Truscott, H. H. | Search bioRxiv

bioRxiv2026

Actin-membrane interface stress regulates Arp2/3-branched actin density during lamellipodial protrusion

Motile cells can sense and exert forces on the extracellular environment through dynamic actin networks. Increased stress against the polymerizing barbed ends of branched actin networks has been shown to lead to an increase in the density of these networks through a force feedback mechanism, though this phenomenon has not been explored through the examination of real-time responses of endogenous branched actin in cells. Here, we utilize mouse CRISPR knock-in fibroblasts with labeled Arp2/3 complex to identify cellular and extracellular conditions that regulate branched actin density and enrichment at the leading edge of lamellipodial protrusions. A common theme shared among all branched actin density-increasing conditions is higher levels of predicted interface stress between the plasma membrane and the barbed ends of the lamellipodial actin network. Among these conditions, we find that Arp2/3 is specifically required for robust spreading and protrusion in response to increased extracellular viscosity. Interestingly, time-lapse traction force microscopy of Arp2/3-dependent viscosity responses show significantly reduced changes in strain energy applied to the substrate when compared to spreading and motility utilizing cell-matrix adhesion. In addition, we find that increased extracellular viscosity can bypass the need for extracellular matrix proteins to support lamellipodial protrusion driven by optogenetic Rac activation. Our studies provide strong support for in vitro models of branched actin force feedback responses and further characterize an essential role for branched actin in mediating dramatic cell shape changes in response to increased extracellular viscosity.

cell biology↗

bioRxiv2025

Can We Extract Physics-like Energies from Generative Protein Diffusion Models?

Diffusion models have emerged as the state-of-the-art method in generative artificial intelligence (AI) and have shown great success in image synthesis, video generation, molecular design, and protein structure prediction. For biophysical problems, such as protein folding and association, a fundamental question in diffusion-based methods is how their learned functions correspond to thermodynamics. In this paper, we study diffusion models through the lens of theoretical biophysics, analyzing their underlying formulation of potentials and exploring their applications in scoring protein interactions. We develop simple theories rooted in statistical physics that relate thermodynamic potentials to the negative log of the probability of observing a system in a particular state. We include dimensional analysis of diffusion model equations and a table mapping AI and physics jargon. We then test a diffusion models ability to capture learned energies as negative log-likelihood values, -log p0(x0), by integrating over the diffusion-generated path or a probability flow path. We test these integrals on a simple 1D Gaussian mixture diffusion model and a protein-docking diffusion model, DFMDock. In the 1D case, we find that integration over both diffusion and flow paths can accurately recover ground truth probabilities. When we extract the learned docking energies for cases where DFMDock succeeds, we observe energy funnels with the minimum energy near the experimental docked structure, like those we observe with Rosetta, an empirically tuned physics-based biomolecular modeling suite. The learned energy performs comparably or outperforms Rosetta interface energy in 9 out of 25 cases at ranking the correctness of docked poses. These data show that we can extract a relevant learned energy function from a diffusion model and compare it to physical energy functions.

biophysics↗