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Peptide Bonds

Peptide Bonds

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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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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Protein digestion begins in the stomach, where the highly acidic environment can easily disrupt protein structure by exposing the peptide bonds of polypeptide chains. After polypeptide chains are broken into individual amino acids by a series of digestive enzymes, the amino acids are transported to the liver via the bloodstream to produce energy.

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Updated: May 4, 2026

Solid Phase Synthesis of a Functionalized Bis-Peptide Using "Safety Catch" Methodology

Solid Phase Synthesis of a Functionalized Bis-Peptide Using "Safety Catch" Methodology

Published on: May 15, 2012

Defining peptides in ChEBI.

Simon Flügel¹, Till Mossakowski², Fabian Neuhaus³

¹Institute for Computer Science, University of Osnabrück, Neuer Graben 29, Osnabrück, 49074, Lower Saxony, Germany. simon.fluegel@uos.de.

Journal of Cheminformatics

|May 2, 2026

Summary

This summary is machine-generated.

We developed a novel peptide taxonomy and automated classification method for the Chemical Entities of Biological Interest (ChEBI) ontology. This approach enhances knowledge organization and identifies potential inconsistencies in existing chemical databases.

Keywords:

ChEBI Monadic second-order logic Peptides

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Last Updated: May 4, 2026

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Area of Science:

Biochemistry
Bioinformatics
Ontology Engineering

Background:

Modern biochemistry generates extensive chemical data, necessitating efficient knowledge organization.
Existing ontologies like Chemical Entities of Biological Interest (ChEBI) struggle to keep pace with domain growth through manual classification alone.

Purpose of the Study:

To propose a novel, comprehensive taxonomy of 67 peptide-related classes for the ChEBI ontology.
To develop a methodology for automated classification based on formal logical axiomatization.

Main Methods:

Expanded and refined natural language definitions for peptide classes.
Formalized definitions using monadic second-order logic (MSOL).
Developed a translation methodology from MSOL to algorithmic classification.

Main Results:

Created a new taxonomy with 53 novel peptide classes and refined 14 existing ones.
Implemented an efficient algorithm for large-scale molecule classification.
Identified potential inconsistencies within the current ChEBI taxonomy through comparative analysis.

Conclusions:

The proposed automated classification method enhances ontological precision and consistency.
This approach facilitates the large-scale organization and validation of chemical knowledge.
Expert evaluation confirmed the quality of the natural language definitions.