Dynamic light scattering was used to analyze nanoscale and mesoscopic protein-associated objects formed in lysozyme–NaCl solutions under crystallization conditions. The study focuses on the sequence of particle-size populations detected after salt addition and on their relation to the initial state of the protein solution. NaCl reproducibly induced a small population with hydrodynamic diameters of 5.6–6.5 nm, which is consistent with nanoscale oligomeric lysozyme assemblies and with previous SAXS/SANS evidence for dimers and octamers in crystallization solutions. Mesoscopic particles in the tens-to-hundreds of nanometers range and large submicron-to-micron aggregates appeared at selected stages of the experiments. The samples that produced crystals showed different mesoscopic-particle dynamics, whereas a sample with similar early DLS behavior did not yield crystals. These observations suggest that, in this limited series, the evolution of protein-associated nano- and mesoscopic objects was related to the initial particle-size distribution and the full temporal trajectory rather than to any single DLS peak. The DLS data are interpreted qualitatively, since intensity-weighted distributions do not provide direct number or mass fractions of the detected populations. The results characterize a multiscale and nonstationary self-organization process in a concentrated protein solution, involving a small nanoscale population consistent with oligomeric precursor clusters, mesoscopic aggregated states, and large sedimenting objects. This work may be useful for understanding soft biological nanoobjects and mesoscopic protein assemblies formed during salt-induced association and crystallization-related restructuring of concentrated protein solutions.
Plants account for the vast majority of the biomass on land. They first appear in the fossil record many millions of years ago. Some researchers have proposed that the obvious success of plant-life indicates that plants are “intelligent.” Plant intelligence has been strongly challenged, however, because plants do not possess brain-like tissues similar to electrical neurons, axons, and synapses. The following paper argues that a plant leaf is more properly understood as being a kind of analog computer called a “crossbar array.” The leaf’s computational facility arises from how leaf stomata respond to environmental water vapor, light, carbon dioxide, and heat. Though counterintuitive, a leaf’s success is enhanced by the phenomenon of “stomatal oscillations.”
In many plant cells, there are two types of mitochondrial motion: directed and wiggling. While the former is mediated by F-actin and microtubules, the latter is not. The fact that mitochondria migrate via wiggling suggests the existence of other mechanisms of motion aside from those related to the cytoskeleton and protein motors. In this work, it is assumed that wiggling mitochondria are active Brownian particles, self-propelled bodies whose motion at low Reynolds number is affected by noise. The proposed mechanism of motion is microswimming, where a wiggling mitochondrion is driven by a cycle of shape changes resembling a peristaltic wave travelling along its body. The peristaltic wave is modelled on a two-sphere swimmer under the far-field approximation, yielding expressions for the kinetic and dynamic variables involved, as well as for the factors determining its interaction with chloroplasts. The calculations show that the microswimmer can reach reported speeds with small size deformations and explain the observed high percentage of wiggling mitochondria captured by chloroplasts. Using the hydrodynamic results enables the application of a theoretical probabilistic model, including active and passive noise, which fits well with experimental results on speed distribution and trajectories of wiggling mitochondria. Taken together, the results explain the main mitochondrial wiggling characteristics observed in experiments, thus suggesting the feasibility of microswimming as a mechanism for mitochondrial wiggling.
Energy landscape theory provides a unifying framework for describing protein structure, dynamics, and function across hierarchies of spatial and temporal scales. In practice, however, protein energy landscapes are never accessed directly; they are represented through a hierarchy of theoretical models, from quantum mechanical potential energy surfaces to coarse-grained potentials of mean force. Each level of description entails a systematic reduction of degrees of freedom and a corresponding transformation of the underlying landscape. In this review, we examine how protein energy landscape representations change under successive approximations to the molecular Hamiltonian, with particular emphasis on the emergence, interpretation, and robustness of landscape concepts across scales. We argue that many simplified models succeed not because they reproduce microscopic interactions in detail, but because key topological features of the landscape, such as funnels, barriers, and competing basins, are preserved under projection. This article also clarifies the physical principles underlying coarse-graining, solvent modelling, and multilevel simulation strategies and demonstrates why energy landscape theory remains predictive despite reduced chemical resolution, highlighting its role as a unifying framework for exploring biomolecular space.
The family of deubiquitinating enzymes is primarily involved in the removal of ubiquitin chains from specific target proteins. Among these proteins, ubiquitin-specific protease 12 (USP12 protein) has been identified as a key molecule in regulating cellular homeostasis, cancer progression, and immune regulation. This protein is reportedly associated with colorectal cancer, prostate cancer, and breast cancer, along with neurodegenerative disorders like Huntington's disease. The study of mutations in USP12 could be crucial for understanding its impact on cancer and neurodegeneration, making it a potential target for therapeutics against such diseases. Our study uses a sequence-based and a structure-based screening method to identify a mutation in USP12 having the most deleterious and destabilizing effect. The mutation Y49N was found to significantly alter structural and biological functions of USP12 protein while compromising its stability. A total of 5000 conformations were generated for wild-type and Y49N_USP12 structures through normal mode analysis and a comparative study was conducted. We evaluated parameters like RMSD and Rg values to study structural divergence and reduced compactness in mutant protein conformations. Analysis of RMSF values of mutant protein showed significant structural deviations hinting at its increased flexibility. Further, heavily modified surface charge distributions were found through electrostatic surface charge mapping and fluctuations in energetic distribution indicated conformational perturbation. Moreover, the study shows how Y49N mutation could induce a helix to coil conversion in the N-terminal region of mutant protein structures throughout 5000 cycles of simulation. Our work helps advance our understanding of USP12 and its clinical value which needs to be explored further.
This study presents a novel approach to modeling fluid and ion transport in the proximal convoluted tubule (PCT) of the nephron using bond graphs. Bond graphs provide a robust framework for analyzing complex systems, explicitly depicting multi-domain energy exchange. Leveraging the modular nature of bond graphs, we first defined resistive modules representing membranes and capacitive modules representing solution-filled compartments, then coupled them using circuit theory. Our implementation extends beyond previous bond graph models of physiological processes by explicitly representing volumetric flow as a distinct variable within capacitive modules. In so doing, our model enables the consideration of mechanotransduction effects, where changes in fluid volume can influence membrane transporter activity, a crucial aspect of PCT function. Our bond graph model of the PCT (BG-PCT) comprises four fluid compartments bounded by five distinct membranes. The BG-PCT considers five chemical species (Na ^+ , K ^+ , Cl ^- , HCO _3^- , and glucose) and six key membrane transporters distributed across the different membranes. Each structural subsystem comprises elementary thermodynamic processes, including dissipation, free-energy change, and power flow. This study demonstrates the advantages of bond graph modeling, particularly in its capacity to couple multiple energy domains and its modularity, which enables future extensibility. The BG-PCT provides a flexible, thermodynamically consistent platform for in silico research on epithelial transport dynamics and is available on GitHub under an open-source license to facilitate future research.
Efflux pump (EP) proteins play an important role in the emergence of multidrug-resistant (MDR) Staphylococcus aureus and in reducing the effectiveness of antibiotic therapy. EP, NorA of the Major Facilitator Superfamily (MFS) family, is well known for effluxing fluoroquinolones, thereby reducing the intracellular drug concentrations to sub-inhibitory levels and causing S. aureus to be less susceptible. NorA uses the electrochemical proton gradient to efflux drugs across the cell membrane; however, the mechanism of drug efflux remains to be discovered. In the present study, conventional molecular dynamics of 1.0 μs have been performed and essential dynamics (ED), and global motion of the structure has been observed and further used as reference for specifying collective variables (CVs). A metadynamics (MetaD) simulation of 100 ns was employed to sample the conformational space based on the assigned collective variables (CVs). Five energy minima states, including a global minimum, were identified from the free-energy landscape analysis, demonstrating the opening and closing mechanisms of the NorA efflux pump. Transmembrane helices 1 and 2 are identified as the most flexible helices of the efflux pump, performing the function of a lid in the extracellular section (Leu 21 to Leu 42). The efflux process through the NorA efflux pump can be observed from the resulting conformational states, which may be further utilized for the future design and development of next-generation EP inhibitors to address the drug resistance issue in S. aureus.
Phototherapy using indocyanine green (ICG) has been shown to utilize photothermal and photodynamic effects, and its effectiveness is determined through complex interactions of light transport, heat generation, and reactive oxygen species (ROS) production. In this paper, we report a computational framework for simulating the pulsed 808-nm photoactivation of ICG in a three-dimensional tumor spheroid. Photon transport in the heterogeneous spheroid domain is simulated using GPU-accelerated Monte Carlo photon transport, and the absorbed optical energy is coupled with a transient bioheat equation, an oxygen diffusion–reaction equation, and ROS production equation. The computational domain was defined as a voxelized spheroid with dimensions of 100 × 100 × 100 voxels, which represented the hypoxic tumor core and proliferative tumor areas. The simulation revealed that the fluence distribution is heterogeneous with significant attenuation, with about 70
Biological rhythms serve as essential timing mechanisms for regulating physiological processes, with period and amplitude being two key characteristics. Decoupled control of these characteristics is challenging due to their complex interdependencies. This study proposes a combinatorial coordination ratio perturbation approach for independently modulating the period and amplitude of biological rhythms. The approach integrates correlation analysis and Taylor expansion. Correlation analysis offers potential perturbation combinations, while Taylor expansion assists in determining the coordination perturbation ratio. We mathematically demonstrate the feasibility of independently adjusting the period and amplitudes through combinatorial perturbation and have developed an algorithm to identify optimal parameter combinations. When applied to the Per-Cry-Bmal1 circadian model, the method enables amplitude modulation while maintaining a constant period, and it also allows for period modulation while keeping the amplitude constant, thus demonstrating its practical effectiveness. This study advances circadian rhythm research by providing a simple and feasible method for decoupled period-amplitude control, with potential applications in the treatment of circadian-related disorders and chronomedicine. Moreover, this method exhibits excellent generality and is applicable to other oscillatory dynamics as well.
A digital holographic interferometer is used to measure surface deformation and fracture points in hen eggs under mechanical compression. All samples were subjected to a constant compression load until they fractured. Three different tests are performed to analyze the eggs’ mechanical response. In the first test, the egg is vertically oriented, and so the applied load; meanwhile, for the second test, the egg is horizontally oriented with the load applied vertically. The third test keeps the egg horizontal, but on this occasion, the applied load is also horizontal. As the mechanical load is applied, a camera records full-field holograms that retrieve the entire surface deformation of each egg. The influence of the egg’s orientation and the load direction modifies the eggshell surface deformation and fracture pattern. In general, fracture propagation is aligned with the load direction. From the results, samples compressed along the length of the egg show fractures, but remained in one piece. A different behavior is observed when the load is applied at the egg’s width, which separates the eggshell into two semispheres, allowing the egg’s content to drain out. A multivariate analysis was used to integrate the egg dimensions with the resulting optical data, showing a strong relationship between egg size and the fracture point. Larger eggs tend to exhibit more extensive fractures, while smaller and medium-sized eggs showed minor damage. Even though some of this information is empirically known, this is the first time it has been proven by a full imaging inspection showing eggshell deformation and cracks across eggs, independent of egg size. The latter is an advantage as there is no need to limit the study to particular dimensions to measure the exact moment of the fracture, its position, and distribution.
HER2 plays a crucial role in breast cancer (BC) progression, with the D769H and D769Y mutations significantly influencing its structural integrity, drug-binding dynamics, and therapeutic response. This study employs molecular docking and molecular dynamics simulations (MDS), with trajectories propagated for 1000 ns, to examine their distinct effects. Root mean square deviation (RMSD) analysis indicates increased conformational deviations in mutant structures, signifying heightened instability, while root mean square fluctuation (RMSF) reveals enhanced flexibility near the mutation site. Solvent accessible surface area (SASA) calculations highlight changes in solvent exposure, directly affecting ligand accessibility, while radius of gyration (Rg) assessments suggest structural loosening or tightening in response to mutation-induced alterations. Binding free energy calculations using MM-PBSA indicate variability in drug affinity, with mutations disrupting hydrogen-bonding networks and altering ligand stability. Principal Component Analysis (PCA) delineates distinct motion trajectories in mutant proteins, revealing shifts in conformational behavior. Umbrella sampling simulations indicate that while the wild-type HER2-drug complex requires 150 ps to reach equilibrium, the D769H mutant stabilizes within 100 ps, suggesting diminished drug retention. Conversely, the D769Y mutation enhances ligand binding, surpassing wild-type interaction strength. These findings elucidate mutation-specific effects on HER2 structural dynamics and drug interactions, underscoring the need for mutation-tailored therapeutic strategies to mitigate the impact of these variants.
The study employs dynamic mode decomposition (DMD) to elucidate the underlying mechanisms contributing to the enhanced motility observed in sperm bundles, primarily focusing on the role of flagellar synchronization. The decomposition reveals that synchronized flagellar movements might be a key factor enabling sperm cells to attain higher velocities when connected in bundles. Through DMD, periodical characteristics of individual periodical motion patterns, such as frequency, amplitude, and modal growth/decay rates (from DMD eigenvalues), are characterized, elucidating main parameters of the dynamic behavior of these biological systems, such as dominant frequencies of periodical motion, as well as amplitudes and velocities. The implications of this research extend beyond understanding sperm bundle dynamics, as the methodology is adaptable for identifying healthy sperm cells based on their motility patterns. Additionally, the approach holds potential for broader applications in studying other flagellar-driven microorganisms, providing a valuable tool for comparative analysis across various species.
DNA is one of the primary intracellular targets for various anticancer drugs. Insight into the ligand-DNA interactions is critical for developing novel, promising bioactive molecules for therapeutic use. This is greatly aided by interpreting the interaction mechanism between small molecules and natural polymeric DNAs. The binding of small molecules to the DNA alters the mechanics of the strands, resulting in the inhibition of replication and transcription, providing information on the influence of gene expression. Xanthoxylin (XAN) is a phenolic compound recognised for various therapeutic activities, including anticancer; however, its mode of interaction with DNA has not been elucidated yet. This study investigated the interaction between XAN and calf thymus DNA (Ct-DNA) using docking simulation and spectroscopic techniques. Before DNA-binding studies, XAN was subjected to in silico ADMET analysis using SwissADME and pkCSM web servers. ADMET predictions are crucial to assess the drug-likeness and the safety profile of the bioactive molecules early in the development phase. XAN demonstrated acceptable physicochemical properties, conformance to drug-likeness, and a low risk of toxicity. Docking analysis revealed two distinct binding modes: intercalation and possible minor groove binding. Hyperchromic shifts observed in absorption spectroscopy confirmed the complex formation between XAN and Ct-DNA, with an estimated association constant (Ka) in the order of 104 M−1. The thermodynamic parameters indicated a spontaneous and exothermic binding process, involving van der Waals forces and hydrogen bonding. Dye dislocation studies revealed that XAN binds via the minor groove. Circular dichroism and thermal denaturation profiles further manifested the groove binding mode of XAN. However, molecular docking studies were inconsistent in predicting the precise binding mode, only partially corroborating the in vitro findings. Plasmid nicking assays indicated that XAN does not induce DNA damage and is not a prooxidant. Conversely, it significantly reduced DNA lesions induced by the Fenton reaction, suggesting its role as a reactive oxygen species (ROS)-scavenging agent. These computational and in vitro results evinced that XAN has drug-relevant attributes and can act as a DNA-binding agent and an antioxidant.
We develop a refined quantum framework for the induced-fit model of allosteric enzymes incorporating vibrational exciton (Davydov’s soliton) dynamics and open-system perturbation theory. Using realistic biochemical parameters, we numerically evaluate the excitation conditions and find that under normal assumptions the quantum excitation energy remains orders of magnitude below the threshold needed to drive a stable soliton. This implies that classical Davydov conditions alone are insufficient for enzyme catalysis on sub-nanosecond timescales. To address this, we identify additional factors – multi-state energy accumulation and strong quantum-coherent processes – that could plausibly enhance the effect. We discuss model limitations (e.g., idealized 1D protein chain, neglect of dissipation) and the validity of our assumptions. By modeling allosteric enzymes as quantum multi-particle systems, we represent substrate-induced structural changes as Hamiltonian deformations and calculate transition probabilities and interaction energies that correlate with enzymatic accuracy or error. While Davydov’s soliton offers an appealing formalism, our calculations indicate they are insufficient under naïve parameter choices. Under standard parameters, this mechanism alone is not sufficient and requires auxiliary mechanisms. We present conditions (e.g., multi-state accumulation, enhanced coupling) under which solitonic behaviour might emerge, and propose experiments/simulations to validate these scenarios. This work bridges biophysical mechanisms with quantum mechanics, offering a novel perspective on enzymatic function at the quantum level. Finally, we situate our model within the broader context of macro-quantum effects (quantum coherence, tunneling, superradiance) known in biology, arguing that while Davydov’s soliton remains speculative, related quantum phenomena (e.g., proton tunneling) are well-supported in enzymatic systems.
Estimation of cell stiffness has assumed significance as their difference in cancer and normal cells is being exploited for diagnostic and therapeutic purposes. However, the cell stiffness values reported (in terms of elastic modulus (E)) are largely varied even within the same cell line, hindering their exploitation. The current study aims to dissect and understand the various parameters resulting in these differences. Based on the analysis of E values reported from 1992 to 2025, it is found that the stiffness of breast cancer cell lines decreases in the order MCF-10A > MCF-7 > MDA-MB-231. The effect of anti-cancer strategies on cell stiffness reveals the important role played by the remodeling of the cytoskeleton. The dual role played by the cytoskeleton in maintaining the required stiffness and deformability in cancer cells based on biological conditions is consistently observed through the analyzed studies. The forest plots synthesized propose that although increasing and decreasing the stiffness of cancer cells is a treatment strategy, more studies focus on decreasing the stiffness of cancer cells to limit metastasis. The trend of variation in cell stiffness urges us to hypothesize the existence of a threshold range for mean stiffness values in cancer cells. The insights obtained can serve as a framework for studies related to the stiffness of breast cancer cells and aid researchers in making meaningful assessments of the obtained E values—a criterion emerging as a major player in cancer diagnosis.
It has been known for a long time that osmosis transfers water from low to high salt concentrations, but few simulation studies have been conducted in confined spaces. This study evaluated the osmotic axial pressure at charged disk surfaces within charged cylindrical nanopores, how osmosis influences other axial pressures, and what controls it. The screening charges on the disk surfaces determine the osmotic axial pressures and critically influence the other axial pressures. External factors (electric field and ion concentrations) and internal (disk and pore wall charges) control the screening charges. The external electric field influenced the Coulomb and dielectric axial pressures more by altering the disk screening charges than by acting directly on the fixed charges. In contrast, the pore wall fixed charges influenced the Coulomb and dielectric axial pressures exclusively by altering the disk screening charges, and their influence extended into the pore center (i.e., well beyond the length of the pore wall diffuse double layer). Both pressures increased near the pore center, especially for larger disks, indicating greater screening charge accumulation around larger disks. The axial osmotic pressure was constant along the disk surface, almost to the tip, and was disk size-independent, owing to the radially constant and disk size-independent screening charges at the disk surfaces. Finally, the axial osmotic pressure at the disk surfaces (with dielectric and fluidic pressures) typically counter-balanced the Coulomb pressure that drives the disk translocation through the nanopore, reducing its speed. The disk translocation direction may reverse at low external ion concentrations.
Biofilms are widely present in any environment with water and a substrate, posing microbial contamination risks to flow pipelines. This study established a bacterial biofilm flow growth model based on the experimental phenomena of Bacillus subtilis biofilm in microfluidic channels, combining the principles of cellular automata with the finite element method. In the model, the hydrodynamic model was developed using the COMSOL platform to analyze the flow field distribution characteristics induced by micropost. A cellular automata model was developed in MATLAB, innovatively incorporating a flow direction weight algorithm and a filamentous growth mode. The study focused on the attachment behavior of biofilms in microfluidic channels, and simulations of biofilm growth in microfluidic channels with different micropost structures were conducted. The model successfully reproduced key experimental phenomena, such as the attachment and growth of filamentous structures and the aggregation of streamer-like biofilms. By combining real-time flow field analysis with the model, the attachment and growth mechanism of biofilm in the micropillar-flow system was revealed. The spatial arrangement of microposts affects the flow paths of free bacteria by altering streamline distribution. The secondary flow induced by the micropillars promotes bacterial attachment, and its spatial distribution characteristics determine the initial attachment sites of bacteria. This study provides a reference for preventing biofilm formation in flow pipelines and reducing the risk of microbial contamination in similar devices.
Exosomes released by epithelial keratinocytes and dermal fibroblasts significantly accelerate wound healing. Moreover, endogenous electric fields (EFs) were demonstrated to promote wound healing by directing the migration of epidermal cells toward the wound center, it is currently unclear whether EFs may facilitate wound healing by regulating the secretion of exosomes in these cells. In this study, we demonstrated that physiological-intensity EFs significantly enhanced exosome secretion from HaCaT cells, with the total protein content of the exosomes increased by approximately 1.5 times higher than that of the control group. Additionally, the exosomes derived from EF-stimulated HaCaT cells accelerated the wound healing rate of HaCaT and HSF cells, and the wound closure rate increased by approximately 20%. Mechanistically, we identified that EFs regulated exosome secretion by influencing the expression of exosome-related proteins-including ALIX and TSG101. Overall, our research results indicate that the electric field is an effective regulatory factor for enhancing exosome secretion and establish a novel high-exosome-producing strategy based on bioelectrics. This may lay the foundation for the translational application of exosomes in wound healing and other fields.
Interest in studying the interaction of small molecules with DNA is caused by the need to develop new, highly effective, and low-toxic drugs for cancer treatment. The strong and highly specific binding of thionine with DNA makes it a promising candidate for use in medicine and pharmacology. In this study, DNA-thionine complexes in aqueous solutions were investigated using UV–Vis absorption spectroscopy. The thermal stability of native DNA was studied in a broad range of thionine concentrations. The mechanisms of thionine binding to DNA, depending on the concentration of thionine, have been established. At low thionine concentrations ([cth] ≤ 1.5 mg/L), thionine molecules intercalate between the base pairs of the DNA double helix. At a thionine concentration of 1.5 – 10 mg/L, the groove binding and external electrostatic interaction of positively charged thionine with negatively charged biopolymer phosphate groups of the DNA backbones is preferable. In all cases, the interaction of thionine with DNA leads to an increase in the thermal stability of the polynucleotide. These findings provide valuable insight into the concentration-dependent molecular mechanisms of DNA-small molecule interactions, supporting the rational design of anticancer and antimicrobial agents, as well as exploiting molecular probes for nucleic acid detection, imaging, and other biomedical applications.
Turing patterns emerging from the vegetation-water model exhibit complex spatial and networked structures, while parameter identification of these patterns has become a challenging inverse problem. This paper aims to present two types of methods for parameter identification, based on a vegetation-water model coupled with climate data on precipitation, temperature, and carbon dioxide concentration in Zhangye. The statistical approach identifies parameters through handcrafted image feature matching using the distance metric. In addition, the deep learning method is employed for parameter identification, one is the modified ResNet50 with a regression head and integrated regularization to enhance generalization; the other is the improved VGG19 that adopts the Gaussian Error Linear Unit (GELU) function and mixed-precision training for greater efficiency. The identification results show that the deep learning methods achieve superior accuracy and robustness compared to the statistical approach, and ResNet50 achieves the best overall performance. Normalized difference vegetation index (NDVI) data further validate the numerical simulation results. Results from parameter identification on patterns enhance the parameterization and predictive capacity of vegetation-water models under climate change.
