Membrane fusion is central to biological processes such as viral entry, fertilization and cell-to-cell fusion. Gaining a mechanistic understanding of fusion requires the ability to visualize and quantify the dynamic interaction between two membranes and their associated protein machineries at high temporal and spatial resolution. However, studying these processes in live cells remains challenging due to the complexity of the cellular environment. Here we demonstrate a versatile cell-free platform based on giant plasma membrane vesicles (GPMVs) that enables controlled, quantitative analysis of receptor binding and membrane fusion kinetics in a native membrane context. As proof of concept, we reconstitute the SARS-CoV-1 Spike-ACE2 interaction, capturing specific receptor engagement and accumulation at the membrane interface using confocal microscopy and micropipette aspiration. Fusion was induced by proteolytic activation and quantified using both high-resolution microscopy and high-throughput Imaging Flow Cytometry. The platform also reveals the influence of membrane composition on fusion efficiency, demonstrated by the impact of cholesterol depletion. This approach provides a broadly applicable system for dissecting membrane fusion and protein-protein interactions across membranes, with compatibility for biophysical, imaging and structural analysis. It offers new opportunities for mechanistic studies and inhibitor screening in a biologically relevant yet experimentally accessible context.

Biomolecular condensates are membraneless intracellular structures formed from the phase separation of proteins and nucleic acids. Although biomolecular condensates do not have a phospholipid membrane at their surface, recent studies reveal conspicuous assemblies of proteins at the interface between the dense and dilute phases of nucleoli, P granules, and other condensates. The molecular and biophysical rules that govern the surfactant-like localization of these proteins are only beginning to be understood. Here, we created de novo nucleolar surface proteins (NoLSurfers) by fusing diverse synthetic nucleoli-philic and nucleoli-phobic protein segments. By quantitatively analyzing the spatial distribution of different combinations of nucleoli-philic and nucleoli-phobic protein segments, we find that the nucleolar surface localization of NoLSurfers is largely determined by the oligomerization state of nucleoli-philic proteins and their immiscibility with the underlying condensate structure. While nucleoli-philic proteins with low oligomerization are miscible in the dense phase of nucleoli, nucleoli-philic proteins with high oligomerization become immiscible and show surface localization, and in some cases form de novo condensates in the nucleoplasm. These engineered NoLSurfers are useful as a tool to both elucidate central aspects of the biophysics of condensate interfaces and to potentially modulate condensate properties and function.

Organisms frequently encounter abiotic stresses such as drought, salinity, and extreme temperatures, requiring sophisticated adaptive mechanisms. Stress memory enables them to respond more efficiently to repeated environmental challenges by retaining information from prior exposures. Biomolecular condensates, dynamic, membrane-less cellular assemblies formed by liquid-liquid phase separation, have emerged as crucial regulators of post-transcriptional gene expression, particularly in stress conditions. These condensates modulate RNA fate and translational repression by selectively storing and organizing key molecules in ways that may contribute to cellular memory mechanisms. Here, we explore the biophysical principles underpinning condensate formation and dynamics, with a focus on processing bodies (PBs) as potential cellular memory storage systems. We propose a framework for how PBs might integrate biochemical and biophysical signals to encode, maintain, and retrieve stress-responsive information, and discuss the evidence supporting their role in coordinated stress responses and adaptive resilience in plants.

Magnetotactic bacteria (MTB) utilize magnetosomes to navigate along magnetic fields and employ photosensitive proteins to respond to light. However, whether a synergistic mechanism exists between phototaxis and magnetotaxis remains unclear. To investigate the cooperative roles of the magnetosensitive protein Amb0994 and the photosensitive protein Amb2291 under magnetic reversal and coupled magneto-optical conditions, we conducted a comparative analysis of Magnetospirillum magneticum AMB-1 wild-type (WT) and mutant strains (Δamb0994, Δamb2291, Δamb0994Δamb2291). Experimental results demonstrated that WT strains exhibited consistent magnetotaxis responses under both illuminated and control (no added light) conditions, suggesting balanced signal integration via protein synergy. The Δamb0994 mutant exhibited accelerated magnetotaxis responses, indicating that Amb0994 normally function to moderate magnetic signal processing. In contrast, the Δamb2291 mutant showed prolonged reversal times and lacked directional selectivity post-illumination, implying Amb2291 functions as a "photo-directionality sensor". The double mutant displayed the slowest responses. Further, gene expression analysis of chemotaxis and flagellar genes revealed that the Amb0994 and Amb2291 proteins act as repressors in a bidirectional inhibitory equilibrium model, which converges on shared downstream effectors to control "U-turn" behavior. Based on these findings, we extended the motion simulation model framework to provide a phenomenological explanation for the magneto-optical response. Together, these results elucidate potential mechanisms of magneto-optical signal synergy in MTB and offer new insights into the evolution of microbial taxis behaviors.

The intercalated disk (ID) is a structurally heterogeneous junctional complex essential for synchronized cardiac conduction and contraction. Previous computational models have investigated the influence of ID structure on cardiac conduction. However, most have relied on oversimplified geometries and uniformly distributed ion channels, limiting their ability to capture nanoscale heterogeneity. In this study, we expand our previous finite element mesh framework to produce a more physiologically realistic representation of the ID, incorporating spatially heterogeneous gap junctions and multiple ion and ionic current dynamics. We systematically quantify the impact of key structural and electrophysiological features on conduction by generating a comprehensive library of 384 ID mesh configurations and simulating tissue-level conduction for both strong and reduced gap junctional coupling. Further, we employed a multilayer perceptron neural network approach to quantify gradient-based sensitivity analysis, enabling a systematic quantification of the relative influence of geometric and nanostructural factors on ID and cleft dynamics, as well as tissue-level conduction across multiple regimes. In particular, sensitivity analysis revealed that gap junctional coupling, cleft geometry, and nanostructure heterogeneity are the dominant determinants of cleft potential, sodium current synchronization, and conduction velocity. We identify that membrane separation of the ID interplicate and plicate regions can exhibit context-dependent influences on conduction, either enhancing or slowing, depending on gap junctional coupling. Collectively, these findings highlight the regime-dependent roles of ID ultrastructure and establish a quantitative framework that links nanoscale ID morphology to tissue-scale cardiac conduction.

Epithelial-mesenchymal plasticity is a cell-fate switching program that enables cells to adopt a spectrum of phenotypes ranging from epithelial (E) to mesenchymal (M), including intermediate hybrid E/M states. Hybrid E/M phenotypes are conducive to cancer metastasis, as they are associated with metastatic initiation, cancer stemness, drug resistance, and collective migration. Boolean models of the gene regulatory networks underlying epithelial-mesenchymal plasticity have yielded valuable insights into the dynamics of E and M phenotypes. However, these models are limited in their ability to capture hybrid phenotypes effectively, as they restrict gene expression to binary states. In contrast, hybrid E/M cells often exhibit partial expression of epithelial and mesenchymal markers. To overcome this limitation, we modified a threshold-based Boolean formalism to incorporate intermediate gene expression levels. The resulting multilevel model reveals novel hybrid steady states characterized by partial expression of both E and M genes, thereby expanding the phenotypic landscape beyond that represented by traditional Boolean approaches. Notably, these hybrid states exhibit lower frustration compared with their counterparts in classical Boolean models. By resolving dynamical degeneracy, we demonstrate that the hybrid states identified by the multilevel model are more stable. Furthermore, the multilevel hybrid states are found to be highly heterogeneous and more plastic than the Boolean hybrid states, with enhanced hybrid-to-hybrid plasticity that can better explain sustained collective migration during metastasis. These findings suggest that introducing minimal additional complexity into Boolean models can uncover previously hidden qualitative features of phenotypic landscapes governed by gene regulatory networks.

We report that axons of cortical neurons, structurally intricate excitable media, maintain remarkably high fidelity in transmitting somatic spike timing, even during complex spontaneous network activity that includes extremely short (2-3 msec) inter-spike intervals. This robustness underscores their function as reliable conducting devices under physiological conditions. It is nevertheless well established that under artificially imposed, high-rate pulsing stimuli, axonal conduction can fail, with vulnerability depending on distance and branching. In line with this, we demonstrate that conduction failures can also occur at frequencies as low as 10 Hz, provided that stimulation is sustained for several seconds. Under these conditions, propagation delays increase and failures accumulate, particularly in distal branches, whereas effects are negligible at 1-4 Hz. Simulations incorporating cumulative sodium channel inactivation at vulnerable sites reproduce these dynamics. Our findings refine the view of axons as active, heterogeneous structures: they are exceptionally reliable across most physiological regimes, yet exhibit limits under prolonged or extreme stimulation, a regime that is critical for understanding axonal excitability, especially during sustained drive or in pathological conditions.

Chromatin organization is essential for DNA packaging and gene regulation in eukaryotic genomes. While significant progresses have been made, the exact molecular arrangement of nucleosomes remains controversial. Using a well-calibrated residue-level coarse-grained model and advanced dynamics modeling techniques, particularly the non-Markovian dynamics model, we map the free energy landscape of tetra-nucleosome systems, identify both metastable conformations and intermediate states in folding pathways, and quantify the folding kinetics. Our findings show that chromatin with 10n base pairs (bp) DNA linker lengths favors zigzag fibril structures. However, longer linker lengths destabilize this conformation. When the linker length is 10n+5 bp, chromatin loses the unique dominant conformation, favoring a dynamic ensemble of structures resembling folding intermediates. Embedding the tetra-nucleosome in a nucleosome condensate similarly shifts stability towards folding intermediates as a result of the competition of inter-nucleosomal contacts. These results suggest that chromatin organization observed in vivo arises from the unfolding of fibril structures due to nucleosome crowding and linker length variation. This perspective aids in unifying experimental studies to develop molecular models for chromatin.

The main thermodynamic force opposing protein folding is the loss of the polypeptide conformational entropy. Backbone conformational entropy, as well as other thermodynamic forces associated with folding, are difficult to measure directly, even for simple model systems such as α-helices. Helix-coil theories describe α-helix folding as a process involving the loss of backbone conformational freedom and the formation of hydrogen bonds. However, measuring the parameters for these two processes is difficult due to their coupled nature and requires additional parameters associated with the experimental observables. Here, we determine the backbone conformational entropy change (ΔS) accompanying α-helix formation by measuring the absolute heat capacities of a series of alanine peptides using differential scanning calorimetry. Directly measuring one of the system's thermodynamic properties enables a robust determination of helix-coil parameters using an ensemble-based statistical-thermodynamic model and Bayesian inference. The resulting backbone entropy change for the helix-to-coil transition is ΔS = (5.2 ± 0.3) cal mol K per peptide unit, less than previous experimental values but in line with the recent estimates from molecular dynamics simulations. Additionally, the determination of enthalpy and heat capacity changes offers a unified thermodynamic picture of α-helix formation. This has important implications for understanding the net energetic balance in protein folding and the interactions of intrinsically disordered proteins that undergo α-helix folding upon binding.

Tau is a microtubule-associated protein that plays a critical role in regulating the organization and stability of microtubules (MTs) in neurons. Although the association of tau with MTs is well recognized, the structural consequences of its binding at steady state remained poorly understood. Here, we combined small-angle X-ray scattering (SAXS) with AlphaFold predictions, and high-resolution modeling using our D+ software, to investigate how tau modulates the MT architecture in-vitro. We reconstituted a minimal model system of dynamic MTs with purified tubulin and then added either full-length tau (FL-tau, or 2N4R) or a minimal 4-repeats tau construct (4R-tau). We applied two assembly protocols: addition of tau to preassembled MTs and coassembly of tau with tubulin. Our analysis revealed that the two assembly protocols resulted in very similar structures at steady state. Tau altered the MT lattice organization in a construct-dependent manner. Specifically, tau binding increased the mass fraction of tubulin dimers in MTs with 15-protofilaments, whereas the largest mass fraction of tubulin still formed 14-protofilament MTs. Tau also limited the number of tubulin dimers in positional correlation along the longitudinal MT-axis. This result suggests that tau promoted tubulin nucleation and stabilized shorter MTs. These effects were more significant with FL-tau than with 4R-tau. In addition, the critical tubulin concentration for MT assembly decreased by about 20% in the presence of tau, and tau formed larger (non-MT) small tubulin complexes that coexisted with MT or tau-coated MT. Our results show that tau modified the lateral and longitudinal tubulin interactions, and modulated the MT architecture in a manner that is likely to be relevant to its function in vivo.

Liquid-liquid phase separation (LLPS) is one mechanism that cells can use to organize biomolecules spatially and functionally. Some coiled-coil (CC) proteins, such as the centrosomal proteins pericentrin and spd-5, are thought to LLPS, but it is currently unknown what parts of these proteins facilitate the process. It is thought, however, that the numerous CC domains in these proteins might be contributing to their LLPS. We recently showed, using computational studies and designed proteins, that CC domains can facilitate LLPS through specific interactions between the CC domains themselves, meaning that each CC was designed to interact only with a subset of other CCs in the system. This is in contrast to nonspecific interactions, where all CCs would be able to interact with all other CCs in the system, which is akin to some interactions (e.g. π-π) seen in phase-separating intrinsically disordered proteins. Because the specificity of interactions between natural CC domains is tunable in a sequence-dependent fashion, CC domains present a unique system that allows us to investigate the contributions of specific versus nonspecific interactions on LLPS. We show, in our computational system, that CC proteins with nonspecific interactions can LLPS but with less propensity compared to specific interactions. The LLPS propensity of CC proteins with nonspecific interactions can be improved by altering the structure of linker segments, without directly changing the specificity of interactions. We also demonstrate that the number of intra-chain CC contacts plays a direct role in determining LLPS for nonspecifically interacting proteins. These results have broad implications for the role of linker segments-protein features beyond the interaction domains e.g. 'stickers'-in protein LLPS and the formation of biomolecular condensates.

Cells in load-bearing tissues experience both solid deformation and interstitial fluid flow during physiological loading, but the mechanisms by which they integrate these poroelastic mechanical signals remain poorly understood. Here, we develop a porous, nanoarchitected 3D scaffold that allows simultaneous delivery and control of matrix strain and fluid shear stress. We validated the platform through cyclic loading experiments and simulations of fluid-structure interactions. In static, stress-free culture mechanical environments, osteoblast-like cells adopted shapes, cytoskeletal architectures, and focal adhesion patterns templated by the 3D scaffold geometry. Under cyclic compression, the combined influence of matrix deformation and induced fluid flow disrupted this alignment, producing disordered actin structures and reduced focal adhesion eccentricity. These changes emerged even under low-frequency loading, within the drained poroelastic regime, indicating a high sensitivity of cytoskeletal organization to fluid-solid coupling. Our findings establish a tractable and tunable platform to investigate how cells sense and respond to dynamic poroelastic mechanical environments in 3D.

Macromolecules, such as proteins, antibodies, nanobodies, and other affinity binders, play essential roles in therapeutic and diagnostic applications due to their high specificity and functionality. Understanding their structure is critical for deciphering their biological activity and drug discovery; however, the inherent complexity of these molecules poses significant challenges. Computational approaches have emerged as powerful tools for modeling macromolecular structures and interactions, offering faster and more cost-effective alternatives to experimental techniques. This review highlights state-of-the-art computational methods used in macromolecule modeling, with a strong focus on artificial intelligence (AI) and machine learning (ML)-based approaches. Key methodologies, on advanced AI/ML techniques that have revolutionized the field are discussed. We also discuss therapeutic applications of AI/ML approaches and explore how these technologies are transforming drug discovery by accurately predicting macromolecular structures, designing novel therapeutic molecules, modeling protein-protein and protein-drug interactions, estimating binding affinities and helping cheminformatics analyses. Finally, the review outlines ongoing shortcomings such as data integration, interpretability, and model validation, and offers perspectives on future directions. We assess the strengths and limitations of each computational approach and present challenges unique to different macromolecule types. By providing a comprehensive overview of current computational strategies, this review serves as a valuable resource for developing innovative approaches in drug development, while showcasing the state-of-the-art in computational macromolecular modeling.

The protein hydration shell is a key mediator of processes such as molecular recognition, protein folding, and proton transfer. How surface-exposed amino acids shape the hydration shell structure is not well understood. We combine molecular dynamics simulations with explicit-solvent predictions of small-angle X-ray scattering (SAXS) curves to quantify the contributions of all 20 proteinogenic amino acids to the hydration shell of the globular GB3 domain and the intrinsically disordered protein (IDP) XAO. We focus on two quantities encoded by SAXS curves: the hydration shell effect on the radius of gyration and the electron density contrast between protein and solvent. We derive an amino-acid-specific contrast score, revealing that acidic residues generate the strongest contrast with 1 to 1.5 excess water molecules relative to alanine, followed by cationic and polar residues. In contrast, apolar residues generate a water depletion layer. These trends are consistent across simulations with different water models. Around the XAO peptide, the hydration shell is generally far weaker compared to the globular GB3 domain, indicating unfavorable water-peptide packing at the IDP surface. The hydration shell effect on the radius of gyration of the IDP is strongly conformation-dependent. Together, the calculations show that the composition and spatial arrangement of surface-exposed amino acids govern the hydration shell structure, with implications for a wide range of biological functions and for hydration-sensitive experimental techniques such as solution scattering.

Cold atmospheric plasma (CAP) generates various products, including radiation, electrons, ions, electric field (EF) and reactive oxygen and nitrogen species. CAP promotes wound healing and has been implemented as several medical devices to treat acute or chronic wounds. Beyond the closure of the epidermal monolayer of keratinocytes, wound healing requires migration of dermal fibroblasts. The effect of CAP on fibroblast migration has been insufficiently characterized. Here we show, using primary human dermal fibroblasts that a 10 second exposure to CAP suffices to increase Arp2/3-dependent lamellipodium formation and migration persistence for several hours. In an attempt to identify the specific CAP- generated products involved in this activity, we found that reactive oxygen species and the EF induced by plasma were critical for enhanced and sustained migration persistence. Taken separately, these two stimuli were only partially active in inducing migration persistence at the dose that CAP generates. However, their combination was sufficient to recapitulate the full enhancement of migration persistence obtained with CAP exposure, indicating that these two stimuli act in synergy and that they likely represent the active components of CAP to activate migration of dermal fibroblasts during wound healing.