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

Abstract

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.