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Protein Dynamics in Living Cells

Protein Dynamics in Living Cells

Different fluorescence-based techniques are used to study the protein dynamics in living cells. These techniques include FRAP, FRET, and PET.
Fluorescent recovery after photobleaching (FRAP) is a fluorescent-protein-based detection technique used to quantify protein movement rates within the cell. This method exposes a small portion of the cell to an intense laser beam. The laser beam causes permanent photobleaching of the fluorophore-tagged proteins in the exposed region. As the bleached...

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Related Experiment Video

Updated: Jun 30, 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

Enhancing subcellular protein localization mapping analysis using Sc2promap utilizing attention mechanisms.

Kaitai Han¹, Xi Liu¹, Guocheng Sun¹

¹Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China.

Biochimica Et Biophysica Acta. General Subjects

|March 24, 2024

Summary

The sc2promap model accurately maps subcellular protein localization in cells. This advanced method improves feature extraction from protein images, outperforming existing techniques for disease research.

Keywords:

Clustering Endogenous labeled proteins Large-scale protein image analysis Spatial and channel attention Subcellular protein localization Vector quantization

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Last Updated: Jun 30, 2025

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Area of Science:

Biophysics
Cell Biology
Bioinformatics

Background:

Aberrant protein localization is a hallmark of human diseases, impacting tissue and organ function.
High-throughput technologies and bioinformatics generate large, pattern-rich datasets for analysis.

Purpose of the Study:

To introduce sc2promap (Spatial and Channel for SubCellular Protein Localization Mapping) for protein localization analysis and clustering.
To extract meaningful features from single-channel grayscale protein images.

Main Methods:

sc2promap utilizes a spatial-channel attention mechanism within its encoder.
A prediction head component with supplementary protein annotations enhances feature extraction.
The model generates high-resolution protein localization maps, including nuclear and non-nuclear domains.

Main Results:

sc2promap demonstrated superior performance compared to previous methods.
Qualitative and quantitative evaluations confirmed the model's effectiveness.
Analysis of model components highlighted the contribution of attention and prediction head.

Conclusions:

The integration of attention mechanisms and prediction head components significantly improves protein localization clustering and analysis.
sc2promap enhances the extraction of features and knowledge from protein fluorescence images.