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Statistical Correction of IDP Oligomer Valency by Iterative Synthetic Reconstruction of EM Micrographs

Biophysical Journal(2021)

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Abstract
Multivalent intrinsically disordered protein (IDP) complexes control many diverse cellular functions, such as tuning levels of transcription, coordination of cell-signaling events and regulating the assembly/disassembly of complex macromolecular architectures. These systems are difficult to characterize structurally, due to inherent conformational and compositional heterogeneity. Although electron microscopy (EM) is a powerful tool for visualizing multivalent complexes, the IDPs themselves are “invisible” by EM, which poses challenges to traditional methods of image analysis and structural interpretation. Here, we present an automated pipeline for the identification of multivalent IDPs bound to ∼20 kDa cross-linking LC8 proteins, which can statistically correct spurious observations arising from random proximity of bound and unbound LC8 ligands. Our analysis first provides a ‘direct’ count of putatively assembled IDP-LC8 oligomers, which are distinguished from unbound LC8 particles based on a straightforward particle clustering and geometric scoring scheme. However, this step is necessarily prone to error because of the prevalence of dissociated LC8 particles and because IDPs are not directly observed. Therefore, a statistical correction procedure was developed which synthetically replicates the EM micrography by ‘re-scattering’ the assigned free LC8 proteins, and oligomers are re-classified for comparison with the original assignments. This procedure provides a statistical likelihood that a free LC8 artificially lengthens an oligomer assignment by random proximity, and the valency of the putative true-positive oligomer assignments are adjusted accordingly. The process is iterated until self-consistency is achieved with the original direct counts. Our results demonstrate that the automated analysis identifies a heterogeneous population distribution of oligomeric species that are consistent with manually analyzed data. Furthermore, the correction procedure provides a self-consistent picture of the underlying species distribution, not directly apparent from naïve analysis of the image dataset.
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