Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

WNetAlign: fast and accurate spectra alignment using truncated Wasserstein distance and network simplex.

Briefings in bioinformatics·2026
Same author

spatialstein: An Open-Source Workflow for Annotation, Deconvolution, and Spatially Aware Segmentation of Mass Spectrometry Imaging Data.

Analytical chemistry·2026
Same author

Efficient Compression of Mass Spectrometry Images via Contrastive Learning-Based Encoding.

Analytical chemistry·2025
Same author

NMR Reaction Monitoring Robust to Spectral Distortions.

Analytical chemistry·2025
Same author

An Automated Analysis of Homocoupling Defects Using MALDI-MS and Open-Source Computer Software.

Journal of the American Society for Mass Spectrometry·2024
Same author

MIND4OLIGOS: Determining the Monoisotopic Mass of Oligonucleotides Observed in High-Resolution Mass Spectrometry.

Analytical chemistry·2024

Related Experiment Video

Updated: Jun 25, 2026

Capture Compound Mass Spectrometry - A Powerful Tool to Identify Novel c-di-GMP Effector Proteins
12:11

Capture Compound Mass Spectrometry - A Powerful Tool to Identify Novel c-di-GMP Effector Proteins

Published on: March 29, 2015

Modeling exopeptidase activity from LC-MS data.

Bogusław Kluge1, Anna Gambin, Wojciech Niemiro

  • 1Institute of Informatics, University of Warsaw, Warsaw, Poland. bogklug@mimuw.edu.pl

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|February 6, 2009
PubMed
Summary

Cancer detection may be improved by modeling exopeptidase activity using statistical analysis of serum peptides. This approach aids in classifying cancer through proteomic analysis of liquid chromatography-mass spectrometry samples.

More Related Videos

Characterization of Neuronal Lysosome Interactome with Proximity Labeling Proteomics
11:40

Characterization of Neuronal Lysosome Interactome with Proximity Labeling Proteomics

Published on: June 23, 2022

Profiling of Methyltransferases and Other S-adenosyl-L-homocysteine-binding Proteins by Capture Compound Mass Spectrometry (CCMS)
17:12

Profiling of Methyltransferases and Other S-adenosyl-L-homocysteine-binding Proteins by Capture Compound Mass Spectrometry (CCMS)

Published on: December 20, 2010

Related Experiment Videos

Last Updated: Jun 25, 2026

Capture Compound Mass Spectrometry - A Powerful Tool to Identify Novel c-di-GMP Effector Proteins
12:11

Capture Compound Mass Spectrometry - A Powerful Tool to Identify Novel c-di-GMP Effector Proteins

Published on: March 29, 2015

Characterization of Neuronal Lysosome Interactome with Proximity Labeling Proteomics
11:40

Characterization of Neuronal Lysosome Interactome with Proximity Labeling Proteomics

Published on: June 23, 2022

Profiling of Methyltransferases and Other S-adenosyl-L-homocysteine-binding Proteins by Capture Compound Mass Spectrometry (CCMS)
17:12

Profiling of Methyltransferases and Other S-adenosyl-L-homocysteine-binding Proteins by Capture Compound Mass Spectrometry (CCMS)

Published on: December 20, 2010

Area of Science:

  • Biochemistry
  • Proteomics
  • Computational Biology

Background:

  • Tumor protease activity generates peptides in cancer patient serum.
  • These peptides show potential for cancer detection and classification.
  • Exopeptidase activity is a key factor in generating these diagnostic peptides.

Purpose of the Study:

  • To develop the first formal statistical model for exopeptidase activity.
  • To analyze exopeptidase activity using liquid chromatography-mass spectrometry (LC-MS) data.
  • To validate the model on real-world clinical samples.

Main Methods:

  • Statistical modeling of peptidome degradation.
  • Development of a Metropolis-Hastings algorithm for Bayesian inference.
  • Application and validation using LC-MS datasets from cancer patients.

Main Results:

  • Successful validation of the proposed statistical model on a real LC-MS dataset.
  • Demonstration of quantifiable exopeptidase activity patterns.
  • Evidence supporting disease-specific exopeptidase activity.

Conclusions:

  • The developed model provides a formal approach to analyzing exopeptidase activity.
  • Findings support the link between exopeptidase activity and specific diseases.
  • This research opens avenues for novel diagnostic approaches in clinical proteomics.