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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.
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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Related Experiment Video

Updated: Apr 15, 2026

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation
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Prediction of antimicrobial peptides based on sequence alignment and support vector machine-pairwise algorithm

Xin Yi Ng1, Bakhtiar Affendi Rosdi2, Shahriza Shahrudin3

  • 1School of Electrical & Electronic Engineering, Universiti Sains Malaysia, 14300 Nibong Tebal, Seberang Perai Selatan, Pulau Pinang, Malaysia.

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|March 25, 2015
PubMed
Summary

This study introduces a new computational method for predicting antimicrobial peptides (AMPs) to combat antibiotic resistance. The integrated algorithm shows high sensitivity in identifying potential AMPs from protein sequences, aiding drug discovery.

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Drug Discovery

Background:

  • Rising antibiotic resistance in bacterial strains necessitates novel therapeutic strategies.
  • Antimicrobial peptides (AMPs) are crucial for innate immunity and are being explored as alternative drugs.
  • Experimental methods for AMP identification are time-consuming and expensive, highlighting the need for computational tools.

Purpose of the Study:

  • To develop and validate a novel computational algorithm for predicting antimicrobial peptides (AMPs).
  • To address the limitations of experimental AMP identification by providing a faster and more cost-effective in silico approach.

Main Methods:

  • An integrated algorithm combining sequence alignment and a Support Vector Machine (SVM)-LZ complexity pairwise algorithm was developed.
  • The algorithm's performance was evaluated using jackknife and independent test sets.

Main Results:

  • The proposed algorithm achieved a sensitivity of 95.28% (jackknife) and 87.59% (independent test) when using all training sequences.
  • When using sequences with <70% similarity, sensitivities were 88.74% (jackknife) and 78.70% (independent test).

Conclusions:

  • The developed integrated algorithm effectively predicts antimicrobial peptides (AMPs) from protein sequences.
  • This computational tool offers a highly sensitive method for identifying potential AMPs, supporting the development of new antibiotics.