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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...
Proteomics01:33

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term proteomics...

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Related Experiment Video

Updated: Jun 18, 2026

Resolving Affinity Purified Protein Complexes by Blue Native PAGE and Protein Correlation Profiling
09:35

Resolving Affinity Purified Protein Complexes by Blue Native PAGE and Protein Correlation Profiling

Published on: April 1, 2017

DeepPep: Deep proteome inference from peptide profiles.

Minseung Kim1,2, Ameen Eetemadi1,2, Ilias Tagkopoulos1,2

  • 1Department of Computer Science, University of California, Davis, Davis, California, United States of America.

Plos Computational Biology
|September 6, 2017
PubMed
Summary
This summary is machine-generated.

DeepPep, a deep-convolutional neural network, accurately identifies proteins from peptide data without requiring peptide detectability. This deep learning framework offers a competitive alternative for protein inference in proteomics.

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High-Resolution Complexome Profiling by Cryoslicing BN-MS Analysis
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Related Experiment Videos

Last Updated: Jun 18, 2026

Resolving Affinity Purified Protein Complexes by Blue Native PAGE and Protein Correlation Profiling
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Published on: April 1, 2017

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
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Area of Science:

  • Proteomics
  • Bioinformatics
  • Computational Biology

Background:

  • Protein inference is crucial for identifying protein origins from peptide data in proteomics.
  • Existing methods often rely on peptide detectability, limiting their scope and accuracy.

Purpose of the Study:

  • To introduce DeepPep, a novel deep-convolutional neural network framework for protein inference.
  • To evaluate DeepPep's performance against existing methods, particularly its independence from peptide detectability.

Main Methods:

  • Developed DeepPep, a deep-convolutional neural network framework.
  • Quantified the impact of protein presence/absence on peptide-spectrum match scores.
  • Applied DeepPep to benchmark datasets for performance evaluation.

Main Results:

  • DeepPep achieved competitive predictive performance (AUC of 0.80±0.18, AUPR of 0.84±0.28).
  • The framework successfully infers proteins without relying on peptide detectability.
  • Convolutional neural networks outperformed traditional artificial neural networks in this task.

Conclusions:

  • DeepPep offers a robust and accurate method for protein inference in proteomics.
  • Deep learning architectures, like DeepPep, show promise for related fields such as metagenome and cell type profiling.