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Related Concept Videos

Resonance and Hybrid Structures02:16

Resonance and Hybrid Structures

According to the theory of resonance, if two or more Lewis structures with the same arrangement of atoms can be written for a molecule, ion, or radical, the actual distribution of electrons is an average of that shown by the various Lewis structures.
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Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions
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FRET-SAM: SAM_Med2D-based automatic FRET two-hybrid analysis.

Jingzhen Wang1, Yanling Xu1, Beini Sun1

  • 1Key Laboratory of Laser Life Science, Ministry of Education, College of Biophotonics, School of Optoelectronic Science and Engineering, South China Normal University, Guangzhou, 510631, Guangdong, China; Guangdong Key Laboratory of Laser Life Science, College of Biophotonics, School of Optoelectronic Science and Engineering, South China Normal University, Guangzhou, 510631, Guangdong, China.

Computer Methods and Programs in Biomedicine
|December 16, 2025
PubMed
Summary

This study introduces FRET-SAM, a deep learning tool that automates fluorescence resonance energy transfer (FRET) two-hybrid analysis. FRET-SAM enhances accuracy and efficiency in quantifying protein interactions and shows promise for drug discovery.

Keywords:
Automatic image processingFRETFRET two-hybrid analysisSAM_Med2D

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Area of Science:

  • Biophysics
  • Computational Biology
  • Molecular Biology

Background:

  • Fluorescence resonance energy transfer (FRET) two-hybrid assays quantify protein interactions in living cells.
  • Manual analysis of FRET images is labor-intensive and computationally complex, limiting assay application.
  • Deep learning offers a solution to automate FRET image analysis, improving efficiency and accuracy.

Purpose of the Study:

  • To develop a deep learning method for automated analysis of FRET two-hybrid images.
  • To enhance the efficiency, accuracy, and objectivity of FRET two-hybrid assays.
  • To establish FRET-SAM as a tool for drug discovery by resolving drug-target interactions.

Main Methods:

  • Developed FRET-SAM, an optimized analysis method based on the Segment Anything Model (SAM).
  • Adapted SAM_Med2D for automated region of interest (ROI) selection and fluorescence signal extraction in FRET images.
  • Trained and validated FRET-SAM using a comprehensive FRET image dataset with multiple plasmids and FRET pairs.

Main Results:

  • FRET-SAM demonstrated improved segmentation accuracy (MPA, MIoU, Dice coefficient) compared to SAM_Med2D.
  • FRET-SAM-derived results showed high consistency with literature values (relative errors < 5%).
  • FRET-SAM showed potential in drug screening case studies, resolving drug-target interactions.

Conclusions:

  • FRET-SAM significantly enhances FRET two-hybrid assay efficiency and accuracy through automated image analysis.
  • The elimination of subjective bias improves the reliability of protein interaction quantification.
  • FRET-SAM is a promising tool for drug discovery due to its ability to resolve drug-target interactions.