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bioRxiv2025

CASPULE: A computational tool to study sticker spacer polymer condensates

Phase separated condensates are recognized as a ubiquitous mechanism of spatial organization in cell biology. Biophysical modeling of condensates provides critical insights into the dynamics and functions of these subcellular structures that are difficult to extract via experiments. Here we present an efficient computational pipeline, CASPULE (Condensate Analysis of Sticker Spacer Polymers Using the LAMMPS Engine), to simulate and analyze the biological condensates made of sticker-spacer polymers. CASPULE implements a unique force field that combines traditional Langevin dynamics with a "detailed-balance proof" protocol for single-valent bond formation between stickers. This framework allows us to study the nontrivial biophysics that emerge from singlevalent sticker interactions, coupled with the effect of separating the energetic contributions of stickers and spacers. We provide detailed documentation on how to setup the simulation environment, perform simulations and analyze the results. Through case studies, we highlight the utility and efficacy of our pipeline. Importantly, we provide statistical parameters to characterize the cluster size distribution often observed in biological systems. We envision this tool to be broadly useful in decoding the interplay of kinetics and thermodynamics underlying the formation and function of biological condensates. Code and documentation: https://caspule.github.io/caspule/ Author SummaryLiving cells contain droplet-like compartments called biomolecular condensates that organize important molecules and reactions without a surrounding membrane. Understanding how these condensates form and behave helps us learn more about healthy cellular function and diseases. Condensate forming biopolymers (proteins and nucleic acids) often contain a sticker spacer architecture that gives rise to interesting emergent properties when they assemble into a condensate. Here we report an easy-to-use computational pipeline, called CASPULE, that allows us to simulate and analyze the condensation of polymer chains. Unlike previous tools, CASPULE models both the physical motion of these molecules and specific bonding interactions between sticker segments. This user-friendly tool will enable researchers to explore the governing principles underlying the formation and function of biomolecular condensates.

biophysics↗

bioRxiv2025

Optimal bioelectric control accelerates collective wound healing

Collective cell migration is critical in a range of biophysical processes spanning wound healing to tumor metastasis. It is therefore important to develop techniques for regulating cell motion, but while we have developed powerful migration tools from optogenetics and bioelectricity, the complex mechanics of collective systems make it difficult to determine where and when to apply these stimuli. For example, here we begin with a circular sheet of skin cells with a central hole, or "wound", and show that globally stimulating all cells to migrate radially inwards to close the gap through electrotaxis causes catastrophic mechanical damage, emerging from strong cell-cell adhesion, which we explain with an active elasticity model. We propose a solution inspired by sheepherding based on using local stimulation to learn the collective perturbation-response of the group and then drive tissue motion. First, we induce local electrotaxis to characterize the impulse-response function of quasi-1D strips of skin tissue, discovering that hyper-local stimulation triggers long-range tissue response over a length scale set by cell-cell adhesion also predicted by our model. Based on this, we apply local, concentric ring fields to in vitro circular wounds. Continuously driving cells towards the wound core kinetically traps the tissue in a jammed state and freezes healing. Pulsing the stimuli allows the tissue to relax and fluidize again, accelerating migration. Finally, we integrate the key length and timescales of tissue mechanics into a biophysically-informed continuum control model. The models predictive framework helps determine where and when to apply stimulation for optimal tissue growth which, when tested, accelerates healing 5-fold.

biophysics↗

bioRxiv2025

Deep-brain thermo-endomicroscopy

Subtle deviations in biophysical parameters, particularly temperature, within deep-brain regions exert substantial effects on whole-body homeostasis and metabolism. However, the underlying mechanisms remain poorly understood because cellular-resolution measurements of these parameters in the deep brain have been technically inaccessible. Here, we present a lensless fiber-optic quantum-sensing endomicroscopy that enables cellular-resolution temperature mapping in the mouse deep brain. A microwire-coupled, lensless fiber bundle permits simultaneous temperature readout from optically detected magnetic resonance spectra of >50 fluorescent nanodiamonds, achieving cellular resolution (4.88 m) with a precision of 0.49 (minimum precision, 0.18 ) within a 0.28 mm2 field of view. We demonstrated temperature mapping of multiple brain depths in head-fixed and vigorously behaving mice subjected to nociceptive stimulation, detecting lower temperatures during anesthesia compared to the awake state in ventromedial hypothalamic nucleus (VMH) at a 5 mm depth. These mappings reveal that spatio-temporal temperature distribution and its anesthesia-dependent reduction are heterogeneous rather than uniform in VMH. This endomicroscopy provides a promising platform to dissect how deviations in biophysical parameters modulate deep-brain function.

biophysics↗

bioRxiv2025

Extracellular Stimulation and Ephaptic Coupling of Neurons in a Fully Coupled Finite Element-Based Extracellular-Membrane-Intracellular (EMI) Model

The extracellular potential surrounding neurons is of great importance: it is measured to interpret neural activity, it underpins ephaptic coupling between neighboring cells, and it forms the basis for external stimulation of neural tissue. These phenomena have been studied for decades, both experimentally and computationally. In computational models, variants of the classical cable equation for membrane dynamics and an electrostatic equation for the extracellular field are the most common approaches. Such formulations, however, typically decouple the governing equations and therefore neglect the biophysical coupling between the extracellular (E) space, the cell membrane (M), and the intracellular (I) space. Here, we use a finite element-based Extracellular-Membrane-Intracellular (EMI) approach that solves a fully coupled system of equations to study extracellular stimulation and ephaptic coupling of cerebellar Purkinje neurons and neocortical layer 5 pyramidal neurons. These two archetypes differ substantially in morphology, ion-channel distribution, and firing behavior, and together span a range of neuronal properties. Specifically, we assess responses to extracellular stimulation while varying the distance to the stimulation source, the amplitude, and the frequency of the external current. We also investigate ephaptic interactions between neurons, and examine how the firing pattern of one neuron can be affected by the firing pattern of a neighboring neuron, how the rate of synchronization between neighboring neurons depends on cell distance and extracellular conductivity, and finally whether a neuron can directly trigger excitation in a neighboring neuron through ephaptic coupling. The results provide quantitative insight into extracellular field-mediated neural coupling and how externally applied fields, such as those used in deep brain stimulation, interact with single-neuron biophysics.

biophysics↗

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↗

bioRxiv2018

Solubilization kinetics determines the pulsatory dynamics of lipid vesicles exposed to surfactant

We establish a biophysical model for the dynamics of lipid vesicles exposed to surfactants. The solubilization of the lipid membrane due to the insertion of surfactant molecules induces a reduction of membrane surface area at almost constant vesicle volume. This results in a rate-dependent increase of membrane tension and leads to the opening of a micron-sized pore. We show that solubilization kinetics due to surfactants can determine the regimes of pore dynamics: either the pores open and reseal within a second (short-lived pore), or the pore stays open up to a few minutes (long-lived pore). First, we validate our model with previously published experimental measurements of pore dynamics. Then, we investigate how the solubilization kinetics and membrane properties affect the dynamics of the pore and construct a phase diagram for short and long-lived pores. Finally, we examine the dynamics of sequential pore openings and show that cyclic short-lived pores occur at a period inversely proportional to the solubilization rate. By deriving a theoretical expression for the cycle period, we provide an analytic tool to measure the solubilization rate of lipid vesicles by surfactants. Our findings shed light on some fundamental biophysical mechanisms that allow simple cell-like structures to sustain their integrity against environmental stresses, and have the potential to aid the design of vesicle-based drug delivery systems.

biophysics↗

bioRxiv2017

Statistical methods for large ensemble of super-resolution stochastic single particle trajectories

Following recent progresses in super-resolution microscopy obtained in the last decade, massive amount of redundant single stochastic trajectories are now available for statistical analysis. Flows of trajectories of molecules or proteins are sampling the cell membrane or its interior at a very high time and space resolution. Several statistical analysis were developed to extract information contained in these data, such as the biophysical parameters of the underlying stochastic motion to reveal the cellular organization. These trajectories can further reveal hidden subcellular organization. We present here the statistical analysis of these trajectories based on the classical Langevin equation, which serves as a model of trajectories. Parametric and non-parametric estimators are constructed by discretizing the stochastic equations and they allow recovering tethering forces, diffusion tensor or membrane organization from measured trajectories, that differ from physical ones by a localization noise. Modeling, data analysis and automatic detection algorithms serve extracting novel biophysical features such as potential wells and other sub-structures, such as rings at an unprecedented spatiotem-poral resolution. It is also possible to reconstruct the surface membrane of a biological cell from the statistics of projected random trajectories.

biophysics↗

bioRxiv2017

Cell size regulation through tunable geometric localization of the bacterial actin cytoskeleton

In the rod-shaped bacterium Escherichia coli, the actin-like protein MreB localizes in a curvature-dependent manner and spatially coordinates cell-wall insertion to maintain cell shape across changing environments, although the molecular mechanism by which cell width is regulated remains unknown. Here, we demonstrate that the bitopic membrane protein RodZ regulates the biophysical properties of MreB and alters the spatial organization of E. coli cell-wall growth. The relative expression levels of MreB and RodZ changed in a manner commensurate with variations in growth rate and cell width. We carried out single-cell analyses to determine that RodZ systematically alters the curvature-based localization of MreB and cell width in a manner dependent on the concentration of RodZ. Finally, we identified MreB mutants that we predict using molecular dynamics simulations to alter the bending properties of MreB filaments at the molecular scale similar to RodZ binding, and showed that these mutants rescued rod-like shape in the absence of RodZ alone or in combination with wild-type MreB. Together, our results show that E. coli controls its shape and dimensions by differentially regulating RodZ and MreB to alter the patterning of cell-wall insertion, highlighting the rich regulatory landscape of cytoskeletal molecular biophysics.

biophysics↗

bioRxiv2017

Repertoire analysis of antibody CDR-H3 loops suggests affinity maturation does not typically result in rigidification

Antibodies can rapidly evolve in specific response to antigens. Affinity maturation drives this evolution through cycles of mutation and selection leading to enhanced antibody specificity and affinity. Elucidating the biophysical mechanisms that underlie affinity maturation is fundamental to understanding B-cell immunity. An emergent hypothesis is that affinity maturation reduces the conformational flexibility of the antibodys antigen-binding paratope to minimize entropic losses incurred upon binding. In recent years, computational and experimental approaches have tested this hypothesis on a small number of antibodies, often observing a decrease in the flexibility of the Complementarity Determining Region (CDR) loops that typically comprise the paratope and in particular the CDR-H3 loop, which contributes a plurality of antigen contacts. However, there were a few exceptions, and previous studies were limited to a small handful of cases. Here, we determined the structural flexibility of the CDR-H3 loop for thousands of recently-determined homology models of the human peripheral blood cell antibody repertoire using rigidity theory. We found no clear delineation in the flexibility of naive and antigen-experienced antibodies. To account for possible sources of error, we additionally analyzed hundreds of human and mouse antibodies in the Protein Data Bank through both rigidity theory and B-factor analysis. By both metrics, we observed only a slight decrease in the CDR-H3 loop flexibility when comparing affinity-matured antibodies to naive antibodies, and the decrease was not as drastic as previously reported. Further analysis, incorporating molecular dynamics (MD) simulations, revealed a spectrum of changes in flexibility. Our results suggest that rigidification may be just one of many biophysical mechanisms for increasing affinity.

biophysics↗

bioRxiv2018

A First-principles Approach to Large-scale Nuclear Architecture

Model approaches to nuclear architecture have traditionally ignored the biophysical consequences of ATP-fueled active processes acting on chromatin. However, transcription-coupled activity is a source of stochastic forces that are substantially larger than the Brownian forces present at physiological temperatures. Here, we describe a first-principles approach to large-scale nuclear architecture in metazoans that incorporates cell-type-specific active processes. The model predicts the statistics of positional distributions, shapes and overlaps of each chromosome. Our simulations reproduce common organising principles underlying large-scale nuclear architecture across human cell nuclei in interphase. These include the differential positioning of euchromatin and heterochromatin, the territorial organisation of chromosomes including both gene-density-based and size-based chromosome radial positioning schemes, the non-random locations of chromosome territories and the shape statistics of individual chromosomes. We propose that the biophysical consequences of the distribution of transcriptional activity across chromosomes should be central to any chromosome positioning code.

biophysics↗

bioRxiv2018

Native tau structure is disrupted by disease-associated mutations that promote aggregation

Tauopathies are neurodegenerative diseases characterized by intracellular amyloid deposits of tau protein. Missense mutations in the tau gene (MAPT) correlate with aggregation propensity and cause dominantly inherited tauopathies, but their biophysical mechanism driving amyloid formation is poorly understood. Many disease-associated mutations localize within taus repeat domain at inter-repeat interfaces proximal to amyloidogenic sequences, such as 306VQIVYK311. Using cross-linking mass spectrometry, intramolecular FRET, recombinant protein and synthetic peptide systems, in silico modeling, and cell models, we conclude that the aggregation prone 306VQIVYK311 motif forms metastable compact structures with the upstream sequence that modulates aggregation propensity. Disease-associated mutations, isomerization of a critical proline, or alternative splicing are all sufficient to destabilize this local structure and trigger spontaneous aggregation. These findings provide a biophysical framework to explain the basis of early conformational changes that may underlie genetic and sporadic tau pathogenesis.

biophysics↗

bioRxiv2018

Deep clustering of protein folding simulations

We examine the problem of clustering biomolecular simulations using deep learning techniques. Since biomolecular simulation datasets are inherently high dimensional, it is often necessary to build low dimensional representations that can be used to extract quantitative insights into the atomistic mechanisms that underlie complex biological processes. In this paper, we use a convolutional variational autoencoder (CVAE) to learn low dimensional, biophysically relevant latent features from long time-scale protein folding simulations in an unsupervised manner. We demonstrate our approach on three model protein folding systems, namely the Fs-peptide (14 s aggregate sampling), villin head piece (single trajectory of 125 s) and the mixed {beta}-{beta}- (BBA) protein (223 + 102 s sampling across two independent trajectories). In these systems, we show that the CVAE latent features learned correspond to distinct conformational substates along the protein folding pathways. The CVAE model predicts nearly 89% of all contacts within the folding trajectories correctly, while being able to extract folded, unfolded and potentially misfolded states in an unsupervised manner. Further, the CVAE model can be used to learn latent features of protein folding that can be applied to other independent trajectories, making it particularly attractive for identifying intrinsic features that correspond to conformational substates that share similar structural features. Together, we show that the CVAE model can quantitatively describe complex biophysical processes such as protein folding.

biophysics↗

bioRxiv2018

Conditions for optimal mechanical power generation by a bundle of growing actin filaments

Bundles of actin filaments are central to a large variety of cellular structures, such as filopodia, stress fibers, cytokinetic rings or focal adhesions. The mechanical properties of these bundles are critical for proper force transmission and force bearing. Previous mathematical modeling efforts have focused on bundles rigidity and shape. However, it remains unknown how bundle length and thickness are controlled by external physical factors, and how the attachment of the bundle to a load affects its ability to transmit forces. In this paper, we present a biophysical model for dynamic bundles of actin filaments that takes into account individual filaments, their interaction with each other and with an external load. In combination with in vitro motility assays of beads coated with formins, our model allowed us to characterize conditions for bead movement and bundle buckling. From the deformation profiles, we determined key biophysical properties of tethered actin bundles, such as their rigidity and filament density. Our model also demonstrated that filaments undulate under lateral constraints applied by external forces or by neighboring filaments of the bundle. Last, our model allowed us to identify optimum conditions in filament density and barbed end tethering to the load for a maximal yield of mechanical power by a dynamic actin bundle.

biophysics↗

bioRxiv2018

Tv2483, a lipoprotein from Treponema vincentii and homolog of Tp0309 from T. pallidum, is ahighly selective L-arginine-binding protein

Biophysical and biochemical studies on the lipoproteins and other periplasmic proteins from the spirochetal species Treponema pallidum have yielded numerous insights into the functioning of the organisms peculiar membrane organization, its nutritional requirements, and intermediary metabolism. However, not all T. pallidum proteins have proven to be amenable to biophysical studies. One such recalcitrant protein is Tp0309, a putative polar-amino-acid-binding protein of an ABC transporter system. To gain further information on its possible function, a homolog of the protein from the related species T. vincentii was used as a surrogate. This protein, Tv2483, was crystallized, resulting in the determination of its crystal structure at a resolution of 1.75 [A]. The protein has a typical fold for a ligand-binding protein, and a single molecule of L-arginine was bound between its two lobes. Differential scanning fluorimetry and isothermal titration calorimetry experiments confirmed that L-arginine bound to the protein with unusually high selectivity. However, further comparison to Tp0309 showed differences in key amino-acid-binding residues may impart an alternate specificity for the T. pallidum protein.

biophysics↗

bioRxiv2018

Large Fatty Acid-derived Aβ42 oligomers Form Ring-like Assemblies

As the primary toxic species in the etiology of Alzheimer disease (AD) are low molecular weight oligomers of A{beta}, it is crucial to understand the structure of A{beta} oligomers for gaining molecular insights into AD pathology. We have earlier demonstrated that in the presence of fatty acids A{beta}42 peptides assemble as 12-24mer oligomers. These Large Fatty Acid-derived Oligomers (LFAOs) exist predominantly as 12mers at low, and as 24mers at high concentrations. The 12mers are more neurotoxic than the 24mers and undergo self-replication, while the latter propagate to morphologically distinct fibrils with succinct pathological consequences. In order to glean into their functional differences and similarities, we have determined their structures in greater detail by combining molecular dynamic simulations with biophysical measurements. We conjecture that the LFAO are made of A{beta} units in an S-shaped conformation, with the 12mers forming a double-layered hexamer ring (6 x 2) while the structure of 24mers is a double-layered dodecamer ring (12 x 2). A closer inspection of the (6 x 2) and (12 x 2) structures reveals a concentration and pH dependent molecular reorganization in the assembly of 12 to 24mers, that seems to be the underlying mechanism for the observed biophysical and cellular properties of LFAOs.

biophysics↗

bioRxiv2018

Phasor histone FLIM-FRET microscopy quantifies spatiotemporal rearrangement of chromatin architecture during the DNA damage response.

To investigate how chromatin architecture is spatiotemporally organised at a double strand break (DSB) repair locus, we established a biophysical method to quantify chromatin compaction at the nucleosome level during the DNA damage response (DDR). The method is based on phasor image correlation spectroscopy (ICS) of histone FLIM-FRET microscopy data acquired in live cells co-expressing H2B-eGFP and H2B-mCherry. This multiplexed approach generates spatiotemporal maps of nuclear-wide chromatin compaction that when coupled with laser micro-irradiation induced DSBs, quantify the size, stability, and spacing between compact chromatin foci throughout the DDR. Using this technology, we identify that ATM and RNF8 regulate rapid chromatin decompaction at DSBs and formation of a compact chromatin ring surrounding the repair locus. This chromatin architecture serves to demarcate the repair locus from the surrounding nuclear environment and modulate 53BP1 mobility.\n\nSIGNIFICANCE STATEMENTChromatin dynamics play a central role in the DNA damage response (DDR). A long-standing obstacle in the DDR field was the lack of technology capable of visualising chromatin dynamics at double strand break (DSB) sites. Here we describe novel biophysical methods that quantify spatiotemporal chromatin compaction dynamics in living cells. Using these novel tools, we identify how chromatin architecture is reorganised at a DSB locus to enable repair factor access and demarcate the lesion from the surrounding nuclear environment. Further, we identify novel regulatory roles for key DDR enzymes in this process. Finally, we demonstrate method utility with physical, pharmacological and genetic manipulation of the chromatin environment, identifying method potential for use in future studies of chromatin biology.

biophysics↗

bioRxiv2018

A novel lipid-polymer system with unique properties has potential in drug delivery and biotechnology applications

Bicelles have been demonstrated to be a valuable tool for studying membrane protein interactions and structure in vitro. They are distinguished by a distinct lipid bilayer that mimics the plasma membrane of cells making it more native-like than its detergent micelle counter-part. Bicelles are typically comprised of a long-chain phospholipid such as dimyristoylphosphatidylcholine (DMPC) and a short-chain phospholipid such as dihexanoylphosphatidylcholine (DHPC). When mixed together in solution DMPC-DHPC bicelles assume a discoidal structure comprised of a heterogeneous arrangement where the short-chain lipids gather around the rim of the disk and the long-chain lipids form the flat, planar, bilayer region. In this study, the nonionic surfactant, C8E5, was used to prepare mixtures with DMPC to determine if it adopts properties similar to bicelles with a q [≥] 0.5. At q [≥] 0.5, DMPC-DHPC bicelles are bilayered and DMPC is sequestered from the detergent micelle-like DHPC. Mixtures of DMPC and C8E5 were prepared at various q values, a parameter used to describe the mole ratio of DMPC to DHPC in the preparation of bicelles. Employing biophysical methods like dynamic light scattering, 31P-NMR and analytical ultracentrifugation, properties of these lipid-detergent complexes are described. Interestingly they adopted a spherical-shaped micellar structure morphology and did not assume a discoidal shape typical of bicelles at q [≥] 0.5. However, they appear to retain bilayer-like properties that may prove beneficial for in vitro biophysical studies of membrane proteins.

biophysics↗

bioRxiv2018

Transduction of the Geomagnetic Field as Evidenced from Alpha-band Activity in the Human Brain

Magnetoreception, the perception of the geomagnetic field, is a sensory modality well-established across all major groups of vertebrates and some invertebrates, but its presence in humans has been tested rarely, yielding inconclusive results. We report here a strong, specific human brain response to ecologically-relevant rotations of Earth-strength magnetic fields. Following geomagnetic stimulation, a drop in amplitude of EEG alpha oscillations (8-13 Hz) occurred in a repeatable manner. Termed alpha event-related desynchronization (alpha-ERD), such a response is associated with sensory and cognitive processing of external stimuli. Biophysical tests showed that the neural response was sensitive to the dynamic components and axial alignment of the field but also to the static components and polarity of the field. This pattern of results implicates ferromagnetism as the biophysical basis for the sensory transduction and provides a basis to start the behavioral exploration of human magnetoreception.

biophysics↗