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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,...

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Related Experiment Video

Updated: Oct 7, 2025

Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells

Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells

Published on: June 30, 2018

Learning protein subcellular localization multi-view patterns from heterogeneous data of imaging, sequence and

Ge Wang^1,2, Min-Qi Xue^1,2, Hong-Bin Shen^3,4

¹School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China.

Briefings in Bioinformatics

|January 12, 2022

Summary

This study introduces SIN-Locator, a new tool that integrates protein sequence, image, and network data for more accurate subcellular localization prediction. Combining diverse data sources significantly improves protein location classification.

Keywords:

deep learning multi-kernel learning multi-source data protein subcellular location

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Last Updated: Oct 7, 2025

Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells

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Published on: June 30, 2018

Localizing Protein in 3D Neural Stem Cell Culture: a Hybrid Visualization Methodology

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Published on: December 19, 2010

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

Area of Science:

Cellular Biology
Bioinformatics
Proteomics

Background:

Location proteomics aims to automate high-resolution protein location descriptions within cells.
Existing protein subcellular localization predictors often rely on single data types like images or amino acid sequences.
Integrating heterogeneous protein data sources remains underexplored.

Purpose of the Study:

To present SIN-Locator, a pipeline for multi-view protein description by integrating diverse data types.
To enhance the accuracy of protein subcellular localization classification.
To demonstrate the utility of integrated data for protein location analysis.

Main Methods:

Developed SIN-Locator, a pipeline integrating protein expression images, amino acid sequences, and protein-protein interaction networks.
Encoded proteins using both handcrafted and deep learning features.
Implemented multiple data integration and classification methods.

Main Results:

Optimal integration of heterogeneous data sources significantly enhanced classification accuracy for protein subcellular localization.
SIN-Locator demonstrated utility when applied to new proteins in the Human Protein Atlas.
Investigated the contribution of individual data sources and the impact of data absence.

Conclusions:

Unifying heterogeneous protein data sources, including images, sequences, and networks, is crucial for accurate subcellular localization.
SIN-Locator provides a robust framework for multi-source data integration in proteomics.
This work offers insights into reconciling and combining multi-source data for advanced protein location analysis.