bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.64898/2026.10.28.762497

Mass spectrometric detection of protein nanoclusters: the invisible intermediates of phase separation

Robb, C. G.·Haris, A.·Dao, T. P.·Castaneda, C. A.·Ujma, J.·Beveridge, R.
View original source ↗Export metadata ↓

Abstract

Phase separation of biomolecules, including intrinsically disordered proteins, is an underlying physical process behind cellular condensate formation. To increase mechanistic understanding of its initial stages, we demonstrate the application of charge detection mass spectrometry to characterise the mass dispersion of protein nanoclusters that represent intermediates in this important biophysical process. We detect polydisperse clusters of 1-280 protein molecules (64 kDa-18.4 MDa), that are currently invisible to most analytical techniques.

Explore related subjects

biophysicsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Robb, C. G., Haris, A., Dao, T. P., Castaneda, C. A., Ujma, J., Beveridge, R.. 2026-10-30. Mass spectrometric detection of protein nanoclusters: the invisible intermediates of phase separation. https://doi.org/10.64898/2026.10.28.762497

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Learning inhibitor interactions from protein deep mutational scanning data and deep learning models

Recent advances in protein language models, geometric deep learning, and structure prediction frameworks have the potential to accelerate computational drug discovery. However, it remains unclear how well these methods generalize beyond their training sets. Mutation-dependent drug responses present a challenge to these methods because there are often large effects from relatively small perturbations. Here, we systematically benchmark diverse mutational effect and drug-target affinity prediction models using deep mutational scanning datasets spanning 11 inhibitor conditions for the MET kinase domain. Across both mutational effect prediction and drug-target affinity tasks, current AI models produce largely ligand-invariant predictions despite experimentally distinct inhibitor-dependent fitness landscapes. Ligand-aware geometric models, including LigandMPNN and LASErMPNN, partially recover experimentally validated resistance-associated hotspots, highlighting the importance of explicitly modeling protein-ligand interactions and local structural flexibility. In contrast, deep learning affinity prediction models underestimate mutation-induced changes in binding affinity. This deficiency likely reflects mutation-invariant structural priors derived from co-folded structure prediction frameworks and the absence of mutation-conditioned supervision in current affinity datasets. Finally, we further extended our analysis to several kinase-inhibitor deep mutational scanning datasets and confirm the overall trends. Our findings suggest that a primary limitation in improving performance on this task lies in the scarcity of mutation-conditioned protein-ligand training data rather than model architecture alone.

biophysics↗
bioRxiv2026

FoxO1 and HNF1α cooperatively remodel nucleosome architecture and mechanical stability to activate transcription

How different transcription factors (TFs) cooperate to bind nucleosomal DNA targets remains unclear. Cryo-EM and force spectroscopy show that the pioneer factor FoxO1 binds superhelical location (SHL) +2 of nucleosomes assembled on the human IGFBP1 promoter, enhances distal DNA unwrapping, destabilizes the histone core, and reduces nucleosome mechanical stability. TF HNF1 binds SHL +5/+7 as a dimer, induces extensive DNA unwrapping without perturbing the histone core, and reshapes nucleosome disassembly dynamics. FoxO1 enhances HNF1 recruitment, and together they stabilize the nucleosome during its mechanically-induced disassembly trajectories. In-cell nucleosome footprinting on active promoters shows that FoxO1 binding enhances unwrapped nucleosomes and subnucleosomes presence, while HNF1 increases stabilized and breathing nucleosomes and reduces the subnucleosome population. Combined, they display an intermediate nucleosome phenotype. Our data leads to a model where FoxO1 destabilizes the nucleosome and enhances HNF1 recruitment, and together they remodel nucleosome architecture both in vitro and in native chromatin.

biophysics↗
bioRxiv2026

Particle picking and centering in cryo-electron micrographs via a vanishing radial kernel

A training-free procedure combines particle picking and approximate centering in cryo-electron microscopy through a single convolution of the micrograph with a compact radial kernel, whose weights increase with distance from the origin, saturate in an annulus, and vanish beyond it. Particles are darker than their surroundings and much weaker than the noise; each carves a well in the response, and local minima of the response are candidate centers. Constant background offsets leave these extrema unchanged away from image boundaries, and a local stability bound controls the displacement of a well's minimum under signed perturbations without assuming a symmetric particle. Grid seeds are iteratively updated, nearby endpoints are merged, and two-dimensional classification follows. On the \emph{Plasmodium falciparum} 80S ribosome and $\beta$-galactosidase data sets, reconstructions reach 3.68 and 4.30~\AA{}, compared with 3.77 and 4.36~\AA{} using reference coordinates, with picking at 0.20--0.26~s per micrograph.

biophysics↗