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Related Concept Videos

Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

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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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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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PepFun: Open Source Protocols for Peptide-Related Computational Analysis.

Rodrigo Ochoa1, Pilar Cossio1,2

  • 1Biophysics of Tropical Diseases, Max Planck Tandem Group, University of Antioquia, Medellin 050010, Colombia.

Molecules (Basel, Switzerland)
|April 3, 2021
PubMed
Summary
This summary is machine-generated.

PepFun offers an open-source Python package for comprehensive peptide analysis, covering sequence, structure, and protein interactions. This tool simplifies the study of peptides for biomarker and therapeutic applications.

Keywords:
bioinformaticscheminformaticspeptidepython

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Cheminformatics

Background:

  • Peptide research is expanding due to applications in biomarkers, therapeutics, and vaccines.
  • Computational tools aid in identifying peptide sequences, predicting properties, and modeling structures.
  • A need exists for accessible, open-source protocols for straightforward peptide analysis.

Purpose of the Study:

  • Introduce PepFun, a customizable open-source Python package for multifaceted peptide analysis.
  • Provide a unified platform for studying peptides at sequence, structure, and protein-interaction levels.
  • Facilitate the design and screening of peptide libraries.

Main Methods:

  • PepFun integrates bioinformatics and cheminformatics functionalities.
  • The package utilizes Python with BioPython and RDKit open-source libraries.
  • It enables calculation of peptide characteristics and structural observables from complexes.

Main Results:

  • PepFun allows for the analysis of massive peptide sequence datasets.
  • It facilitates the study of structural aspects of protein-peptide complexes.
  • Customizable random or guided peptide library design is supported.

Conclusions:

  • PepFun provides an easy-to-implement and customizable solution for peptide research.
  • The package streamlines the analysis of peptides for various biological applications.
  • Demonstrated utility through tutorials on MHC class II binders and Granzyme B protease interactions.