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Related Concept Videos

Protein Dynamics in Living Cells01:19

Protein Dynamics in Living Cells

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Different fluorescence-based techniques are used to study the protein dynamics in living cells. These techniques include FRAP, FRET, and PET.
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Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...
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Proteins show rotational as well as lateral diffusion across the membrane. The lateral diffusion of proteins was confirmed through the cell fusion experiment where mouse and human cells were fused, resulting in hybrid cells. When the human and mouse cells fused, the specific membrane proteins on human and mouse cells were marked with the red and green-fluorescent markers, respectively. Initially, the red and green fluorescence was located on the respective hemisphere of the cell. As time...
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Knowledge-enhanced protein subcellular localization prediction from 3D fluorescence microscope images.

Guo-Hua Zeng1,2, Xing-Zheng Zhu3, Hong-Rui Yang1,2

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

Bioinformatics (Oxford, England)
|June 3, 2025
PubMed
Summary

We developed KE3DLoc, a deep learning model for 3D protein subcellular localization. It enhances accuracy by integrating biological knowledge and outperforms existing methods for spatial proteomics.

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Area of Science:

  • Proteomics
  • Cell Biology
  • Bioinformatics

Background:

  • Accurate protein subcellular localization is crucial for understanding protein function and disease.
  • Current machine learning methods for protein localization primarily use 2D images, limiting analysis of complex 3D cellular structures.
  • Analyzing protein distribution in 3D offers higher resolution but faces challenges due to data scarcity and complex modeling.

Purpose of the Study:

  • To develop a novel deep learning model for accurate protein subcellular localization using 3D fluorescence microscope images.
  • To address limitations of existing methods by incorporating biological knowledge and handling 3D image complexities.
  • To facilitate protein translocation analysis and biomarker discovery in spatial proteomics.

Main Methods:

  • Developed KE3DLoc, a knowledge-enhanced deep learning model for 3D protein subcellular localization.
  • Implemented an image feature extraction module using 3D and 2D projected cell information.
  • Incorporated a knowledge enhancement module leveraging Gene Ontology (GO) knowledge graphs and optimized protein representation.
  • Utilized asymmetric loss and confidence weights to manage data imbalance and weak annotations.
  • Employed protein ID aggregation to ensure feature consistency across different cells.

Main Results:

  • KE3DLoc accurately recognizes protein distribution patterns in 3D fluorescence microscope images.
  • The model significantly outperforms existing methods on three public datasets.
  • Experimental results demonstrate the model's effectiveness in spatial proteomics research.

Conclusions:

  • KE3DLoc provides a powerful tool for 3D protein subcellular localization.
  • The knowledge-enhanced approach improves protein representation and localization accuracy.
  • This advancement offers valuable insights for spatial proteomics and disease research.