<br>从单体到六聚体：剖析细胞中蛋白质寡聚化的相邻密度方法的理论概率,Analytical Chemistry

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从单体到六聚体：剖析细胞中蛋白质寡聚化的相邻密度方法的理论概率
Analytical Chemistry ( IF 6.7 ) Pub Date : 2023-12-29 , DOI: 10.1021/acs.analchem.3c04728
Huanhuan Chen ₁ , Tai-Yen Chen ₁

Affiliation

破译细胞内蛋白质的寡聚状态对于了解它们在复杂细胞过程中的作用至关重要。随着单分子定位显微镜的最新进展，以前的工作已经利用蛋白质定位密度方法与模拟相结合，提取细胞中的膜蛋白寡聚状态，突出了此类技术的价值。然而，一种可以普遍应用于不同蛋白质（例如，膜和胞质蛋白）的综合理论方法仍然难以捉摸。在这里，我们介绍了邻域密度（PND）的理论概率，作为识别细胞环境中蛋白质寡聚状态的可靠工具。利用我们的方法，根据膜和胞质蛋白的模拟数据验证了理论 PND，与膜蛋白的实验基线一致。即使在调整蛋白质浓度或探索各种寡聚状态的蛋白质时，也能保持这种一致性。我们方法的优势不仅在于其精度，还在于其适应性，在不影响准确性的情况下适应不同的细胞蛋白质场景。理论 PND 的开发和验证有助于准确的蛋白质寡聚状态测定，并加强我们对蛋白质介导的细胞功能的理解。

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From Monomers to Hexamers: A Theoretical Probability of the Neighbor Density Approach to Dissect Protein Oligomerization in Cells

Deciphering the oligomeric state of proteins within cells is pivotal to understanding their role in intricate cellular processes. With the recent advances in single-molecule localization microscopy, previous efforts have harnessed protein location density approaches, coupled with simulations, to extract membrane protein oligomeric states in cells, highlighting the value of such techniques. However, a comprehensive theoretical approach that can be universally applied across different proteins (e.g., membrane and cytosolic proteins) remains elusive. Here, we introduce the theoretical probability of neighbor density (PND) as a robust tool to discern protein oligomeric states in cellular environments. Utilizing our approach, the theoretical PND was validated against simulated data for both membrane and cytosolic proteins, consistently aligning with experimental baselines for membrane proteins. This congruence was maintained even when adjusting for protein concentrations or exploring proteins of various oligomeric states. The strength of our method lies not only in its precision but also in its adaptability, accommodating diverse cellular protein scenarios without compromising the accuracy. The development and validation of the theoretical PND facilitate accurate protein oligomeric state determination and bolster our understanding of protein-mediated cellular functions.

更新日期：2023-12-29

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