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.