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Related Concept Videos

Peptide Identification Using Tandem Mass Spectrometry01:33

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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Protein Families

Protein families are groups of homologous proteins; that is, they have similarities in amino acid sequences and three-dimensional structures. Protein families usually occur because of gene duplication, where an additional copy of a gene is inserted into the genome of an organism.   Mutations that change the amino acids but still allow the protein to be properly synthesized, will lead to new protein family members.   If these new proteins contain similar amino acids in key locations, protein...
Signal Sequences and Sorting Receptors01:41

Signal Sequences and Sorting Receptors

Signal sequences are short amino acid sequences that guide newly synthesized proteins to their proper location within the cell. Classical signal sequences are fifteen to sixty amino acids long and present at the N-terminus of a polypeptide chain. Each signal sequence has a conserved segment of basic residues towards their N terminus, a hydrophobic core, and a C-terminus rich in polar residues. The C-terminus also contains a signal cleavage site and features a -3 -1 sequence motif. The -3-1...
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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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Peptide bioinformatics: peptide classification using peptide machines.

Zheng Rong Yang1

  • 1School of Biosciences, University of Exeter, UK.

Methods in Molecular Biology (Clifton, N.J.)
|December 11, 2008
PubMed
Summary
This summary is machine-generated.

This study explores peptide classification using machine learning, specifically peptide machines, for predicting protein functions and structures. It details essential concepts, feature extraction, and key issues in this bioinformatics field.

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Published on: May 31, 2022

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Peptide-based Identification of Functional Motifs and their Binding Partners
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Peptide-based Identification of Functional Motifs and their Binding Partners

Published on: June 30, 2013

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning

Background:

  • Peptides from protein sequences are crucial for bioinformatics tasks like functional site prediction and protein structure identification.
  • Accurate peptide classification is essential for understanding protein roles and structures.

Purpose of the Study:

  • To discuss fundamental concepts of peptide classification.
  • To review common feature extraction methods used in peptide classification.
  • To introduce three types of peptide machines and discuss critical issues in the field.

Main Methods:

  • Utilizing machine learning approaches, particularly peptide machines, for peptide classification.
  • Exploring various feature extraction techniques relevant to peptide sequences.
  • Analyzing the performance and application of different peptide machine models.

Main Results:

  • Peptide machines show excellent performance in various peptide classification applications.
  • Effective feature extraction is key to successful peptide classification.
  • Understanding common issues enhances the reliability of peptide classification models.

Conclusions:

  • Peptide classification is a vital area within bioinformatics with significant applications.
  • Machine learning, especially peptide machines, offers powerful tools for peptide analysis.
  • Further research into feature extraction and model optimization will advance peptide classification accuracy.