bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2024.01.10.574984

Photoswitchable endocytosis of biomolecular condensates in giant vesicles

Mangiarotti, A.·Aleksanyan, M.·Siri, M.·Lipowsky, R.·Dimova, R.
View original source ↗Export metadata ↓

Abstract

Interactions between membranes and biomolecular condensates can give rise to complex phenomena such as wetting transitions, mutual remodeling, and endocytosis. In this study, we demonstrate a light-triggered manipulation of condensate engulfment using giant vesicles containing photoswitchable lipids. UV irradiation increases the membrane area, facilitating a rapid condensate endocytosis, which can be reverted by blue light. The affinity of the protein-rich condensates to the membrane and the reversibility of the engulfment processes is quantified from confocal microscopy images. The degree of engulfment, whether partial or complete, depends on the initial membrane excess area and the relative sizes of vesicles and condensates. Theoretical estimates suggest that utilizing the light-induced excess area to increase the vesicles-condensate adhesion interface is energetically more favorable than the energy gain from folding the membrane into invaginations and tubes. Our overall findings demonstrate that membrane-condensate interactions can be easily and quickly modulated via light, providing a versatile system for building platforms to control cellular events and design intelligent drug delivery systems for cell repair.

Source connections

Explore related subjects

biophysicsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Mangiarotti, A., Aleksanyan, M., Siri, M., Lipowsky, R., Dimova, R.. 2024-01-11. Photoswitchable endocytosis of biomolecular condensates in giant vesicles. https://doi.org/10.1101/2024.01.10.574984

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

KEEP EXPLORING

Related preprints

bioRxiv2026

Mechanism of molecular recognition revealed through dynamic drug binding pathways to SARS-CoV-2 main protease

Characterization of drug-binding pathways remains experimentally limited by transient intermediates and computationally challenging due to long timescales intractable for conventional molecular dynamics. To address these challenges, we combined solution NMR titrations with weighted ensemble (WE) enhanced sampling simulations to resolve atomistic pathways of nirmatrelvir binding to the SARS-CoV-2 main protease. NMR titration revealed residue-dependent heterogeneity spanning fast, intermediate, and slow exchange regimes. WE simulations complement the NMR by providing insights into unassigned residues and adding time-resolved and three-dimensional structural context. We map key interactions along two distinct binding pathways, provide dynamic explanations for residues involved in resistance, and capture unique backbone conformations compared to those sampled in unbound or bound states. Our comprehensive binding model is consistent with a combined conformational selection and induced fit mechanism in which early transient contacts are made with residues E47 and L50 and allosteric motions are centered around residue V204 of the distal domain. This synergistic application of WE and titration NMR enables a more comprehensive characterization of drug binding than either method alone, providing an integrated framework that may have broader applicability to defining structure-kinetic relationships and guiding design of next-generation inhibitors.

biophysics↗
bioRxiv2026

Discriminating betacoronavirus receptor usage across subgenera using protein structure prediction and molecular dynamics

A critical step in the emergence of a virus is the ability of the viral protein to bind a host receptor and mediate cell entry. For many coronaviruses, this interaction occurs between the Spike S1 subunit and the human ACE2 receptor. Whether this binding interface can be computationally distinguished across unstudied viruses without experimentally resolved protein structures remains an open question. We predicted how 28 emerging coronaviruses may bind to human ACE2 using structural predictions, static interaction prediction programs, and molecular dynamics simulations. To screen the emerging coronaviruses, we predicted a library of S1 structures using AlphaFold. These predicted structures were then used to model the S1-ACE2 interaction with AlphaFold, ClusPro, and HADDOCK. We used known ACE2-binding sarbecoviruses as positive controls and coronaviruses that bind other receptors as negative controls to threshold predicted binding. Contact analysis quantified the predicted binding and revealed that these static interaction prediction methods varied in discriminative power. Less restrained static predictions separated binders from non-binders, whereas heavily restrained docking did not, potentially forcing an interaction where none should exist. This analysis highlighted an emerging coronavirus, Zhejiang2013, as a potential ACE2 binder. We used molecular dynamics simulations to further assess the static predictions and model the interaction over time. Overall, our results indicate that Zhejiang2013 exhibits dynamic interaction patterns consistent with ACE2 binding. Given that two ACE2-binding coronaviruses have caused global pandemics within the past two decades, identifying potential ACE2 binders is critical for early warning and pandemic preparedness.

biophysics↗
bioRxiv2026

De novo design of flexible protein interactions with GuideFlip

De novo design of protein binders requires a target structure. However, for flexible targets, such as intrinsically disordered proteins, this structure does not exist until the binder has stabilized the interaction. Such targets are therefore difficult for methods that separate structure generation from sequence design. We introduce GuideFlip, which co-designs structure and sequence through guided discrete flow matching: binder residues are assigned progressively while the complex is re-predicted at each step, allowing the evolving interface to affect the design process. GuideFlip reduces the hydrophobic bias of direct AlphaFold optimization and improves in silico success rates over existing approaches. We release a database of binder candidates for 177 human disordered proteins. Experimentally, we obtain de novo binders to the C-terminus of -synuclein and the disordered amino terminus of RBX1 with hit rates of 13.5% and 41.7%, respectively, and we confirm the epitopes of selected binders by NMR and mutagenesis. Applying GuideFlip to flexibility on the binder side, we design a nanobody that binds the agonist-bound {beta}1-adrenergic receptor in the active state, but not the receptor in its inactive state, with a 75% hit rate and cryo-EM structure confirming the design. GuideFlip enables protein design where bound structures emerge only upon binding.

biophysics↗