies and post-translational modifications, accelerating peptide research.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Machine Learning

Background:

  • Peptides are crucial in drug discovery and food science.
  • Machine learning accelerates peptide research.
  • User-friendly computational tools are needed.

Purpose of the Study:

  • Introduce peptidy, a Python library for peptide-to-numerical representation conversion.
  • Facilitate machine learning applications in peptide research.
  • Support diverse encoding strategies and post-translational modifications.

Main Methods:

  • Developed a lightweight Python library named peptidy.
  • Implemented various encoding strategies for peptide sequences.
  • Included support for post-translational modifications (phosphorylation, acetylation, methylation).

Main Results:

  • peptidy enables seamless conversion of peptides to machine learning-ready numerical formats.
  • The library is dependency-free and integrates easily into Python environments.
  • Extended functionality for peptides with post-translational modifications.

Conclusions:

  • peptidy enhances machine learning approaches for peptide discovery and design.
  • The library offers a versatile tool for computational peptide research.
  • peptidy is available on GitHub under a permissive license.