FRET-SAM:基于SAM_Med2D的自动FRET双混合分析
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
概括
本研究介绍了FRET-SAM,这是一种深度学习工具,可以自动化光共振能量转移 (FRET) 两种混合分析. FRET-SAM提高了蛋白质相互作用量化的准确性和效率,并显示了药物发现的前景.
科学领域:
- 生物物理学的生物物理.
- 计算生物学 计算生物学
- 分子生物学分子生物学
背景情况:
- 光共振能量转移 (FRET) 两种混合试验量化活细胞中的蛋白质相互作用.
- 手动分析FRET图像是劳动密集型和计算复杂的,限制了测试应用.
- 深度学习为自动化FRET图像分析提供了解决方案,提高了效率和准确性.
研究的目的:
- 开发一种深度学习方法,用于自动分析FRET双混合图像.
- 为了提高FRET双混合测定的效率,准确性和客观性.
- 通过解决药物向相互作用,建立FRET-SAM作为药物发现的工具.
主要方法:
- 开发了FRET-SAM,这是一个基于Segment Anything Model (SAM) 的优化分析方法.
- 适应SAM_Med2D用于自动化感兴趣区域 (ROI) 选择和FRET图像中的光信号提取.
- 经过训练和验证的FRET-SAM使用一个全面的FRET图像数据集与多个等离子体和FRET对.
主要成果:
- 与SAM_Med2D相比,FRET-SAM显示了更好的细分精度 (MPA,MIoU,Dice系数).
- FRET-SAM衍生结果与文献值保持高度一致 (相对误差<5%).
- 在药物查案例研究中,FRET-SAM显示出潜力,解决药物向相互作用.
结论:
- 通过自动化图像分析,FRET-SAM显著提高了FRET双混合分析的效率和准确性.
- 消除主观偏差可以提高蛋白相互作用量化的可靠性.
- 由于其能够解决药物向相互作用,FRET-SAM是药物发现的一个有前途的工具.
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