Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Peptide Identification Using Tandem Mass Spectrometry

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...

You might also read

Related Articles

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

Sort by

Same author

A genome-wide genetic screen identifies a novel kDNA replication protein in trypanosomes.

Nucleic acids research·2026

Same author

Lossless resistive micro-heater design for reconfigurable phase-change photonics.

Optics letters·2026

Same author

FMOPhore for hotspot identification and efficient fragment-to-lead growth strategies.

Nature communications·2026

Same author

Genetic origins and proteomic consequences of kinetoplast loss in trypanosomes.

PLoS pathogens·2026

Same author

Acoziborole resistance associated mutations in Trypanosoma brucei CPSF3.

PLoS pathogens·2026

Same author

Decoding efficacy and resistance space at a drug binding site.

Nature communications·2026

Same journal

Covariance decomposition for distance based species tree estimation.

BMC bioinformatics·2026

Same journal

SNPio: a Python interface for population genomic data processing.

BMC bioinformatics·2026

Same journal

SpaHNR: a spatial domain identification method via sparse attention-based hierarchical node representation and multi-view contrastive learning.

BMC bioinformatics·2026

Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026

Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026

Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 17, 2026

Detection of Protein Ubiquitination Sites by Peptide Enrichment and Mass Spectrometry

Detection of Protein Ubiquitination Sites by Peptide Enrichment and Mass Spectrometry

Published on: March 23, 2020

Data mining of enzymes using specific peptides.

Uri Weingart¹, Yair Lavi, David Horn

¹School of Physics and Astronomy, Tel Aviv University, Tel Aviv 69978, Israel. uriweing@tau.ac.il

BMC Bioinformatics

|December 26, 2009

Summary

This summary is machine-generated.

This study introduces a novel motif method using Specific Peptides (SPs) to predict protein enzyme function and classification. The Data Mining of Enzymes (DME) methodology accurately identifies enzymes and their EC classification, especially with coverage-length criteria.

More Related Videos

Fast Enzymatic Processing of Proteins for MS Detection with a Flow-through Microreactor

Fast Enzymatic Processing of Proteins for MS Detection with a Flow-through Microreactor

Published on: April 6, 2016

A Spin-Tip Enrichment Strategy for Simultaneous Analysis of N-Glycopeptides and Phosphopeptides from Human Pancreatic Tissues

A Spin-Tip Enrichment Strategy for Simultaneous Analysis of N-Glycopeptides and Phosphopeptides from Human Pancreatic Tissues

Published on: May 4, 2022

Related Experiment Videos

Last Updated: Jun 17, 2026

Detection of Protein Ubiquitination Sites by Peptide Enrichment and Mass Spectrometry

Detection of Protein Ubiquitination Sites by Peptide Enrichment and Mass Spectrometry

Published on: March 23, 2020

Fast Enzymatic Processing of Proteins for MS Detection with a Flow-through Microreactor

Fast Enzymatic Processing of Proteins for MS Detection with a Flow-through Microreactor

Published on: April 6, 2016

A Spin-Tip Enrichment Strategy for Simultaneous Analysis of N-Glycopeptides and Phosphopeptides from Human Pancreatic Tissues

A Spin-Tip Enrichment Strategy for Simultaneous Analysis of N-Glycopeptides and Phosphopeptides from Human Pancreatic Tissues

Published on: May 4, 2022

Area of Science:

Bioinformatics
Computational Biology
Enzymology

Background:

Protein function prediction from sequence is a key challenge in bioinformatics.
Traditional methods rely on sequence similarity or motifs.
Specific Peptides (SPs) offer a novel motif-based approach for enzyme classification.

Purpose of the Study:

To develop and validate a new methodology for predicting enzyme function and classification using SPs.
To establish the effectiveness of the Data Mining of Enzymes (DME) methodology.
To compare DME with existing methods like BLAST.

Main Methods:

Devised the Data Mining of Enzymes (DME) methodology to search for Specific Peptides (SPs) in protein sequences.
Extracted novel SP sets from Swiss-Prot enzyme data.
Utilized training and test sets from 2006-2008, and later updated with 2009 data.

Main Results:

The predictive power of SPs depends on the coverage length of matches.
DME showed lower recall than BLAST but identified proteins with low homology.
A coverage-length cutoff (L >= 7) proved effective for identifying true negatives.
Newly annotated enzymes were identified post-prediction.
Application to metagenomic data provided enzymatic profiles of microbial communities.

Conclusions:

Specific Peptides (SPs) effectively predict enzymatic activity and classification.
Coverage-length criteria are crucial for accurate predictions, with L >= 7 yielding high accuracy.
The DME methodology offers complementary information to sequence similarity searches and is applicable to large-scale metagenomic analyses.