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

Proteomics

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...

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

Tagging and Fusion Proteins

Tagging and Fusion Proteins

Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...

You might also read

Related Articles

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

Sort by

Same author

Harmonization and Integration of Pharmacogenomics Screens.

Bioinformatics (Oxford, England)·2026

Same author

DREAM repressive activity links somatic mutation, lifespan and disease.

Nature aging·2026

Same author

A foundation model of cancer genotype enables precise predictions of therapeutic response.

Cancer discovery·2026

Same author

An optimization framework for hierarchical clustering.

Bioinformatics advances·2026

Same author

A tri-modal contrastive learning framework for protein representation learning.

Cell reports methods·2026

Same author

Common and rare genetic variants show network convergence for a majority of human traits.

EMBO reports·2026

Same journal

MOREshiny: a user-friendly application for the inference of phenotype-specific multi-omic regulatory networks.

Bioinformatics advances·2026

Same journal

spammR: an R package designed for analysis and integration of spatial multi-omic measurements.

Bioinformatics advances·2026

Same journal

Interpretable prediction and generation of ASC-speck aptamers using multiscale deep biological learning models.

Bioinformatics advances·2026

Same journal

vClassifier: a toolkit for high-resolution phylogenetic classification of prokaryotic viruses.

Bioinformatics advances·2026

Same journal

GWAIS-Web: a free and secure web service for ultra-fast and large-scale genome-wide association interaction studies.

Bioinformatics advances·2026

Same journal

Folding the unfoldable 2: using AlphaFold and ESMFold to explore spurious proteins.

Bioinformatics advances·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 9, 2026

Mapping Dysfunctional Protein-Protein Interactions in Disease

Mapping Dysfunctional Protein-Protein Interactions in Disease

Published on: October 24, 2025

Unifying proteomic technologies with ProteinProjector.

Leah V Schaffer¹, Mayank Jain¹, Rami Nasser²

¹Department of Medicine, University of California San Diego, La Jolla, CA 92093, United States.

Bioinformatics Advances

|December 8, 2025

Summary

This summary is machine-generated.

ProteinProjector, a new deep learning framework, integrates diverse proteomic data to create a unified map of protein location. This approach enhances accuracy and coverage for understanding subcellular organization.

More Related Videos

Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames

Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames

Published on: April 11, 2019

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Related Experiment Videos

Last Updated: Jan 9, 2026

Mapping Dysfunctional Protein-Protein Interactions in Disease

Mapping Dysfunctional Protein-Protein Interactions in Disease

Published on: October 24, 2025

Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames

Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames

Published on: April 11, 2019

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Area of Science:

Proteomics
Systems Biology
Bioinformatics

Background:

Subcellular protein organization is crucial for cellular function.
Existing proteomic methods offer limited scope and sensitivity for mapping protein localization.
Integrating diverse data types is challenging but necessary for comprehensive understanding.

Purpose of the Study:

To develop a novel deep learning framework, ProteinProjector, for integrating multi-modal proteomic data.
To create a unified map of protein subcellular positions by leveraging diverse datasets.
To enhance the accuracy and coverage of subcellular localization predictions.

Main Methods:

Developed a self-supervised deep learning framework named ProteinProjector.
Integrated four proteome-wide datasets from HEK293 cells: AP-MS, PL-MS, SEC-MS, and fluorescent imaging.
Evaluated the framework's performance against individual modalities and other integration methods.

Main Results:

ProteinProjector successfully integrates diverse proteomic data for unified protein mapping.
Map coverage and accuracy significantly improved with the addition of more data modalities.
Maximal recovery of known protein complexes was achieved using all four integrated datasets.
ProteinProjector outperformed individual methods in predicting orthogonal functional and physical associations.

Conclusions:

ProteinProjector provides a robust foundation for integrating diverse data modalities to characterize subcellular structure.
The framework offers improved accuracy and coverage for mapping protein localization compared to existing methods.
This approach facilitates a more comprehensive understanding of cellular organization and protein function.