Receptor–ligand interactions at cell interfaces initiate signaling cascades essential for cellular communication and effector functions. Specifically, T cell receptor (TCR) interactions with pathogen-derived peptides presented by the major histocompatibility complex (pMHC) molecules on antigen-presenting cells are crucial for T cell activation. The binding duration, or dwell time, of TCR–pMHC interactions correlates with downstream signaling efficacy, with strong agonists exhibiting longer lifetimes compared to weak agonists. Traditional surface plasmon resonance (SPR) methods quantify 3D affinity but lack cellular context and fail to account for factors like membrane fluctuations. In the recent years, single-molecule Förster resonance energy transfer (smFRET) has been applied to measure 2D binding kinetics of TCR–pMHC interactions in a cellular context. Here, we introduce a rigorous mathematical model based on survival analysis to determine exponentially distributed receptor–ligand interaction lifetimes, verified through simulated data. Additionally, we developed a comprehensive analysis pipeline to extract interaction lifetimes from raw microscopy images, demonstrating the model’s accuracy and robustness across multiple TCR–pMHC pairs. Our new software suite automates data processing to enhance throughput and reduce bias. This methodology provides a refined tool for investigating T cell activation mechanisms, offering insights into immune response modulation.

This study explores the efficacy of using EEG-based parameters to diagnose bipolar disorder. A synthetic dataset was generated, including both correctly diagnosed and misdiagnosed cases, simulating realistic clinical conditions. EEG features such as theta-alpha mean, beta band mean, and coherence measures were used to train a multi-layer perceptron (MLP) model. The model achieved a validation accuracy of 92%, demonstrating strong potential for EEG-based diagnostics. However, challenges such as standardization of electrode configurations and addressing equipment differences are crucial for broader applicability and validity of the findings in diverse clinical settings.

This study investigates the ergodic behavior of brain waves through an innovative multidisciplinary approach, integrating insights from neuroscience, physics, economics, and biology. By examining the statistical properties and dynamic characteristics of alpha (8-13 Hz), beta (13-30 Hz), theta (4-8 Hz), and delta (0.5-4 Hz) brain waves, we argue that the ergodic nature of these neural oscillations can be comprehensively understood and quantified using analytical tools from diverse scientific disciplines. Our research begins with a thorough review of current neurophysiological understanding of brain wave generation and propagation. We then introduce the concept of ergodicity, originating from statistical physics, and explore its applicability to neural systems. The study employs advanced time series analysis techniques and stochastic process models, borrowed from economics and physics, to analyze large-scale electroencephalographic (EEG) datasets. Key findings suggest that brain waves exhibit ergodic properties under specific physiological conditions, with implications for understanding neural information processing and brain state transitions. We propose a novel framework that characterizes the ergodic behavior of brain waves across different frequency bands and cognitive states. This framework provides new insights into the relationship between local neural dynamics and global brain function.Furthermore, we discuss the biological implications of ergodic brain waves, considering evolutionary perspectives and potential adaptive advantages. Our analysis reveals how the ergodic properties of neural oscillations may contribute to the brain's ability to balance stability and flexibility, a crucial aspect of adaptive behavior and cognition. The study's findings have significant implications for multiple fields. In neuroscience, our framework offers new tools for analyzing brain function and may inform the development of innovative therapeutic interventions for neurological disorders. In physics and complex systems theory, it provides a concrete biological example of ergodic behavior in a highly complex, non-linear system. For the field of economics, it demonstrates the successful application of financial modeling techniques to biological time series data. This research contributes to a more holistic understanding of the brain as a complex, adaptive system operating at the intersection of physics, biology, and information processing. It also opens new avenues for future research, including investigations into the relationship between ergodic properties of brain waves and consciousness, cognitive flexibility, and the emergence of complex behavior from neural ensembles.

Abstract: Currently, antibodies are major therapeutic modalities in biopharmaceutical research and development. Because of required high dose for certain clinical applications, antibodies are formulated at high protein concentration for convenience of administration, shipping and storage. We will describe the inter-molecular interactions that can occur in such a crowded environment of high protein concentration. Two types of repulsive interactions, i.e., electrostatic and excluded volume effects, and three types of attractive interactions, i.e., hydrophobic, electrostatic and cross-linking, need to be considered in such a crowded environments. We then show consequences of such inter-molecular interactions, leading to high viscosity, opalescence and phase separation of antibody solutions. Co-solvents are used to control the stability, structure and molecular interactions and thereby to alleviate these solution problems. Among various co-solvents, arginine has been extensively used to suppress protein-protein interactions at high protein concentration. Arginine weakly but extensively interacts with aromatic/hydrophobic groups as well as charged groups on proteins, leading to effective suppression of viscosity, opalescence and phase separation of antibody solution.

Autism spectrum disorder (ASD) is characterized by diverse patterns of social interaction, communication, and behavior. This study explores the application of clustering and stochastic migration models to visualize and understand the social and neural connectivity patterns associated with autism. The clustering model, with its strong internal connections and limited external connections, mirrors the tight-knit social groups and communication barriers often observed in individuals with autism. In contrast, the stochastic model, characterized by more dispersed connections and greater adaptability, represents the variability and flexibility seen in social interactions across typical individuals. By comparing these models, we can gain deeper insights into the unique challenges and strengths of individuals with autism, highlighting the importance of personalized interventions. Additionally, these models provide a visual framework for understanding the differences in brain connectivity patterns observed in autism, with implications for both behavioral and neural correlates. This study underscores the value of computational models in advancing our understanding of autism and guiding effective support strategies.

We extended our model of the S1 tubular segment to address the mechanisms by which SGLT1 interacts with lateral Na/K pumps and tight junctional complexes to generate isosmotic fluid reabsorption via tubular segment S3. The strategy applied allowed simulation of laboratory experiments. Reproducing known experimental results constrained the range of acceptable model outputs and contributed to minimizing the free parameter space. 1. In experimental conditions, published Na and K concentrations of proximal kidney cells were found to deviate substantially from their normal physiological levels. Analysis of the mechanisms involved suggested insufficient oxygen supply as the cause and indirectly, that a main function of the Na/H exchanger (NHE3) is to extrude protons stemming from mitochondrial energy metabolism. 2. The water path from the lumen to the peritubular space is through aquaporins on the cell membrane and claudin-2 at paracellular tight junctions, with a smaller additional contribution to water transport by the coupling of 1 glucose:2 Na:200 H2O in SGLT1. 3. A Na+-uptake component passed through paracellular junctions by solvent drag in Na- and water-permeable claudin-2, thus bypassing the Na/K-pump, in agreement with the findings of early studies. 4. Electrical cross talk between apical rheogenic SGLT1 and lateral rheogenic Na/K pumps results in tight coupling of luminal glucose uptake and transepithelial water flow. 5. Isosmotic transport is achieved by Na-mediated ion recirculation at the peritubular membrane.

In the introductory part of this review, the relevant results of mostly the latest experimental studies on carotenoid absorption and availability in the human body are briefly presented. The subsequent parts summarise such topics as carotenoid carriers, body transport and tissue delivery, before finally reporting on molecular-level studies of carotenoid binding by membrane receptors. The main message of the review is contained in the section describing computational investigations of carotenoid intercalation and dynamic behaviour in lipid bilayers. The relevance of these computational studies lies in showing the direct link between the microscopic behaviour of molecules and the characteristics of their macroscopic ensembles. Furthermore, studying the interactions between carotenoids and lipid bilayers, and certainly proteins, on the molecular- and atomic-level using computational methods facilitates interpretation and explanation of the macroscopic properties of these systems and, hopefully, helps to better understand the biological functions of carotenoids.

This study explores the intriguing phenomenon of action potentials reverberating and propagating in high-resistance tissues, such as gliosis, leading to the emergence of short circuits within the brain. Gliosis, characterized by increased tissue resistance, presents a unique environment for electrical signals. My research investigates the possible mechanisms behind these reverberations and their role in the creation of self-empowering short circuits that can potentially affect the entire brain's functionality. Understanding these processes may have significant implications for the treatment and management of neurological disorders associated with gliosis and abnormal electrical activity in the brain.

The spread of multidrug-resistant mycobacterium strains requires the development of new approaches to combat diseases caused by these pathogens. For that, photodynamic inactivation (PDI) is a promising approach. In this study, a tricarbocyanine (TCC) is used for the first time as a near-infrared (740 nm) activatable PDI photosensitizer to kill mycobacteria with deeper light penetration. For better targeting, a novel tricarbocyanine dye functionalized with two trehalose units (TCC2Tre) is developed. The photodynamic effect of the conjugate against mycobacteria, including Mycobacterium tuberculosis, is evaluated. Under irradiation, TCC2Tre causes more effective killing of mycobacteria compared to the photosensitizer without trehalose conjugation, with 99.99% dead vegetative cells of M. tuberculosis and M. smegmatis. In addition, effective photoinactivation of dormant forms of M. smegmatis is observed after incubation with TCC2Tre. Mycobacteria treated with TCC2Tre are more sensitive to 740 nm light than the Gram-positive Micrococcus luteus and the Gram-negative Escherichia coli. For the first time, this study demonstrates the possibility of in vitro PDI of mycobacteria including the fast-growing M. smegmatis and the slow-growing M. tuberculosis using near-infrared activatable photosensitizers. These findings are useful for the development of new efficient alternatives to antibiotic therapy.

Amino acid sequences of native proteins are generally not palindromic. The protein molecule obtained as a result of reading the amino acid sequence backwards, i.e., a retro-protein, having the same amino acid composition and the same hydrophobicity profile as the native sequence may behave as a different molecule. Here we have used R4, a directed evolution product of Scfv-13, a protein that binds β-galactosidase, and report the properties of the retro-R4 (rR4). rR4 remains a binding protein and its new target is Glutamine-fructose-6-phosphate aminotransferase (GLMS), a native E. coli protein. Additionally, a tight junction immunoglobulin domain- containing membrane protein, junctional adhesion molecule A (JAMA) retro-protein retains the ability to drive cell-cell interactions. Thus, protein engineering suggests a new potential application to retro-proteins of the immunoglobulin superfamily.

Drug-target binding is an essential parameter in drug discovery and design to ensure drug efficacy and specificity. In 2022, the concept of a general intermolecular binding affinity calculator (GIBAC) was for the first time coined: $Kd = f(molecules, envPara)$. Technically, GIBAC represents a set of in silico approaches for structural and biophysical data generation towards a paradigm shift in precise drug discovery and design, providing a comprehensive framework for intermolecular binding affinity calculation with adequate accuracy, precision and efficiency. For the first time, this study reports a prototype of GIBAC (semaGIBAC), i.e., a one-dimensional semaglutide-GLP-1R-based mini static GIBAC, based on an experimental complex structure of semaglutide and GLP-1R. Semaglutide is a potent GLP-1 receptor agonist used to treat type 2 diabetes mellitus by regulating blood glucose levels and promoting weight loss. In 2021, a structural modification involving a Val27-Arg28 exchange was manually introduced to enhance semaglutide-GLP-1R binding affinity. This study employs a comprehensive structural and biophysical analysis aimed at thoroughly exploring the sequence space of semaglutide-GLP-1R to design analogues with improved binding affinity, leading to the identification of a promising semaglutide analogue, which binds to GLP-1R with an affinity that is more than two orders of magnitude (113.3 times) higher than native semaglutide. To sum up, this article puts forward a promising structural biophysical approach for developing GLP-1 receptor agonists with enhanced efficacy, and with a GIBAC prototype (semaGIBAC), this article argues again that the time is now ripe for the construction of a real GIBAC to be listed on the agenda of the drug discovery and design community.

Although evolutionary theory has yet to furnish a comprehensive rationale for the underlying mechanism of natural selection, it is pertinent to note that the phenomenon transcends the confines of biological systems, exhibiting analogous patterns in physical systems as well. Specifically, isolated and closed systems tend to evolve towards states of progressively increasing entropy. In scenarios where multiple paths for entropy increase are accessible, such systems tend to favor combinations of paths that exhibit the most rapid entropy increase rates. Among the various processes in nature, life activities constitute a salient means of achieving entropy increase. Genetic variation within organisms generates individuals that vary in their rates of entropy increase. As these organisms interact, compete and combine, they form diverse combinations of entropy-increasing pathways. Nature, in turn, selects among these combinations, favoring those that culminate in the fastest entropy increase, thereby propelling the evolution of life. Essentially, the evolution of life is an ongoing exploration of diverse combinations of pathways that maximize entropy increase across various energy pools. Amidst continuous genetic variation and the selective pressure imposed by nature for maximum entropy production rates, information accumulates, leading to a corresponding acceleration in the rate of entropy increase. This natural selection, which favors maximizing the entropy at the fastest possible rate, serves as the ultimate driving force for the origin and evolution of life.

In these days of information overload and high-throughput analysis, it is easy to lose focus on the study of individual proteins. It is our conjecture that such investigations are still crucially important and offer uniquely penetrative insights. We thus present a discussion of biophysical methods to allow readers to get to know their protein of interest better. Although this perspective is not written with the expert in mind, we hope that for interdisciplinary scientists, or researchers who do not routinely perform biophysical analyses, the content will be helpful and inspiring.

In Orch OR model ; using electromagnetic fields, the brain captures, stores and analyzes the information of the cells and then sends the necessary commands to them. If the cells undergo changes and become cancer cells, the information stored in the brain and the electromagnetic signals inside and outside it change. Based on these changes, tumor and cancer formation can be detected in the early stages, and by storing the right information in the brain; it created the context for sending appropriate messages to return the cells to a normal state.

The endeavor to decode the brain's intricate architecture and its multifaceted functions has propelled scientists into the domain of complex differential equations, aiming to construct a computational framework capable of representing its uncountable intricacies. This article delves into the sophisticated and daunting task of modeling the brain, a system of seemingly uncountable coupled differential equations, each symbolizing the vast array of interconnected neural pathways and processes. We discuss the current limitations of computational resources and the significant challenges faced in accurately capturing the non-linear, high-dimensional, and plastic nature of neural dynamics. Despite these challenges, we explore the progressive strides in computational neuroscience that lay the groundwork for partial models, offering insights into the brain's localized circuits and broader network interactions. These models serve as pivotal stepping stones towards a more comprehensive understanding, which is incrementally built upon by interdisciplinary approaches, leveraging advancements in neuroimaging, data analysis, and theoretical biology. Looking ahead, the article speculates on the potential paradigm shifts that could arise from breakthroughs in computational technology, such as quantum computing, that may one day enable the simulation of the brain in its entirety. The article ultimately paints a picture of cautious optimism, where each incremental discovery is a valuable piece of the grand puzzle, bringing us closer to the epochal goal of comprehensively modeling the human brain.

In the domain of drug discovery and design, the acquisition of precise structural and intermolecular Kd data for biological molecules is paramount. However, conventional methods for obtaining such data are often beset by challenges including high costs, time intensiveness, and inherent limitations in scalability and diversity. This article puts forward a high-throughput approach for in silico generation of structural and intermolecular binding affinity (Kd) data, which keeps exploring the uncharted territories of drug discovery & design by harnessing computational methodologies to synthetically generate diverse datasets. Through a meticulously designed workflow encompassing molecular structural modeling, structural biophysics-based calculations of intermolecular binding affinity (Kd), this innovative methodology transcends the constraints of traditional experimentation, offering a cost-effective, scalable, and efficient alternative. By simulating molecular interactions and binding interfaces and predicting binding affinities with reasonable accuracy, this approach not only expedites the drug development process but also enables the exploration of vast molecular space, thereby facilitating the discovery of novel therapeutics beyond conventional drug modality as a form factor. Moreover, the versatility of synthetic data extends beyond virtual screening and lead optimization, encompassing applications such as dataset augmentation, model validation, and benchmarking against experimental data. This article elucidates the conceptual underpinnings, methodological intricacies, validation strategies, and potential ramifications of in silico generated data, heralding a paradigm shift in drug discovery paradigms. By fostering synergy between computational and experimental research domains, this innovative approach promises to accelerate the pace of drug discovery, enhance the robustness of predictive models, and pave the way for transformative advancements in the entire pharmaceutical industry.

Phosphodiesterase type 5 (PDE5) inhibitors are widely used in the treatment of various conditions, including erectile dysfunction and pulmonary hypertension. Despite their clinical efficacy, these drugs quite often cause visual disturbances due to off-target effects on Phosphodiesterase type 5 (PDE6) in the retina. This review explores the application of a general intermolecular binding affinity calculator (GIBAC)-based binding selectivity strategy in the design of PDE5 inhibitors, aiming to enhance binding selectivity and minimize visual side effects. This GIBAC strategy integrates computational structural biological approaches to iteratively refine drug-target binding affinities, thereby improving target specificity and the structural biophysical limit for the efficacy-safety balance of PDE5 inhibitors. Through detailed analysis of PDE5’s and PDE6’s biological role and the molecular mechanisms underlying visual disturbances, this article underscores the necessity of target binding selectivity in PDE5 inhibitor design in future. Additionally, this article discusses the practical applications of GIBAC in computational drug discovery and design, along with the future directions and the potential for GIBAC to transform PDE5 inhibitor development, ultimately enhancing therapeutic outcomes and patient safety. Finally, this article calls for a GIBAC-based paradigm shift in computational drug discovery and design towards the continued development of the pharmaceutical industry.

Long-range HNCO NMR spectra for proteins show crosspeaks due to 1JNC, 2JNC, 3JNCg and h3JNC couplings. The h3JNC couplings are transmitted through hydrogen bonds and are correlated to hydrogen bond lengths. We collected long-range HNCO data at a series of temperatures for four protein states. P22i and CUS-3i are six-stranded beta-barrel I-domains from phages P22 and CUS-3 that share less than 40% sequence identity. The cis and trans states of the C-terminal domain from pore-forming toxin hemolysin ΙΙ (HlyIIC) arise from isomerization of a single G404-P405 peptide bond. For P22i and CUS-3i the hydrogen bonds detected by NMR agree with those observed in the corresponding domains from cryoEM structures of the two phages. Hydrogen bond lengths derived from the h3JNC couplings, however, are poorly conserved between the distantly related CUS-3i and P22i domains and show differences even between the cis and trans states of HlyIIC. This is consistent with hydrogen bond lengths being determined by local differences in structure rather than the overall folding topology. With increasing temperature hydrogen bonds typically show an apparent increase in length, which has been attributed to protein thermal expansion. Some hydrogen bonds are invariant with temperature, however, while others show apparent decreases in length suggesting they become stabilized with increasing temperature. Considering the data for the three proteins in this study and previously published data for ubiquitin and GB3, lowered protein folding stability and cooperativity correlates with a larger range of temperature responses for hydrogen bonds. This suggests a partial uncoupling of hydrogen bond energetics from global unfolding cooperativity as protein stability decreases.

The interaction between electromagnetic fields (EMFs) and biological systems has been a subject of intense research and debate. This study explores the potential influence of magnetic fields on neural circuits, which are the building blocks of the human brain. By developing a mathematical model and conducting computational simulations, we investigate how magnetic fields can affect the transmission of electrical signals within neural circuits. The results suggest that magnetic fields can alter the speed and direction of signal propagation, offering potential applications in medical treatments and brain-machine interfaces. However, the study also highlights the need for more comprehensive models that consider the complexity of real-world neural circuits and the multifaceted nature of magnetic fields. The interaction between EMFs and neural circuits is a rich and complex subject that requires a multidisciplinary approach, combining physics, biology, mathematics, and computer science. While this study provides valuable insights, further research is needed to fully understand the implications of this interaction and to realize its potential applications in medicine, neuroscience, and technology. The findings contribute to a growing body of knowledge that bridges the gap between physics and neuroscience, opening new horizons for understanding the human mind and its connection to the physical world.

Elevated blood glucose levels, known as glycemia, play a significant role in sudden cardiac arrest, often resulting in sudden cardiac death, particularly among those with diabetes. Understanding the internal mechanisms has been a challenge for healthcare professionals, leading many research groups to investigate the relationship between blood glucose levels and cardiac electrical activity. Our hypothesis suggests that glucose-sensing biophysics mechanisms in cardiac tissue could clarify this connection. To explore this, we adapted a single-compartment, computational model of the human atrial node's action potential. We incorporated glucose-sensing mechanisms with voltage-gated sodium ion channels using ordinary differential equations. Parameters for the model were based on existing experimental studies to mimic the impact of glucose levels on atrial node action potential firing. Simulations using voltage clamp and current clamp techniques showed that elevated glucose levels decreased sodium ion channel currents, leading to a reduction in the sinoatrial node action potential frequency. In summary, our mathematical model provides a cellular-level understanding of how high glucose levels can lead to bradycardia and sudden cardiac death.

Cell mechanics is a biophysical indicator of cell state, such as cancer metastasis, leukocyte activation, and cell cycle progression. Atomic force microscopy (AFM) is a widely used technique to measure cell mechanics, where the Young modulus of a cell is usually derived from the Hertz contact model. However, the Hertz model assumes that the cell is an elastic, isotropic, and homogeneous material and that the indentation is small compared to the cell size. These assumptions neglect the effects of the cytoskeleton, cell size and shape, and cell environment on cell deformation. In this study, we investigated the influence of cell size on the estimated Young’s modulus using liposomes as cell models. Liposomes were prepared with different sizes and filled with phosphate buffered saline (PBS) or hyaluronic acid (HA) to mimic the cytoplasm. AFM was used to obtain the force indentation curves and fit them to the Hertz model. We found that the larger the liposome, the lower the estimated Young’s modulus for both PBS-filled and HA-filled liposomes. This suggests that the Young modulus obtained from the Hertz model is not only a property of the cell material, but also depends on the cell dimensions. Therefore, when comparing or interpreting cell mechanics using the Hertz model, it is essential to account for cell size.

This retrospective begins with Galvani's experiments on frogs at the end of the 18th century and his discovery of ‘animal electricity’. It goes on to illustrate the numerous contributions in the fields of physical chemistry in the second half of the 19th century (Nernst’s equilibrium potential, based on the work of Wilhelm Ostwald, Max Planck's ion electrodiffusion, Einstein's studies of Brownian motion), which led Bernstein to propose his membrane theory in the early 1900s as an explanation of Galvani’s findings and cell excitability. These processes were fully elucidated by Hodgkin and Huxley in 1952, by detailing the ionic basis of resting and action potentials, but without addressing the question of where these ions passed. The emerging question of the existence of ion channels, widely debated over the next two decades, was finally accepted and, a decade later, many of them began to be cloned. This led to the possibility of modelling the activity of individual neurons in the brain and then of simple circuits. Taking advantage of the remarkable advances in computer science in the new millennium, together with a much deeper understanding of brain architecture, more ambitious scientific goals were dreamed of to understand the brain and how it works. The retrospective concludes by reviewing the main efforts in this direction, namely the construction of a digital brain, an in silico copy of the brain that would run on supercomputers and behave just like a real brain.

Reviewed are recent efforts leading to chemo-enzymatic syntheses of strongly fluorescent ribosides of nucleobase analogs, potentially applicable in analytical biochemistry and cell biology. Described are syntheses and properties of fluorescent purine, 8-azapurine and etheno-purine ribosides, obtained using various types of the purine nucleoside phosphorylase (PNP) as catalysts of ribosylation, and -ribose-1-phosphate (r1P) as a second substrate. In several instances, the ribosylation sites were different than the canonical purine N9. Some of the obtained ribosides show fluorescence yields close to 100%. Their main applications are assays of PNP, nucleoside hydrolases, and other enzyme activities both in vitro and, possibly, within the living cells, using fluorescence microscopy.

The identification of an action principle for directing basic system energy exchanges can be used in a general framework for the study of dynamics in systems. The application of such action principles using Lagrangian procedures has become a very powerful methodology for the analysis of complicated problems in modern physics. This form of analytics has recently been considered as the mapping of the system state information in the context of system constraints. The process of translating perceived information into adaptive responses is considered to be the driving action dynamic for the living system. Reconciliation processes that provide for a minimization of the divergence of this information signature from a state of system stability are preferred. Such a state is naturally that of maximal entropy in the context of the operational constraints of the open living system. A mathematical expression incorporating inferential information entropy processing into biologic replicator dynamics describes the mechanics of the perception-action adaptive processes of living systems. From this framework, Kullback’s Principle of Minimum Information Discrimination is identified as the innate axiomatic action principle for guiding the dynamics of biological systems.

Semaglutide is a potent GLP-1 receptor agonist used in the treatment of type 2 diabetes mellitus due to its ability to regulate blood glucose levels and promote weight loss. On July 26, 2021, with a manually defined set of computational structural and biophysical analysis, a simple Val27-Arg28 exchange was for the first time introduced in the backbone of semaglutide to strengthen the semaglutide-GLP-1R binding affinity. In this article, a comprehensive structural and biophysical analysis approach is for the first time proposed towards an exhaustive exploration of the semaglutide-GLP-1R sequence space for the design of semaglutide analogues with elevated binding affinity to GLP-1R, thereby potentially enhancing therapeutic efficacy of structurally conceivable semaglutide analogues. Through structure biophysics-based rational design and computational modeling, this article puts forward a set of semaglutide analogues and calculated their binding affinities to GLP-1R, with one particular semaglutide analogue-GLP-1R structural model reaching a K_d of 3.0 × 10^-8 M, while the K_d is 3.4 × 10^-6 M for the binding of native semaglutide to GLP-1. Overall, the computationally designed semaglutide analogues here constitute a hopeful approach for developing GLP-1 receptor agonists with improved efficacy for the treatment of diabetes and weight management in future.

Corneal biomechanics is a hot topic in ophthalmology. The biomechanical properties of the cornea (BMPs) have important implications in the management and diagnosis of corneal diseases such as ectasia and keratoconus. In addition, the characterization of BMPs is crucial to model the predictability of a corneal surgery intervention, the outcomes of refractive surgery or follow-up of corneal diseases. The biomechanical behavior of the cornea is governed by viscoelastic properties that allow, among other structural implications, the damping of excess of intraocular pressure and reduce damage to the optic nerve. Currently, the most versatile and complete methods to measure corneal viscoelasticity are based on air-puff corneal applanation. However, those methods lack the ability to directly measure corneal viscosity. The aim of this work is to propose a new methodology based on the analysis of corneal air-puff measurements through the Standard Linear Solid model (SLSM) to provide analytical expressions to separately calculate the elastic and time-dependent (corneal retardation time and viscosity) properties. The results shown the mean values of elasticity (E), viscosity (Ƞ) and corneal retardation time (τ) in a sample of 200 young and healthy subjects. The influence of elasticity and viscosity on viscoelasticity, high-order corneal aberrations and optical transparency is investigated. Finally, the SLSM fed back from experimental E and Ƞ values was employed to compare the creep-relaxation response between normal, an ocular hypertension patient and Ortho-K user.

Thermodynamics of protein-ligand interactions seems to be associated to a narrow range of Gibbs Free Energy. As a consequence, a linear enthalpy-entropy relationship showing an apparent Enthalpy-Entropy compensation (EEC) is frequently observed associated to the study of protein-ligand interactions. This EEC affects negatively the design and discovery of new and more efficient drugs capable of binding protein-targets with a higher affinity. Originally attributed to experimental errors, compensation between H and TS values is a real observable fact, although its molecular origin has remained obscure and controversial. Herein we show the results of a data search of G values of 2558 protein-ligand interactions and 3025 “in vivo” ligand concentrations from the Protein Data Bank bind Database and the Metabolome Data Base (2020). These results, together with the mechano-statistical interpretation of the thermodynamic properties leads to the conclusion that the EEC has no basis in statistical thermodynamics. It can be plausibly explained as a consequence of the narrow range of G associated to protein-ligand interactions. The Gaussian distribution of the G values matches very well with that of ligands. These results suggest the hypothesis that the set of G values for the protein-ligand interactions is the result of the evolution of proteins. The conformation versatility of present proteins and the exchange of thousands (even millions) of minute amounts of energy with the environment may have functioned as a homeostatic mechanism to make G of proteins adaptive to changes in availability of ligands, and therefore achieve the maximum regulatory capacity of the protein function. Finally, a plausible strategy to avoid the EEC is suggested.

Methionine oxidation to the sulfoxide form (MSox) is a poorly understood post-translational modification of proteins associated with nonspecific chemical oxidation from reactive oxygen species (ROS) whose chemistries are linked to various disease pathologies including neurodegeneration. Emerging evidence shows MSox site occupancy is in some cases under enzymatic regulatory control mediating cellular signaling including phosphorylation and/or calcium signaling, raising questions as to the speciation and functional nature of MSox across the proteome. The 5XFAD lineage of the C57BL/6 mouse has well-defined Alzheimer’s and aging states. Using this model, we analyzed age, sex and disease dependent MSox speciation in mouse hippocampus. In addition, we explore chemical stability and statistical variance of oxidized peptide signals to understand needed power for MSox based proteome studies. Our results identify mitochondrial and glycolytic pathway targets with increases in MSox with age as well as neuroinflammatory targets accumulating MSox with AD in proteome studies of mouse hippocampus. Further, the paper establishes a foundation for reproducible and rigorous experimental MSox-omics appropriate for biological discovery novel target identification, and biomarker analysis in ROS and other oxidation linked diseases.

Aβ peptides are known to bind neural plasma membranes in a process leading to the deposit of Aβ-enriched plaques. These extracellular structures are characteristic of Alzheimer’s disease, the major cause of late-age dementia. The mechanisms of Aβ plaque formation and deposition are far from being understood. A vast amount of literature describes the efforts to analyze those mechanisms, using a variety of tools. The present review focuses on biophysical studies, mostly carried out with model membranes, or with computational tools. The review starts by describing basic physical aspects of lipid phases, and commonly used model membranes (monolayers and bilayers). This is followed by a discussion of biophysical techniques applied to these systems, mainly but not exclusively Langmuir monolayers, isothermal calorimetry, density-gradient ultracentrifugation, and molecular dynamics. The methodological section is followed by the core of the review, a summary of important results obtained with each technique. The last section is devoted to an overall reflection and an effort in understanding Aβ-bilayer binding. Concepts such as Aβ peptide membrane binding, adsorption, and insertion are defined and differentiated. The roles of membrane lipid order, nanodomain formation, and electrostatic forces in Aβ-membrane interaction are separately identified and discussed.

Insulin therapy is a cornerstone in the management of diabetes, yet the pursuit of optimizing its pharmacokinetic profile remains a focal point in diabetes research. For instance, insulin icodec of Novo Nordisk is a novel long-acting insulin analogue that exhibits an extended duration of action, providing a promising once-weekly treatment option for diabetic patients. However, designing insulin analogues with lower receptor affinity than that of insulin icodec could still produce a further extended duration of action than that of insulin icodec through the suppression of receptor-mediated internalization of insulin analogues. In this study, therefore, I present the design of a novel series of insulin analogues engineered towards lower binding affinity to the insulin receptor compared to that of insulin icodec. Through computational structural biophysics-based rational design strategies, this article aims to further extend the duration of action while maintaining therapeutic efficacy of insulin analogues. Utilizing homology molecular modeling and structural biophysics-based binding affinity calculations, this article puts forward a set of insulin analogues with lower binding affinity to insulin receptor than that of insulin icodec from a structural and biophysical point of view. Overall, this article calls for subsequent in vitro and in vivo evaluations of the efficacy and prolonged action of the engineered insulin analogues to pharmacokinetically test whether these analogues acutally surpass that of insulin icodec as hopeful candidates for next-generation insulin analogue therapies with improved duration of action and enhanced control of blood glucose levels for diabetic patients in future.

Elevated blood glucose levels, known as glycemia, play a significant role in sudden cardiac arrest, often resulting in sudden cardiac death, particularly among those with diabetes. Understanding this link has been a challenge for healthcare professionals, leading many research groups to investigate the relationship between blood glucose levels and cardiac electrical activity. Our hypothesis suggests that glucose-sensing mechanisms in cardiac tissue could clarify this connection. To explore this, we adapted a single-compartment, in-silico model of the human atrial node's action potential. We incorporated glucose-sensing mechanisms with voltage-gated sodium ion channels using ordinary differential equations. Parameters for the model were based on existing experimental studies to mimic the impact of glucose levels on atrial node action potential firing. Simulations using voltage clamp and current clamp techniques showed that elevated glucose levels decreased sodium ion channel currents, leading to a reduction in the sinoatrial node action potential frequency. In summary, our model provides a cellular-level understanding of how high glucose levels can lead to bradycardia and sudden cardiac death.

Contrary to previous belief, mutation frequency also depends on how essential a gene is. Mutation frequency is substantially lower in the coding regions of essential genes and higher in less essential genes. Even though the mutation bias based on gene essentiality is incapable of invalidating Darwinism, this bias certainly added a rather unsubtle obstacle to it by contradicting a paradigm that was widely held: that mutations are random in the sense of being independent of their function for the organism. The reason this bias occurs is currently unknown. Here, I present the theoretical solution of why genetic mutations also depend on whether the gene is more or less essential. My theory is that each gene or epigene has its own level of resistance to entropic elevation, something directly proportional to the frequency of its expression. The more a gene is expressed, the higher is its resistance to entropic elevation and vice versa. For genetic information to be conserved instead of suffering mutation, a relevant factor is also the frequency of its expression. The energy expenditure directed to a gene of current utility engenders resistance to entropic elevation of the information encoded in that gene, as energy enables not only the existence of information but also its conservation. My theory—which is based on fundamental physics and contains predictions that can be empirically tested—solves a crucial problem in evolutionary biology and genetics; if I am right, I will have established a biological law. Finally, my aim here is also to provide a substantial advance in knowledge about the relationship between information, genetics, and thermodynamics.

This article encapsulates an exploration of seizure dynamics through the lens of computational modeling in epilepsy. Starting with a foundational understanding of the FitzHugh-Nagumo model, asimplified representation of neuronal activity, we try to understand the intricacies of epileptic seizures. We methodically demonstrated the transition from normal neuronal activity to a seizure-like state by altering key parameters in the model, such as external current and coupling strength. This was followed by an extension of the model to a network of neurons, simulating the complex interactions and synchronization patterns indicative of seizure propagation. Numerical simulations were conducted to visualize the impact of varying coupling strengths on network dynamics, offering insights into the mechanisms of seizure initiation and spread. The study was complemented by a discussion on the implications of these findings for understanding epilepsy, highlighting the bridging of theoretical models with clinical understanding. Our approach not only illuminates the potential of computational models in epilepsy research but also underscores the significance of interdisciplinary collaboration in advancing our comprehension of neurological disorders. Through this article, we aim to provide a nuanced perspective on the modeling of epileptic seizures, offering a valuable resource for researchers and clinicians in the field.

For the first time, the effect of a significant change in the resistance of the aquatic environment of a population of protozoan microorganisms under the influence of radiofrequency electromagnetic radiation from cell phones has been experimentally studied. The results of the experiment demonstrate the biological nature of the changes occurring in the “water population of protozoa” system, which indicates that the impact of radiation directly affects the cell. This conclusion is confirmed by the same order of magnitude time of information exchange between individual individuals of the population in the calculation and in the experiment. An extremely low concentration of protozoa in the experiment was established, equal to 1000 pcs./liter, at which the indicated effect is observed.An unambiguous dependence of the biological effect in the “water – amoeba population” system on the average power of electromagnetic radiation of cell phones, both in standby mode and in talk mode, has been established. It is concluded that the presented method of changing the physical properties of the aquatic environment of a population of protozoa under the influence of electromagnetic fields can be a model experiment in identifying the mechanisms of interaction of biological systems with electromagnetic radiation, determining the response time of biological systems to external electromagnetic influence, and establishing the degree of influence of radiation in the 4G, 5G ranges.

Due to the vast conformational space proteins can adopt, accurate and efficient prediction of protein structure remains still a challenging task, coupled with the intricacies of interatomic interactions and the limitations of current computational models in effectively navigating this complex molecular landscape. Additionally, the lack of comprehensive experimental data for all protein structures further exacerbates the difficulty in reliable machine learning-based prediction of the three-dimensional conformations of the proteios building block of life. Geometrically, Cartesian coordinate system (CCS, X, Y and Z) and spherical coordinate system (SCS, ρ, θ and φ) are two interconvertible coordinate systems, and are like two sides of one coin. Since the beginning of Protein Data Bank (PDB) in 1971, CCS has been the default approach to specify atomic positions with X, Y and Z in PDB. In this manuscript, therefore, I present a novel method for the reversible spherical geometric conversion of protein backbone structure coordinate matrices to three independent vectors: ρ, θ and φ. This reversible conversion facilitates lossless extraction of essential structural features from protein backbone structural data, enabling the development of advanced novel algorithms for protein structure prediction in future. In short, this inter-atomic SCS approach offers a comprehensive yet efficient means of representing protein backbone geometry, leveraging spherical coordinates to capture spatial relationships in a compact and intuitive inter-atomic manner, and to provide a robust framework for reversible feature extraction for the ongoing efforts in advancing the field of protein structure prediction, the holy grail of computational structural biology.

An imbalance between production and excretion of amyloid β peptide (Aβ) in the brain tissues of Alzheimer’s disease (AD) patients leads to Aβ accumulation and formation of noxious Aβ oligomers/plaques. A promising approach to AD prevention is reduction of free Aβ level by directed enhancement of Aβ binding to its natural depot, human serum albumin (HSA). We previously demonstrated the ability of specific low-molecular-weight ligands (LMWLs) of HSA to improve its affinity for Aβ. Here we develop this approach by bioinformatic search for the clinically approved AD-related LMWLs of HSA, followed by classification of the candidates according to the predicted location of their binding sites on HSA surface, ranking of the candidates, and selective experimental validation of their impact on HSA affinity for Aβ. The top 100 candidate LMWLs were classified into the five clusters. The specific representatives of the different clusters exhibit dramatically different behavior, with 3- to 13-fold changes in equilibrium dissociation constants for the HSA-Aβ40 interaction: prednisone favors HSA-Aβ interaction, mefenamic acid shows the opposite effect, while levothyroxine exhibits the bidirectional effects. Overall, the LMWLs of HSA chosen here provide a basis for drug repurposing for AD prevention, and for search for the medications promoting AD progression.

Histone deacetylases inhibitors (HDACi) have shown promising therapeutic outcomes in haematological malignancies such as leu-kaemia, multiple myeloma and lymphoma, with dis-appointing results in solid tumours when used as monotherapy. As a result, combination therapies either with radiation or other deoxyribonucleic acid (DNA) damaging agents have been suggested as ideal strategy to improve their efficacy in solid tumours. Numerous in vitro and in vivo studies have demonstrated that HDACi can sensitise malignant cells to both electromagnetic and particle types of radiation by inhibiting DNA damage repair. Although the radiosensitising ability of HDACi has been reported as early as 1990s, the mechanisms of radiosensitisation are yet to be fully understood. This review brings forth the various protocols used to sequence the administration of radiation and HDACi treatments in the different studies. The possible contribution of these various protocols on the ambiguity that surrounds the mechanisms of radiosensitisation is also highlighted.

Plant cells' ability to withstand abiotic stress is strongly linked to modifications in their mechanical characteristics. Nevertheless, the lack of a workable method for consistently tracking plant cell mechanical properties severely restricts our comprehension of the mechanical alterations in plant cells under stress. With Polyethylene Glycol (PEG6000), we created a drought-like environment, and we used the Double Resonator Piezoelectric Cytometry (DRPC) method to dynamically and non-invasively track changes in the stress (ΔS) created and viscoelasticity (storage modulus G' and loss modulus G") of protoplasts and suspension cells of rice during drought stress. The findings demonstrate that, rice suspension cells and protoplasts react mechanically differently to 5%–15% PEG6000 stress, implying distinct resistance mechanisms. However, neither of them can withstand 25% PEG6000 stress, they respond mechanically similarly to 25% PEG6000 stress. The results of DRPC are further corroborated by the morphological alterations of rice cells and protoplasts observed under an optical microscope. To sum up, the DRPC technique functions as a precise cellular mechanical sensor and offers novel research tools for the evaluation of plant cell adversity and differentiating between the mechanical reactions of cells and protoplasts under abiotic stress.

As a key mediator of chronic pain, neurotrophin nerve growth factor (NGF) binds to two neurotrophin receptors: p75 neurotrophin receptor (p75NTR) and tyrosine kinase receptor A (TrkA). The formation of the p75NTR-NGF-TrkA complex is implicated in the potentiation of chronic pain signals, making it an attractive target for therapeutic explorations. NGF analogues and monoclonal antibodies (mAbs) targeting NGF represent two distinct approaches in modulating the intricate signaling pathways involved in pain transduction mediated by the p75NTR-NGF-TrkA complex. While NGF analogues offer the advantage of tailored design to fine-tune neurotrophic responses, monoclonal antibodies provide a more systemic and comprehensive blockade of NGF, inhibiting its interactions with both p75NTR and TrkA receptors. However, the use of mAbs may pose challenges related to potential side effects and interference with the physiological functions of NGF. As a result, balancing the benefits and drawbacks of the two approaches is critical for advancing therapeutic strategies towards the alleviation of p75NTR-NGF-TrkA-mediated pain. In this study, therefore, a novel structural and biophysical approach was employed for the design of NGF analogues to suppress p75NTR-NGF-TrkA-related chronic pain signaling. Employing high-throughput structural modeling and biophysics-based intermolecular binding affinity calculations, this article for the first time puts forward a set of NGF analogues, including in particular NGF analogues with four site-specific mutations, for whose dimerizations the K d at 37 ^◦C were reduced by three orders of magnitude (from 10^-9 M to 10^-6 M) compared to the K d at 37 ^◦C for the dimerization of native NGFs. Overall, the integration of structural and biophysical perspectives enhances our understanding of the rational design of NGF analogues as promising candidates for the development of NGF-targeted analgesic therapies, which balances the benefits and drawbacks of anti-NGF antibodies and NGF analogues for advancing therapeutic strategies towards the alleviation of p75NTR-NGF-TrkA-mediated pain.

Hemodynamic relevance of differently located stenoses of the internal jugular veins remains undetermined. It particularly concerns nozzle-like strictures in the upper parts of these veins and stenotic jugular valves located at the end of this vein. This study was aimed at understanding flow disturbances caused by such stenoses. Computational fluid dynamics software, Flowsquare+, was used. We constructed 3-dimensional models of venous outflow, comprising two alternative routes: tube representing the internal jugular vein and irregular network representing the vertebral veins. At the beginning of the tube representing the internal jugular vein, differently-shaped and sized short strictures, representing nozzle-like strictures were built in. At the end of this tube, differently-shaped membranes, representing the jugular valve, were built in. With the use of computational fluid dynamics modeling, we studied how these two obstacles influenced the outflow. We found that the most relevant outflow disturbances were evoked by nozzle-like strictures in the upper part of the internal jugular vein that were either small, long or asymmetrically positioned. Very tight stenotic valves and septum-like malformed valve were equally hemodynamically relevant. These findings suggest that both upper and lower strictures of the internal jugular vein can be of clinical significance.

Sialylated milk oligosaccharides (SMOs) is the most important oligosaccharides in human milk oligosaccharides (HMOs), and have a multifunctional health benefit, and the most abundant SMOs are sialyllactose (SLs). The previous studies indicate that the growth of tumor cells, and angiogenesis on tumor tissues with Lewis lung carcinoma, melanoma, and colon carcinoma cells could be prevented by SL. Thus, it has been proposed that SL might be an ideal candidate for the development of anti-angiogenic drugs without any side effects. It has been proposed that the Sialic acid (Sia) can be produced through SL’s hydrolysis, and the dietary Sia is further transported to the brain and other tissues though blood for formation of CMP-Sia, the activated precursor of Sia, and then is catalyzed by polysialyltransferases (polySTs) for synthesis of polysialic acid (polySia) or polysialylation of neural cell adhesion molecule (NCAM). Thus, it’s necessary to study and develop new inhibitors, which not only modulate a proper balance between the gut health and neuros system development, but also inhibit the overexpression of polySia, and thus block the tumor cell migration. In this study, we proposed that 3’-SL and 6’-SL may be more ideal inhibitors comparing with two previous inhibitors, the low molecular weight heparin (LMWH) and cytidine monophosphate (CMP) in the previous studies.

Globally, cancer is an important health problem worldwide, and has become one modern medicine’s most serious problems to prevent, diagnose, and treat. Many types of cancers still have non-negligible mortality rates, and new treatments need to be developed for them. This study aimed to elucidate cancer mechanisms and facilitate cancer treatment and prevention. The ideal temperature for cancer growth is 35°C; however, conditions were not consistent with chromosomal instability, loss of alleles of tumor-suppressor genes, abnormal DNA methylation, and collagen phagocytosis, which are known causes of cancer. In addition, when cancer develops, white blood cells attack the cancer tissue and remove it; however, as the decrease in body temperature drops, leads to a reduction in the immune potential of the white blood cells. Based on previous papers, this study focused on aimed to determine the effects of the low-temperature environment of 35°C, the temperature at which cancer is most likely to proliferate, and changes in adenosine triphosphate (ATP) levels. The study revealed that Therefore, the causes of cancer is caused by include chromosome fusion that occurs occurring due to the impaired DNA topoisomerase IIα proliferation and primary cilia rotation due to resulting from decreased intercellular adhesion.

The evolution of human societal dynamics over the course of decades and centuries has led to an ever-increasingly complex digital modern life, one of the hallmark of which is the need for multi-tasking, inclusive of that with media. Is there an empirical mapping between this macroscopic (digital) evolution of the human societal dynamics (in terms of multi-tasking) and the microscopic evolution of the functional molecular units within cells (i.e., proteins) and their manifold cross-talks? Multi-tasking requires a certain degree of intrinsic or adapted flexibility which is as true at the bio-molecular level. Evolutionary diversification of structure-function relationship in proteins emphasizes the functional importance of intrinsically disordered entities (IDPs/IDRs) which are highly dynamic biological soft matters. Multi-functionality is favorably supported by their fluid-like shapes and other new malleable members of the protein family. The new wealth of knowledge coming from recent research on protein intrinsic disorder, fold-switching, moonlighting, hub proteins etc. has given us new insights into protein structure – functional relationship. This has led to a paradigm shift in protein science, clearly diverging from the traditional 'one structure – one function' model applicable to enzyme classes to a more complex understanding of protein functionality. This paradigm shift has caused scientists to delve deeper into the subject, exploring various evolutionary toys and tools to fit the cumulative multi-functional demands in higher organisms. This commentary covers the different evolutionary arsenals to achieve the growing multi-functionality and argues in favor of protein intrinsic disorder as probably the sharpest weapon of all.

By this contribution we would like to briefly highlight the problems in conceiving the “Hydrogen Bond” (HB) as a real short-range, directional, electrostatic, attractive interaction and to reframe its nature through the non-approximated view of condensed matter offered by a Quantum Electro-Dynamic (QED) perspective. We focus our attention on water, as the paramount case to show the effectiveness of this 40-years’ old theoretical background, which depicts water as a two-fluid system (where one of the two phases is coherent). The HB emerge to be the result of the electromagnetic field gradient in the coherent phase of water, whose vacuum level is lower than the non-coherent (gas-like) fraction. Thus, the HB can be properly looked at, i.e. no more as a “dipolar force” among molecules, but as the phenomenological effect of their collective thermodynamic tendency to occupy a lower ground-state, compatibly with temperature and pressure. This perspective allows to account for many “anomalous” behaviours of water and to understand why the calculated energy associated to HB should change when considering two molecules (water-dimer), or the liquid state, or the several kinds of ice. The emergence of a condensed, liquid, phase at room temperature is indeed the consequence of the boson condensation as described in the framework of spontaneous symmetry breakings (SSB). The switch from a still semi-classical Quantum Mechanical (QM) in the 1st quantization to a Quantum Field Theory (QFT) view embedded in the 2nd quantization is advocated for a more realistic and authentic description of water, condensed matter and living systems.

Voltage-gated potassium (Kv) channels and hyperpolarization-activated cyclic nucleotide-gated (HCN) channels share similar structures but have opposite gating polarity. Kv channels have a strong coupling ( >109) between the voltage sensor (S4) and the activation gate: when S4s are activated the gate is open to >80%, but when S4s are deactivated, the gate is open < 10-9 of the time. Using noise analysis, we show that the coupling between S4 and the gate is < 200 in HCN channels. In addition, using voltage clamp fluorometry, locking the gate open in a Kv channel drastically altered the energetics of S4 movement. In contrast, locking the gate open or decreasing the coupling between S4 and the gate in HCN channels had only minor effects on the energetics of S4 movement, consistent with a weak coupling between S4 and the gate. We propose that this loose coupling is a prerequisite for the reversed voltage gating in HCN channels.

Glycerol represents a useful functional component of heat-transfer fluids. Glycerol is known to be triboelectrically active. We report how the flow of glycerol, stopped in a cylindrical coiled pipe, influences horseradish peroxidase (HRP) enzyme. Namely, glycerol flow was pumped through the coil and then stopped. After that, we incubated the 0.1 µM solution of HRP near the side of the coil, and then adsorbed HRP onto mica. This operation markedly increased the HRP aggregation on mica in comparison with the control sample. We explain this phenomenon by the influence of triboelectrically induced electromagnetic fields. Our results can be useful in the development of flow-based systems for biosensors and bioreactors.

Hox gene clusters are crucial in Embryogenesis. It was observed that some Hox genes are located in order along the telomeric to centromeric direction of the DNA sequence: Hox1, Hox2, Hox3…. These genes are expressed in the same order in the ontogenetic units of the Drosophila embryo along the Anterior-Posterior axis. The two entities (genome and embryo) differ significantly in linear size and in-between distance. This strange phenomenon was named Spatial Collinearity (SP). Later, it was observed that, particularly in the Vertebrates, a Temporal Collinearity (TC) coexists: first is Hox1 expressed, later Hox2 and even later Hox3,…,. According to a Biophysical Model (BM), pulling forces act at the anterior end of the cluster while a cluster fastening applies at the posterior end. Hox clusters are irreversibly elongated along the force direction. During Evolution, the elongated Hox clusters are broken at variable lengths thus split clusters may be created. An Empirical Rule was formulated distinguishing development due to a complete Hox cluster from development due to split Hox clusters. BM can ‘explain’ this Empirical Rule. In a spontaneous mutation where the cluster fastening is dismantled, a weak pulling force automatically shifts the cluster inside the Hox activation domain. This cluster translocation can probably explain the absence of Temporal Collinearity in Drosophila.

From a structural point of view, understanding interfacial electrostatic features of ligand-receptor complexes is crucial for biophysics-based design of therapeutics with improved efficacy and safety. Here, with a high-throughput structural feature extraction approach, this article reports a set of electrostatic structural features from glucagon-like peptide-1 (GLP-1) and GLP-1 receptor (GLP-1R) complex structures determined using experimental tools. Leveraging a set of computational structural biophysical analyses, this article systematically characterized the electrostatic interactions within the GLP-1-GLP-1R complexes with two sets of criteria to identify interfacial salt bridges and hydrogen bonds. Overall, the results here reveal intricate details of electrostatic interactions critical for GLP-1 binding to its receptor GLP-1R, providing biophysical insights into the stabilization of the GLP-1-GLP-1R complex structure and the molecular basis of GLP-1/GLP-1R signaling. Furthermore, the electrostatic structural features extracted from GLP-1 and GLP-1R complex structures are useful in the development of a machine learning-based ligand-receptor binding affinity calculating model, i.e., a GLP-1-GLP-1R-specific general intermolecular binding affinity calculator (GIBAC), paving the way for accelerated discovery and structure-based design of drug candidates targeting the GLP-1/GLP-1R system with improved efficacy and safety.

This study investigates the metabolic parallels between stimulated pancreatic beta cells and cancer cells, emphasizing glucose and glutamine metabolism. Addressing the significant public health challenges of Type 2 Diabetes Mellitus (T2DM) and cancer, we aim to deepen understanding of the mechanisms driving insulin secretion and cellular proliferation. Our analysis of anaplerotic cycles and NADPH's role in biosynthesis elucidates their vital functions in both processes. Additionally, we find that both cell types share an antioxidative response mediated by the Nrf2 signaling pathway, glutathione synthesis, and UCP2 upregulation. Notably, UCP2 facilitates the transfer of C4 metabolites, enhancing reductive TCA cycle metabolism. We also uncover that hypoxic responses are transient in beta cells post-stimulation but persistent in cancer cells. By synthesizing these insights, our research suggests novel therapeutic targets for T2DM, highlighting the shared metabolic strategies of stimulated beta cells and cancer cells. This comparative analysis not only illuminates the metabolic complexity of these conditions but also emphasizes the crucial role of metabolic pathways in cell function and survival, offering fresh perspectives for tackling T2DM and cancer challenges.

Giant unilamellar vesicles (GUVs) are membrane model used to study membrane properties. Electroformation is one of the methods used to produce GUVs. During electroformation protocol dry lipid film is formed. Drying of the lipid film induce Cholesterol (Chol) demixing artifact, in which Chol forms anhydrous crystals, where these crystals does not participate in formation of vesicles. This leads to a lower Chol concentration in the vesicle bilayers compared to the Chol concentration in the initial lipid solution. To address this problem, we propose novel electroformation protocol that includes rapid solvent exchange, plasma cleaning and spin-coating methods to produce GUVs. We tested the protocol focusing on vesicles with high Chol content using different spin-coating durations and vesicle types deposition. Additionally, we compared the novel protocol using completely dry lipid film. The optimal spin-coating duration for vesicles made out of phosphatidylcholine/Chol mixture was 30 s. Mulitlamellar vesicles (MLVs), large unilamellar vesicles (LUVs) obtained by extrusion of MLVs through 100 nm membrane pores and LUVs obtained by extrusion of previously obtained LUVs through 50 nm membrane pores were deposited on electrode for 1.5/1 Chol/phosphatidylcholine lipid mixture and the results were compared. Electroformation using all three deposited vesicle types gave a high GUVs yield, but deposition of LUVs obtained by extrusion of MLVs through 100 nm membrane pores gave the most reproducible results. Using deposition of these LUVs, we produced GUVs with high yield for 6 different Chol concentrations (from 0 % to 71.4%). Using protocol that includes dry lipid film GUVs yield was lower and Chol demixing artifact was induced, proving that the lipid film should never undergo drying when Chol content is high.

The physiological mechanism of membrane potential generation relies on the passage of mobile ions across the plasma membrane, which is selectively permeable to mobile ions. The existence of a membrane and the transport of ions across it are essential for the generation of membrane potential. However, a long-forgotten theory, Association-Induction hypothesis, asserts that the heterogeneous distribution of charges in space, due to ion adsorption, is the true cause of membrane potential generation. Using an artificial living cell model consisting of a viscous aqueous solution, the mechanism of membrane potential generation is restudied in this work. The results indicate that membrane potential can be generated even in the absence of a plasma membrane and that ion adsorption leading to a heterogeneous spatial charge distribution is an essential factor for membrane potential generation. Consequently, the authors invites people to reconsider the Association-Induction hypothesis as a genuine mechanism for generating membrane potential.

Amylin, also known as islet amyloid polypeptide (IAPP), is a metabolic homeostasis-related hormone that is produced and released by the β cells of the pancreas, the same cells that produce insulin. Insulin-degrading enzyme (IDE) is a protease enzyme that plays an essential role in the breakdown and degradation of various peptides, including insulin and amylin. Direct binding & interaction between amylin and IDE is inextricably linked to the degradation of amylin, and research and development effort in this area is crucial to understand the potential therapeutic implications of disrupting the IDE-amylin interaction in the context of conditions where metabolic homeostasis needs to be regulated, such as diabetes and obesity. Here, this article incorporates currently available experimental complex structure of amylin and IDE, and delves deep into the interstructural biophysics underlying the binding interface of the two interacting partners. With a set of comprehensive structural biophysical analysis, this article identified an intriguing region of high electrostatic potential indicative of strong binding sites between the first N-terminal lysine (Lys1, K1) residue of amylin and Glu341 (E341) of IDE. This unique electrostatic hotspot presented herein paves the way for the rational design of drug-like small molecules that can selectively disrupt this interaction, offering a targeted therapeutic strategy for improved metabolic homeostasis, particularly for patients with diabetes and obesity.

Despite being small and simple structured in comparison to their victims, virus particles have the potential to harm severly and even kill highly developed species such as humans. To face upcoming virus pandemics, detailed quantitative biophysical understanding of intracellular virus replication mechanisms is inevitable. Unveiling the relationship of form and function might allow to determine putative attack points relevant for the systematic development of direct antiviral agents (DAA) and potent vaccines. Biophysical investigations of spatio-temporal dynamics of intracellular virus replication so far are rare. We are developing a framework to allow for fully spatio-temporal resolved virus replication dynamics simulations based on partial differential equations models (PDE) and evaluated with advanced numerical mathematical methods on leading supercomputers. This study presents an advanced highly nonlinear model of the genome replication cycle of a specific RNA virus, the Hepatitis C virus (HCV). The diffusion-reaction model mimics the interplay of the major components of the viral RNA (vRNA) cycle, namely non structural viral proteins (NSP), vRNA and a generic host factor. Technically, we couple surface PDEs (sufPDEs) on the 3D embedded 2D Endoplasmatic Reticulum (ER) manifold with PDEs in the 3D membranous web (MW) and cytosol volume. (The MWs are the replication factories growing on the ER induced by NSPs.) The sufPDE/PDE model is evaluated at realistic reconstructed cell geometries which are based on experimental data. The simulations couple the effects of NSPs which are restricted to the ER surface with effects appearing in the volume. The volume effects include the host factor supply from the cytosol and the MW dynamics. Special emphasis is put to the exchange of components between ER surface, MWs and cytosol volume. As the vRNA spatial properties are not fully understood so far in experiment, the model allows for vRNA both restricted to the ER and moving in the cytosol. The visualization of the simulation resembles a look into some sort of fully 3D resolved “in silico microscopes” to mirror and complement in vitro / in vivo experiments for the intracellular vRNA cycle dynamics. The output data are quantitatively consistent with experimental data and provoke advanced experimental studies to validate the model.

Advanced methods of treatment are needed to fight the threats of virus-transmitted deseases and pandemics. Often they are based on an improved biophysical understanding of virus replication strategies and processes in their host cells. For instance, an essential component of the replication of the Hepatitis C virus (HCV) proceeds under the influence of non-structural HCV proteins (NSPs) that are anchored to the endoplasmatic reticulum (ER), such as the NS5a protein. The diffusion of NSPs has been studied by in-vitro fluorescence recovery after photobleaching (FRAP) experiments. The diffusive evolution of the concentration field of NSPs on the ER can be described by means of surface partial differential equations (sufPDE). Knodel et al, Viruses 2018, 10, 28 estimated the diffusion coefficient of the NS5a protein by minimizing the discrepancy of an extended set of sufPDE simulations and experimental FRAP time series data. Here, we provide a scaling analysis of the sufPDEs that describe the diffusive evolution of the concentration field of NSPs on the ER. This analysis provides an estimate of the diffusion coefficient that is based only on the ratio of the membrane surface area in the FRAP region and its contour length. The quality of this estimate is explored by comparison to numerical solutions of the sufPDE for a flat geometry and for ten different 3D embedded 2D ER grids that are derived from fluorescence z-stack data of the ER. Finally, we apply the new data analysis to the experimental FRAP time-series data analyzed in our previous paper, and we discuss the opportunities of the new approach.

Membrane protein integrase (MPIase), an endogenous glycolipid in Escherichia coli (E. coli) membrane, is essential for membrane protein insertion in E. coli. We have examined Sec-independent membrane protein insertion mechanisms facilitated by MPIase using physicochemical analytical techniques such as solid-state nuclear magnetic resonance, fluorescence measurements, and surface plasmon resonance. In this review, we outline the physicochemical characteristics of membranes that may affect membrane insertion of proteins. Subsequently, we introduce our results verifying the effects of membrane lipids on insertion and estimate the impact of MPIase. Although MPIase is a minor component of E. coli membranes, it regulates insertion by altering the physicochemical properties of the membrane. In addition, MPIase promotes insertion by interacting with substrate proteins. We propose comprehensive mechanisms of membrane insertion of proteins involving MPIase, which provide a physicochemical basis for understanding the roles of glycolipids in protein translocation.

Microcrystal delivery instruments are pivotal to perform serial femtosecond crystallography experiments at the XFEL facilities. We present a novel sample delivery technique based on a micro-tubing reeling system (MRS). Even though the tiny size of the micro-tubing, the MRS device has an advantage of operation without real-time position adjustment of the tube to match with the XFEL pulses. Moreover, the applicable repetition rate is more flexible than the previously reported chip-based one-dimensional fixed target system.

Uncoupling protein 3 (UCP3) belongs to the mitochondrial carrier protein superfamily SLC25 and is abundant in brown adipose tissue (BAT), heart and muscles. Unlike UCP1, which is localized in BAT and involved in non-shievering thermogenesis, its biological function is under dispute. The expression of UCP3 in tissues mainly dependent on fatty acid oxidation suggests its involvement in cellular metabolism and has drawn attention to its possible transport function beyond the transport of protons in the presence of fatty acids. Based on the high homology between UCP2 and UCP3, we hypothesized that UCP3 transports C4 metabolites similar to UCP2. To test this, we measured the transport of substrates against phosphate (32Pi) in proteoliposomes reconstituted with recombinant murine UCP3. We found that UCP3 mainly transports aspartate and sulfate but also malate, malonate, oxaloacetate and succinate. The transport rates calculated from the exchange of 32Pi against extraliposomal aspartate and sulfate were 23.9 ± 5.8 and 17.5 ± 5.1 µmol/min/mg, respectively. Using site-directed mutagenesis we revealed that mutation of R84 resulted in impaired aspartate/phosphate exchange, demonstrating its critical role in substrate transport. Difference in substrate preference between UCP2 and UCP3 may be explained by their different tissue expression pattern and biological function in these tissues.

Stabilization of a G-quadruplex (G4) in the promotor of the c-MYC proto-oncogene leads to inhibition of gene expression, and it thus represents a potentially attractive new strategy for cancer treatment. However, most G4 stabilizers show little selectivity among the many G4s present in the cellular complement of DNA and RNA. Intriguingly, a crescent-shaped cell-penetrating thiazole peptide, TH3, preferentially stabilizes the c-MYC G4 over other promotor G4s, but the mechanisms leading to this selective binding remain obscure. To investigate these mechanisms at the atomic level, we performed an in silico comparative investigation of the binding of TH3 and its analogue TH1 to the G4s from the promotors of c-MYC, c-KIT1, c-KIT2, and BCL2. Molecular docking and molecular dynamics simulations, combined with in-depth analyses of non-covalent interactions and bulk and per-nucleotide binding free energies, revealed that both TH3 and TH1 can induce the formation of a sandwich-like framework through stacking with both the top and bottom G-tetrads of the c-MYC G4 and the adjacent terminal capping nucleotides. This framework produces enhanced binding affinities for the c-MYC G4 relative to other promotor G4s, with TH3 exhibiting an outstanding binding priority. Van der Waals interactions were identified to be the key factor in complex formation in all cases. Collectively, our findings fully agree with available experimental data. Therefore, the identified mechanisms leading to specific binding of TH3 towards the c-MYC G4 provide valuable information to guide the development of new selective G4 stabilizers.

Physical properties like shape, volume and size affect the dynamics of biological systems. Along these lines, we focus on the topological properties of biological fluids and their biochemical and physiological outcomes. We take as a paradigmatic example the salivary fluid and describe how its topological features may affect the physiopathology of the oral cavity. Topological approaches assess the general properties of saliva, ignoring small-scale physical details such as density, flow rate, stiffness, viscosity. Specifically, the mucin aggregates scattered in the salivary fluid can be tackled in terms of topological holes, i.e., vortical clusters that modify the direction, flow, impulse, local rate-of-change and velocity of saliva. While the current methodological approaches are inclined to remove the effects of impurities assessing systems as homogeneous structures, we argue that the occurrence of mucins breaks up the salivary fluid’s homogeneity, leading to unexpected biophysical modifications. We suggest that every collected salivary sample is not reliable for accurate clinical and experimental investigation, since it displays highly local as well as variable chemical, physical and biological features, not reflecting the current physiological state of the oral cavity. Therefore, the assessment of a single salivary sample is not fully reproducible and cannot provide information about the biophysical, enzymatic and microbiological content of the whole saliva. In sum, the very topological features of the saliva - such as volume, shape, antipodal cells, vortex area, whirling fluid mass, segmentation, discretization, triangulation, node numbering - produce unnoticed biological consequences and network connectivity features with intriguing operational implications.

Wolfenden and his coworkers observed the astronomical numbers for the catalytic proficiency of some enzymes. We connected that pinnacle of biological evolution to the universal thermodynamic evolution. We added or multiplied a random noise with chosen rate constants to explore the correlation between dissipation and enzyme efficiency for ten enzymes: beta-galactosidase, glucose isomerase, β-lactamases from three bacterial strains, ketosteroid isomerase, triosephosphate isomerase, and carbonic anhydrase I, II, and T200H. The turnover number kcat and catalytic efficiency kcat/KM are proportional to the overall entropy production – the main parameter from irreversible thermodynamics. For most enzymes with the Michaelis-Menten type cycle kinetics, the best increase in the forward kcat/KM follows after increasing the equilibrium constant of substrate-enzyme association. The Discussion section emphasizes the role of biological evolution in harvesting order (high enzyme efficiency) from disorder (high noise and dissipation). It also connects the applications of the maximum partial entropy production theorem in optimizing enzyme kinetics (D. Juretić "Bioenergetics - A Bridge across Life and Universe") with the present total entropy production role analysis. De novo enzyme design and various attempts to speed up the rate-limiting catalytic steps may profit from our theoretical insights.

Emerging applications of optical technologies are driving the development of miniaturised light sources, which in turn require the fabrication of matching micro-optical elements with sub-1 mm cross sections and high optical quality. This is particularly challenging for spatially-constrained biomedical applications where reduced dimensionality is required, such as endoscopy, optogenetics, or optical implants. Planarisation of a lens by the Fresnel lens approach was adapted for a conical lens (axicon) and was made by direct femtosecond 780 nm/100 fs laser writing in SZ2080™polymer with photo-initiator. Optical characterisation of the positive and negative fraxicons is presented. Numerical modeling of fraxicon optical performance under illumination by incoherent and spatially extended light sources is compared with the ideal case of plane wave illumination. Considering the potential for rapid replication in soft polymers and resists, this approach holds great promise for the most demanding technological applications.

Insulin icodec of Novo Nordisk is a novel long-acting insulin analogue that exhibits an extended duration of action, providing a promising treatment option for individuals with diabetes. It has been reported that the incorporation of fatty acid moieties into insulin icodec plays a crucial role in its prolonged action, as these fatty acid chains facilitate the formation of stable hexameric structures, thereby delaying insulin absorption and promoting sustained release. Yet, the underlying biophysics still is elusive of the roles of the three site-specific mutations (Y14A\_E, Y37B\_H, F46B\_H) of insulin icodec in its prolonged activity. Thus, through a comprehensive structural and biophysical analysis of the insulin (both native and icodec) structures bound to its receptor, this article delves deep into the biophysics underlying the molecular design of insulin icodec, and identified a delicate biophysical mechanism through which two missense mutations of insulin icodec (Y37B\_H and F46B\_H) contribute to its prolonged duration of action. Overall, this structural and biophysical investigation provides valuable insights into the mechanisms underlying the relationship between three site-specific mutations and prolonged duration of action of insulin icodec, while understanding these modifications at a structural and biophysical level can aid in the rational design of future long-acting insulin analogues, offering further enhanced therapeutic options for diabetic patients.

A picture is worth a thousand words. Many branches of Science have been historically benefited with plots and visual analyses (lately, image processing and deep learning) alongside with traditional number crunching. In Molecular Biophysics, one such problem is the structure validation problem in proteins which stands with a history of plot-tools being effectively serving the complex problem to its complete resolution. Spanning across six decades, validation of protein structures (from experimental to modeled) dates back to the legendary Ramachandran Plot (with its never ending growth and modern-day applications) to the relatively recent innovation of the Complementarity Plot (CP), establishing the dual nature of complementarity as the physical basis of both binding and folding of proteins. Lately, CP has been extended to serve as a trustworthy free energy predictor utilizing supervised learning in the form of a comprehensive web-server (EnCPdock: https://www.scinetmol.in/EnCPdock/) that can be directly used in the design of protein interfaces. The commentary recapitulate the history of structure validation with a special emphasis on plot tools, highlighting key features and important discoveries worth re-visiting.

In this paper, a relatively new hyper convolution spectra for characterization liquid, viscoelastic and solid samples is presented. Special attention is paid to the biophysical characterization of epithelial tissues and early detection of cancer of the skin, cervix, colon, and oral cavity. The method is based on the use of two types of white light, diffuse and polarized, from the same LED light sources. Based on the subtraction of the intensity values of these two lights and using the hyper convolution algorithm, the ratio of paired and unpaired electrons of tissue is obtained. This ratio correlates well with the state of the tissue: normal, irritating, precancerous, and cancerous. First, a proof-of-concept studies were performed, and later clinical studies on samples from one thousand two hundred fifteen people were performed. It showed that hyper Opto-Magnetic Imaging Spectroscopy (OMIS) can detect epithelial tissue cancers with an accuracy of 85-96%. Bearing in mind that the OMIS method also acquires the biophysical characteristics of the deeper skin layers (epidermis, basement membrane, dermis) by analysing convolution spectra of RGB channels of both diffuse and polarized white light, it is possible to apply this method in cosmetics (skin hydration, collagen, and elastin states, etc.). During OMIS applications, we noticed places where improvements could be made to increase method’s accuracy using advanced machine learning methodologies.

Enzymes used in the synthesis of natural products are potent catalysts, capable of efficient and stereoselective chemical transformations. Lsd18 catalyzes two sequential epoxidations during the biosynthesis of lasalocid A, a polyether polyketide natural product. We performed protein engineering on Lsd18 to improve its thermostability and catalytic activity. Utilizing structure-guided methods of FoldX and Rosetta-ddG, we designed 15 mutants of Lsd18. Screening of these mutants using thermal shift assay identified stabilized variants Lsd18-T189M, Lsd18-S195M, and the double mutant Lsd18-T189M-S195M. Trypsin digestion, molecular dynamic simulation, circular dichroism (CD) spectroscopy, and X-ray crystallography provided insights into the molecular basis for the improved enzyme properties. Notably, enhanced hydrophobic interaction within the enzyme core and interaction of the protein with the FAD cofactor appear to be responsible for better thermostability.

In its prefusion state, the SARS-CoV-2 Spike protein (similarly to other class I viral fusion proteins) is metastable, which is considered to be an important feature for optimizing or regulating their functions. After the binding process of its S1 subunit (S1) with the ACE2, the Spike protein (S) suffers a dramatic conformational change where S1 splits from the S2 subunit, which then penetrates the membrane of the host cell, promoting the fusion of the viral and cell membranes. This results in the infection of the host cell. In a previous work, we showed -using large scale molecular dynamics simulations- that the application of external electric fields (EF) induces drastic changes and damage in the receptor-binding domain (RBD) of the wild type Spike protein, as well of the Alpha, Beta and Gamma variants, leaving a structure which cannot be recognized any more by ACE2. In this work, we first extend the study to the Delta and Omicron variants and confirm the high sensitivity and extreme vulnerability of S to moderate EF (as weak as 104 V m-1), but, more importantly, we also show that, in contrast, the postfusion state of the spike protein does not suffer structural damage even if electric field intensities four orders of magnitude higher are applied. These results provide a solid scientific basis to confirm the prefusion state metastability roots of the SARS-CoV-2 Spike protein susceptibility to be damaged by EF. After the virus docks to the ACE2 receptor, the stable and robust postfusion conformation develops, which exhibits a similar resistance to EF (damage threshold higher than 108 V m-1) like most globular proteins.

The human body contains 60-70% of water, depending on age. As a body fluid, it is not only a medium in which physical and chemical processes take place, but it is also one of the active mediators. Water is the richest substance with non-covalent hydrogen bonds. Water molecules, by themselves (in vacuum), are diamagnetic, but when organized into clusters, become diamagnetic or paramagnetic. Also, biomolecules (DNA, collagen, clathrin, and other proteins) have non-covalent hydrogen bonds in their structure. The interaction, as well as signal transmission, between water and biomolecules is achieved through the vibrations of covalent and non-covalent hydrogen bonds, which determine the state and dynamics of conformational changes of biomolecules. Disruptive conformational changes of biomolecules, cells, and tissues lead to their dysfunctionality, so they are a frequent cause of many disorders and diseases. For example, the rearrangement of hydrogen bonding due to mitochondrial disease mutation in cytochrome bc1 disturbs heme bH redox potential and spin state. In order to prevent and repair the dysfunctional conformational changes, a liquid substance was developed based on the second derivative of the C₆₀ molecule (SD-C₆₀), which has classical and quantum properties. Characterization of SD-C₆₀ by UV-VIS-NIR, FTIR, TEM, and AFM/MFM was done and it is shown that SD-C₆₀ water layers generate vibrations with near-zero phase dispersion which are transmitted through the Fibonacci’s water chains to biomolecules. In comparison to previously published SD-C₆₀ derivate (3HFWC, size until 10 nm, and 1-5 water layers), improved formulation (3HFWC-W, size 10-25 nm, and 6-9 water layers) showed multiplied cytotoxic activity against melanoma cell lines of different aggressiveness. Apart from this, the mode of action was preserved and based on induction of senescence rather than cell death. Importantly, high selectivity toward malignant phenotype was detected. Observed effects can be ascribed to a machinery of hydrogen bonds, which are generated in SD-C₆₀ and transmitted through water to biomolecules. This approach may open a new field in science and healthcare - a “water-based nanomedicine”.

N-maleimide-derivatized phospholipids are often used to facilitate protein anchoring to membranes. In autophagy studies, this is applied to the covalent binding of Atg8, an autophagy protein, to a phosphatidylethanolamine (PE) in the nascent autophagosome. However, the question remains on how closely the N-maleimide PE derivative (PE-mal) mimicks the native PE in the bilayer. In the present paper, spectroscopic and calorimetric techniques have been applied to vesicles containing either PE or PE-mal (together with other phospholipids) to compare the properties of the native and derivatized forms of PE. According to differential scanning calorimetry, and to infrared spectroscopy, the presence of PE-mal did not perturb the fatty acyl chains in the bilayer. Fluorescence spectroscopy and microscopy showed that PE-mal did not alter bilayer permeability either. However, fluorescence emission polarization of Laurdan and DPH probes indicated an increased order, or decreased fluidity, in the bilayers containing PE-mal. In addition, infrared spectral data from the phospholipid phosphate region revealed a PE-mal-induced conformational change of the polar heads, accompanied by an increased hydration. Globally considered, the results suggest that PE-mal would be a reasonable substitute for PE in model membranes containing reconstituted proteins.

Intermolecular interactions are the fabrics underlying almost all processes in living organisms, where two cornerstone concepts, intermolecular binding affinity (K d ) and binding energy (ΔG), have long been established to physically describe the strengths of biomolecular interactions, e.g., drug-target K d and ΔG to describe the strength of drug-target interaction. The past two-three years saw a big step forward in the use of artificial intelligence (AI) in structural biology (e.g., AlphaFold for protein structure prediction) and drug discovery & design. In light of the roles of K d and ΔG in drug discovery & design, the speed of this AI progress raises a question of what’s next for its practical application in the pharmaceutical industry, in addition to a system-wide account of biomolecular structures and motions. Last August, the concept of a general intermolecular binding affinity calculator (GIBAC) was for the first time coined and proposed in an MDPI-published preprint. Here, this article puts forward an updated conceptual and practical framework of GIBAC, including its inception, definition, construction, practical applications, technical challenges and limitations, and future directions. Moreover, this article argues that the time is now ripe for the construction of such an accurate, precise and efficient GIBAC to be on the agenda of the entire drug discovery & design community, to ensure its applicability & reliability, and to enhance its value in drug R&D in future.

The combined efect of shape and electrostatic complementarities (Sc, EC) at the interface of the interacting protein partners (PPI) serves as the physical basis for such associations and is a strong detterminant of their binding energetics. EnCPdock (https://www.scinetmol.in/EnCPdock/) presents a comprehensive web-platform for the direct conjoint comparative analyses of complementarity and binding energetics in PPIs. It elegantly interlinks the dual nature of local (Sc) and non-local complementarity (EC) in PPIs using the Complementarity Plot. It further derives an AI-based ∆G_binding with a prediction accuracy comparable to the state-of-the-art. This book-chapter presents a practical manual to conceptualize and implement EnCPdock with its various features and functionalities, collectively having the potential to serve as a valuable protein engineering tool in the design of novel protein interfaces.

(1) Background: The Gárdos channel (KCNN4) and Piezo1 are the best-known ion channels in the red blood cell (RBC) membrane. Nevertheless, the quantitative electrophysiological behavior of RBCs and its heterogeneity are still not completely understood.; (2) Methods: Here we use state-of-the-art biochemical methods to probe for the abundance of the channels in RBCs. Furthermore, we utilize automated patch-clamp, based on planar chips, to compare the activity of the two channels in reticulocytes and mature RBCs. Besides this characterization, we performed membrane potential measurements to demonstrate the effect of channel activity and interplay on the RBC properties.; (3) Results: Both, Gárdos channel and Piezo1, albeit their average copy number of activatable channels per cell is in the single digit range, can be detected by transcriptome analysis of reticulocytes. Proteomics analysis of RBCs could only detect Piezo1 but not the Gárdos channel. Furthermore, they can be reliably measured in the whole-cell configuration of the patch-clamp method. While for the Gárdos channel the activity is higher in reticulocytes compared to mature RBCs, for Piezo1 the tendency is the opposite. While the interplay between Piezo1 and Gárdos channel cannot be followed using the patch-clamp measurements, it could be proved based on membrane potential measurements in populations of intact RBCs.; (4) Conclusions: We discuss the Gárdos channel and Piezo1 abundance, interdependencies and interactions in the context of their proposed physiological and pathophysiological functions, which are the passing of small constrictions, e.g., in the spleen, and their active participation in blood clot formation and thrombosis.

The generation and maintenance of membrane potential is considered a fundamental element of the cellular machinery. They are based on elements of physics and chemistry. Mathematics is used to validate the equations and evaluate the results. However, this computation process can lead to a misunderstanding of the phenomena involved or to the masking of crucial events. It is even possible that the calculation no longer corresponds to the hypothesis considered. Membrane theory is no exception to these trivial considerations, which are essential for normal theory validation.

This work is the first part of a series of papers on a comprehensive analysis of the processes of prebiotic self-organization and protophotosynthesis on the surface of semiconductor minerals and in systems of natural dispersed semiconductors. Comprehensive analysis within the framework of the "semiconductor world" concept allows integrating a variety of models on a single physical basis - from ZnS world and FeS world (based on inorganic semiconductors) to the PAH world and aromatic world (including organic semiconductors). Thus, we do not put forward a new alternative hypothesis of chemical evolution - a new "chemical world", but only integrate the evolutionary and geochemical criteria of different "chemical worlds" into a single "physical world", which gives one the opportunity to reconstruct and predict the directions of chemical evolution according to the uniform principles of physical chemistry. In the first part of this series we consider photoinduced self-organization and "photo-controlled" evolution of the early protobiological systems performing solar energy conversion on the surface of dispersed semiconductor minerals capable of (photo)catalyzing and photosensitizing prebiological processes. The latter include a transition from the elementary cycles of photocatalytic reactions on the surface of semiconductors to protophotometabolic cycles and protophotosynthesis, from photophysical processes on the surface of mineral semiconductors to photoinduced membrane potentials, from photocontrolled sorption on the surface of such minerals to the formation of photosensitive protomembranes and selection of photosynthetic structures. The evolutionary approach to the analysis of protobiological mechanisms and protobioenergetics within the framework of the "semiconductor world" concept provides a new approach to the study of the last common point of divergence of protobiological systems, where the emergence principles of different energy supply schemes (like the energy source-specific photoautotrophy and substrate-specific chemoautotrophy) merge at the physicochemical level. The proposed integrating scheme is consistent with most biological, geological, and physicochemical concepts, which ensures its complete cross-checking and internal consistency.

Single-particle cryo-electron microscopy (cryo-EM SPA) has recently emerged as an exceptionally well-suited technique for determining the structure of membrane proteins (MPs). Indeed, in the last years, it was observed a huge increase in the number of MPs solved by cryo-EM SPA at a resolution better than 3.0 Å in the Protein Data Bank (PDB). However, sample preparation remains a significant challenge in the field. Here, we evaluated in the MPs solved by cryo-EM SPA deposited in the PDB in the last two years at a resolution below 3.0 Å the most critical parameters for sample preparation: i) the surfactant used for protein extraction from the membrane, ii) the surfactant, amphiphiles, nanodiscs or other molecules present in the vitrification step, iii) the vitrification method employed, and iv) the type of grids used. The aim is not to provide a definitive answer on the optimal sample conditions for cryo-EM SPA of MPs, but rather assess the current trends in the MP structural biology community towards obtaining high-resolution cryo-EM structures.

This review presents an updated overview of research concerning Leishmania protein structures, primarily sourced from the Protein Data Bank (PDB), that play a role in the metabolic pathways of the Leishmania parasite. Furthermore, we assess the current progress in the identification and development of bioactive chemical agents aimed at addressing this substantially overlooked tropical disease. We have analyzed experimental data obtained from in vitro, in vivo and in silico sources. This data has been categorized into four main areas: a) vector taxonomy and geographic distribution; b) parasite taxonomy and geographic distribution; c) enzymatic functions of proteins engaged in parasite/host interactions throughout various developmental stages (such as oxidoreductases, transferases, hydrolases, lyases, isomerases, ligases, and cytokines); and d) established and experimental treatments employing bioactive chemical compounds. Our objective is to establish a foundational point of reference for research efforts concentrating on the elucidation of interaction mechanisms and the processes of ligand-protein activation/inactivation, specifically linked to Leishmania infections. Consequently, we emphasize enzymes recognized for their involvement in the biochemical pathways incited during Leishmania infection episodes. This review encapsulates the current understanding, offering insights to inform and direct future explorations aimed at targeting proteins and pathways to enhance the management of Leishmania-related diseases.

Ice-binding proteins are crucial for adaptation of various organisms to low temperatures. Some of these, called antifreeze proteins, are usually thought to inhibit growth and/or recrystallization of ice crystals. However, prior to these events, ice must somehow appear in the organism, either coming from outside or forming inside through the nucleation process. Unlike most other works, our paper is focused on ice nucleation rather than the behavior of the already existing ice. The nucleation kinetics is studied both theoretically, with special attention paid to ice nucleation on ice-binding surfaces, and experimentally. For experimental studies, we use the ice-binding protein mIBP83, a previously constructed mutant of a spruce budworm Choristoneura fumiferana antifreeze protein. We show that mIBP83 does not affect the ice nucleation temperature in the buffer in test tubes, but hinders the impact of potent ice nucleators of various chemical natures, namely CuO powder and ice-nucleating bacteria Pseudomonas syringae. Additional experiments on human cells show that mIBP83 is concentrated in some regions, but only in cooled cells. Thus, the antifreeze protein not only binds to ice, but also blocks various sites that act or can act as ice nucleators. Such ice-preventing binding may be the crucial biological task of antifreeze proteins.

Brugada syndrome (BrS) is an inherited disease characterized by right precordial ST-segment elevation on electrocardiograms (ECGs), and a high risk of life-threatening ventricular arrhyth-mia and sudden cardiac death. In BrS patients, except for SCN5A, mutations in other responsible genes are poorly elucidated. We identified a new missense mutation, c.1189C>T (p.R397C), in the KCNH2 gene in asymptomatic male proband with diagnosed BrS. We performed patch-clamp analysis on I(Kr) reconstituted with this KCNH2 mutation in Chinese hamster ovary cells and compared the phenotype with the wild type. We found that the R397C mutation increased the I(Kr) density. In silico modeling demonstrated that this missense mutation potentially leads to the shortening of action potential in the heart.

Recently, we demonstrated the validity of anomalous numbers law, known as Newcomb-Benford´s law, at the mammalian neuromuscular transmission, considering different extracellular calcium concentrations . The present work continues to examine how alterations in extracellular solution modulate the first digit law in the context of the spontaneous release of acetylcholine from the neuromuscular junction. We investigated if the intervals of miniature potentials collected at the neuromuscular junction obey the law in a hyperkalemic environment. The analysis showed that the interval between the miniature potentials at high potassium concentrations follows Newcomb-Benford´s law. Also, our data allowed us to uncover a conformity fluctuation as a function of the number of intervals of the miniature potentials. Finally, we discuss the biophysical implications of the present findings.

Recent advancements in the study of the protein complex photosystem II have clarified the sequence of events leading to the formation of oxygen during the S3->S4->S0 tran-sition, wherein the inorganic Mn4Ca(µ-O)6(OHx)4 cluster finishes photo-catalyzing the water splitting reaction (Greife et al, Nature 2023, 617, 623-628; Bhowmick et al, Nature 2023, 617, 629-636) . During this final step a tyrosine radical (TyrZ), stable for a couple of milliseconds, oxidizes a cluster bound oxygen while the hydrogen bonding patterns of nearby waters shift a proton away. A treatment of this redox reaction within the context of accepted transition state theories predicts rate constants that are significantly higher than experimentally recovered values (10^12s^-1 versus 10^3s^-1). In an effort to understand this disparity, temperature dependent experiments have revealed large entropic con-tributions to the rates with only a moderate energy of activation. We suggest that the entropic source may be related to the observed proton rearrangements, and further possible near isoenergetic variations in the nearby extended h-bonding network de-laying the realization of an ‘ideal’ transition state. In the following we explore this re-lation in the context of Eyring’s transition state theory and Marcus’ electron transfer theory, and evaluate their compatibility with the experimental evidence.

The metabolism of lipoproteins, which regulates the transit of the lipid to and from tissues, is crucial to maintaining cholesterol homeostasis. Cardiac remodeling is referred to as a set of molecular, cellular, and interstitial changes that, following injury, affect the size, shape, function, mass, and geometry of the heart. Acetyl coenzyme A (acetyl CoA), which can be made from glucose, amino acids, or fatty acids, is the precursor for the synthesis of cholesterol. In this article, authors explain concepts behind cardiac remodeling, its clinical ramifications, and the pathophysiological roles played by numerous various components, such as cell death, neurohormonal activation, oxidative stress, contractile proteins, energy metabolism, collagen, calcium transport, inflammation, and geometry. The levels of cholesterol are traditionally regulated by two biological mechanisms at the transcriptional stage. First, the SREBP transcription factor family regulates the transcription of crucial rate-limiting cholesterogenic and lipogenic proteins, which in turn limits cholesterol production. Immune cells become activated, differentiated, and divided, during an immune response with the objective of eradicating the danger signal. In addition to creating ATP, which is used as energy, this process relies on metabolic reprogramming of both catabolic and anabolic pathways to create metabolites that play a crucial role in regulating the response. Because of changes in signal transduction, malfunction of the sarcoplasmic reticulum and sarcolemma, impairment of calcium handling, increases in cardiac fibrosis, and progressive loss of cardiomyocytes, oxidative stress appears to be the primary mechanism that causes the transition from cardiac hypertrophy to heart failure. De novo cholesterol production, intestinal cholesterol absorption, and biliary cholesterol output are consequently crucial processes in cholesterol homeostasis. In the article's final section, the pharmacological management of cardiac remodeling is explored. The route of treatment is explained into different steps: including, promising, and potential strategies. This chapter offers a brief overview of the history of the study of cholesterol absorption as well as the different potential therapeutic targets.

The natural flavonoid epigallocatechin gallate (EGCG) has a wide range of biological activities including binding to nucleic acids; however, mechanisms of interaction of EGCG with DNA organized in chromatin have not been systematically studied. In this work, interaction of EGCG with chromatin in cells, and with nucleosomes and chromatosomes in vitro was studied using fluorescent microscopy and single particle Förster resonance energy transfer approaches, respectively. EGCG effectively penetrates into the nuclei of living cells and binds to DNA there. Interaction of EGCG with nucleosomes in vitro induces large-scale, reversible uncoiling of nucleosomal DNA that occurs without dissociation of DNA or core histones at sub- and low-micromolar concentrations of EGCG. EGCG does not reduce the catalytic activity of poly(ADP-ribose) polymerase 1 (PARP1), but causes modulation of the structure of the PARP1-nucleosome complex. EGCG significantly changes the structure of chromatosomes, but does not cause dissociation of the linker histone. Reorganization of nucleosomes and chromatosomes by EGCG might facilitate access of protein factors involved in DNA repair, replication and transcription to DNA and thus might contribute to the modulation of gene expression by EGCG that was reported earlier.

A human brain can communicate with another one’s brain using quantum entanglement. Similar particles can entangle without interaction. Sensory communication between two individuals from far distances is still not known. Former studies primarily have been performed regarding brain neurons' quantum behavior. The test was performed on two individuals far from each other. These individuals were put under the same music on the basis of brain neuroplasticity property. By activating the brain reward system, aligned thinking was time scheduled in these individuals’ brains. On the basis of entanglement, compressed information was sent and received between these two individuals.

Plasminogen (Plg) is the inactive form of plasmin (Plm) that exists in two major glycoforms, referred to as glycoforms I and II (GI and GII). In the circulation, Plg assumes an activation-resistant 'closed' conformation via interdomain interactions and is mediated by the lysine binding site (LBS) on the kringle (KR) domains. These inter-domain interactions can be readily disrupted when Plg binds to lysine/arginine residues on protein targets or free L-lysine and analogues. This causes Plg to convert into an 'open' form which is crucial for activation by host activators. In this study, we investigated how various ligands affect the kinetics of Plg conformational change using small-angle X-ray scattering (SAXS). We began by examining the open and closed conformations of Plg using size-exclusion chromatography (SEC) coupled with SAXS. Next, we developed a high throughput (HTP) 96-well SAXS assay setup. This method enables us to determine the Kopen value, which is used to compare ligands’ effect on Plg conformation directly. Based on our analysis using Plg GII, we have found that the Kopen for ε-aminocaproic acid (EACA) is approximately three times greater than that of Tranexamic acid (TXA), which is widely recognized as a highly effective ligand. We demonstrated further that Plg undergoes a conformational change when it binds to the C-terminal peptides of the inhibitor α2-antiplasmin and receptor Plg‑RKT. Our findings suggest that, besides the C-terminal lysine, internal lysine(s) are also necessary for the formation of open Plg. Finally, we compared the conformational changes of Plg GI and GII directly and found that the closed form of GI, which has an N-linked glycosylation, is less stable. To summarize, we have successfully determined the response of Plg to various ligand/receptor peptides by directly measuring the kinetics of its conformational changes.

Cellular biomolecular condensates termed ribonucleoprotein (RNP) granules often are enriched in messenger RNA (mRNA) molecules relative to the surrounding cytoplasm. Yet, the spatial localization and diffusion of mRNAs in close proximity to phase separated RNP granules is not well understood. In this study, we performed single molecule fluorescence imaging experiments of mRNAs in live cells in the presence of two types of RNP granules, stress granules (SG) and processing bodies (PB), which are distinct in their molecular composition and function. We developed a photobleaching- and noise-corrected colocalization imaging algorithm that was employed to determine the accurate positions of individual mRNAs relative to the granule’s boundaries. We found that mRNAs are often localized at granule boundaries, an observation consistent with recently published data. We suggest that mRNA molecules become spontaneously confined at the RNP granule boundary similar to the adsorption of polymer molecules at liquid-liquid interfaces, which is observed in various technological and biological processes. We also suggest that this confinement could be due to a combination of intermolecular interactions associated with, first, the screening of a portion of the RNP granule interface by the polymer and, second, electrostatic interactions due to a strong electric field induced by a Donnan potential generated across the thin interface.

We demonstrate the use of a nanotechnology-based approach for the investigation of enzymatic activity of a single molecule of horseradish peroxidase with a solid-state nanopore. The artificial 5 nm solid-state nanopore has been formed in a 40-nm-thick silicon nitride structure. Single molecule of HRP has been entrapped into the nanopore. The activity of horseradish peroxidase (HRP) enzyme molecule inserted in the nanopore has been monitored by recording the time dependence of the ion current through the nanopore in the course of the reaction of 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonate) (ABTS) oxidation reaction. We have found that in the process of ABTS oxidation in the presence of 2.5 mM hydrogen peroxide, individual HRP enzyme molecules are able to retain activity for approximately 700 seconds before a decrease in the ion current through the nanopore, which can be explained by structural changes of the enzyme.

Macromolecular assemblies, such as protein complexes, undergo continuous structural dynamics, including global reconfigurations critical for their function. Two fast analytical methods are widely used to study these global dynamics, namely elastic network model normal mode analysis and principal component analysis of ensembles of structures. These approaches have found wide use in various computational studies, driving the development of complex pipelines in several software packages. One common one has been conformational sampling through hybrid simulations incorporating all-atom molecular dynamics and global modes of motion. However, wide functionality is only available for experienced programmers with limited capabilities for other users. We have, therefore, integrated one popular and extensively developed software for such analyses, the ProDy Python application programming interface, into the Scipion workflow engine. This enables a wider range of users to access a complete range of macromolecular dynamics pipelines beyond the core functionalities available in its command-line applications and the normal mode wizard in VMD. The new protocols and pipelines can be further expanded and integrated into larger workflows together with other software packages for cryo-electron microscopy image analysis and molecular simulations. We present the resulting plugin, Scipion-EM-ProDy, in detail, highlighting the rich functionality made available by its development.

There is a paucity of data on the progression of track cycling sprinters, and the evolution of training and performance over a training cycle. Following prior research showing relationships between sprint cycling power and endurance cycling power, we compared these relations over a 3-month period. Our hypothesis is large improvements in power would come from training either for sprint power or sprint capacity, and this would be reflected in the data. A total of publicly available 25 datasets. These data were plotted against the line of best fit for 30-s and 2-min power to assess if training towards the line led to better 30-s power. We observe the best performances came from riders who start below the 30-s vs 2-min power line of best fit, with greater capacity and ability to ride fast over a sprint series, and progressing towards focusing on specific power in the final block leading into a competition. These results support the hypothesis of combined capacity and power training based on rider-specific relationship to the line of best fit between these measures versus a strictly maximal power training focus.

Mitochondria are highly dynamic organelles that constantly undergo fusion and fission events to maintain their shape, distribution, and cellular function. Mitofusin 1 and 2 proteins are two dynamin-like GTPases involved in the fusion of outer mitochondrial membranes (OMM). Mitofusins are anchored to the OMM through their transmembrane domain and possess two heptad repeat domains (HR1 and HR2) in addition to their N-terminal GTPase domain. The HR1 domain was found to induce fusion via its amphipathic helix, which interacts with the lipid bilayer structure. The lipid composition of mitochondrial membranes can also impact mitochondrial fusion. However, the precise mode of action of lipids in mitochondrial fusion is not fully understood. In this study, we have examined the role of the mitochondrial lipids phosphatidylethanolamine (PE), cardiolipin (CL) and phosphatidic acid (PA) in membrane fusion induced by the HR1 domain, both in the presence and absence of divalent cations (Ca2+ or Mg2+). Our results show that PE, as well as PA in the presence of Ca2+, effectively stimulate HR1-mediated fusion, while CL has a slight inhibitory effect. By considering the biophysical properties of these lipids in the absence or presence of divalent cations, we infer that the interplay between divalent cations and specific cone-shaped lipids creates regions with packing defects in the membrane, which provides a favorable environment for the amphipathic helix of HR1 to bind to the membrane and initiate fusion.

Background: Long QT syndrome (LQTS) can lead to ventricular arrhythmia and sudden cardiac death. The most common congenital cause of LQTS is mutations in the channel subunits generating the cardiac potassium current IKs. Zebrafish (Danio rerio) have been proposed as a powerful system to model human cardiac diseases due to the similar electrical properties of the zebrafish heart and the human heart. Methods: We used high resolution all-optical electrophysiology on ex-vivo zebrafish hearts to assess the effects of IKs analogues on the cardiac action potential. Findings: We found that chromanol 293B (an IKs inhibitor) prolonged the action potential duration (APD) in the presence of E4031 (an IKr inhibitor applied to drug-induced LQT2) and to a lesser extent in the absence of E4031. Moreover, we show that PUFA analogues slightly shorten the APD of the zebrafish heart. However, PUFA analogues failed to reverse the APD prolongation in drug-induced LQT2. However, a more potent IKs activator, ML-277, partially reversed the APD prolongation in drug-induced LQT2 zebrafish hearts. Interpretation: Our results suggest that IKs plays a limited role in ventricular repolarizations in the zebrafish heart under resting conditions but plays a more important role when IKr is compromised, as if IKs in zebrafish serves as a repolarization reserve as in human hearts. This study shows that potent IKs activators can restore the action potential duration in drug-induced LQT2 in zebrafish heart.

The perturbations of DNA methyltransferase 3 alpha (DNMT3A) may cause uncontrolled gene expression, resulting in cancers and tumors. The DNMT inhibitors Azacytidine (AZA) and Zebu-larine (ZEB) inhibit the DNMT family with no specificities and consequently would bring side effects during the treatment. Therefore, it is vital to understand their inhibitory mechanisms in DNMT3A to inform the new inhibitor design for DNMTs. Herein, we carried out molecular dynamics (MD) and quantum mechanics/molecular mechanics (QM/MM) simulations to investigate the inhibitory mechanisms of AZA and ZEB. The results were compared to the methyl transfer of cytosine. We showed AZA might stop the methyl transfer process, whereas the ZEB might be stuck in a me-thyl-transferred intermediate (IM3). Such IM3 state then fails the elimination due to the unique protein dynamics that result in missing the catalytic water chain. Our results brought atomic-level insights into the mechanisms of the two drugs in DNMT3A, which could benefit the new generation of drug design for the DNMTs.

Hoechst 33342 (H33342) is a fluorescent probe that is commonly used to stain DNA of living cells. To do so, it needs to interact with and permeate through cell membranes. The pH of biological media may vary quite significantly and this influences H33342 ionization state, and may change its rate of permeation through biomembranes. In this work, we address the effect of pH in the association of H33342 with lipid bilayers using a combined experimental and computational approach. The partition of H33342 to 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) lipid membranes was experimentally quantified using fluorescence spectroscopy and isothermal titration calorimetry (ITC) measurements. Quantum chemical calculations were performed to select the most stable isomer of H33342 for the overall charges 0, +1, and +2, expected to predominate across the 5 < pH < 10 range. The interaction of these isomers with POPC bilayers was then studied by both unrestrained and umbrella sampling molecular dynamics simulations. While probe insertion occurs for all ionization states, with similar overall equilibrium location, orientation was found to depend on the probe charge, with the neutral form lying mostly parallel and the doubly charged displaying a significant tilt relative to the membrane plane. Both experimental results and computational free energy profiles indicate that the partition coefficient of H33342 displays a small variation over a wide pH range, not exceeding one order of magnitude. The enthalpy of interaction is negative and is larger at lower pH values. This suggests efficient hydrogen bonding between the probe and the lipid, namely for the protonated +2 form. The relatively high lipophilicity obtained for the charged species contrasts with the decrease in their general hydrophobicity as estimated from water and octanol partition. This highlights the distinction between lipophilicity and hydrophobicity, and the importance of considering association with lipid bilayers when predicting the affinity for biomembranes.

Antimicrobial peptides (AMPs) are generally membrane-active compounds that physically disrupt bacterial membranes. Despite extensive research, the precise mode of action of AMPs is still a topic of great debate. This work demonstrates that the initial interaction between the Gram-negative E. coli and AMPs is driven by lipopolysaccharides (LPS) that act as kinetic barriers for the binding of AMPs to the bacterial membrane. A combination of SPR and NMR experiments provide evidence suggesting that cationic AMPs first bind to the negatively charged LPS before reaching a binding place in the lipid bilayer. In the event that the initial LPS-binding is too strong (corresponding to a low dissociation rate), the cationic AMPs cannot effectively get from the LPS to the membrane and their antimicrobial potency will thus be diminished. On the other hand, the AMPs must also be able to effectively interact with the membrane to exert its activity. The ability of the studied cyclic hexapeptides to bind LPS and to translocate into a lipid membrane is related to the nature of the cationic charge (arginine vs lysine) and to the distribution of hydrophobicity along the molecule (alternating vs clumped tryptophan).

Prospects for predicting the fragmentation of polypeptide chains during their enzymatic hydrolysis using proteolysis models are considered. The opening of the protein substrate during proteolysis and the exposure of its internal peptide bonds for a successful enzymatic attack, the so-called demasking process, were taken into account. The two-step model of proteolysis was used, including the parameters of peptide bond demasking and hydrolysis rate constants for various peptide bonds. Herein, we presented an algorithm for calculating the concentrations of peptide fragments depending on the hydrolysis time or the degree of hydrolysis. The intermediate peptide fragments with two or one internal specific bonds were considered. The fragmentation of β-lactoglobulin (b-LG) with trypsin was predicted, and the calculated concentration curves for peptide fragments were compared with the experimental dependences of concentrations on the degree of hydrolysis. Numerical parameters characterizing the kinetic curves were proposed for the intermediate and final peptide fragments, and they were used to compare the calculated and experimental dependences. The predicted distribution of peptide fragments corresponded to the experimental data on the peptide release during proteolysis of b-LG by trypsin.

The outer membrane of Gram-negative bacteria contains a variety of pore-forming structures collectively referred to as porins. Some of these are voltage-dependent, but weakly so, closing at high voltages. Triplin, a novel bacterial pore-former, is a three-pore structure, highly voltage dependent, with a complex gating process. The three pores close sequentially: pore 1 at positive potentials, 2 at negative and 3 at positive. A positive domain containing 14 positive charges (the voltage sensor) translocates through the membrane during the closing process and the translocation is proposed to take place by the domain entering the pore and thus blocking it resulting in the closed conformation. This mechanism of pore closure is supported by kinetic measurements that show that in the closing process the voltage sensor travels through most of the transmembrane voltage before reaching the energy barrier. Voltage-dependent blockage of the pores by polyarginine but not by a 500 fold higher concentrations of polylysine, is consistent with the model of pore closure, with the sensor consisting mainly of arginine residues, and with the presence, in each pore, of a complementary surface that serves as a binding site for the sensor.

Due to the progression of antimicrobial resistance, Photodynamic (aPDT) and Sonodynamic (SDT) Therapies arose as promising approaches for microbial control. Recently, the combination of both therapies, called Sonophotodynamic Therapy (SPDT), have shown greater effects than the single treatments. This study evaluated the effects of aPDT, SDT and SPDT mediated by curcumin on the metabolism, adhesion capacity, biofilm formation ability and structural alteration in the Staphylococcus aureus biofilm. Moreover, possible antimicrobial mechanisms involved, such as the production of reactive oxygen species (ROS) and the degradation spectrum of curcumin by light, ultrasound and light+ultrasound, were measured. SPDT was more effective to inactivate the biofilm than aPDT and SDT. All treatments reduced the adhesion ability of the bacteria and it is probably related to the metabolism cell reduction. Regarding to the biofilm formation ability, all treatments reduced the total biomass. One possible mechanism involved in the inactivation is the ROS production observed. Curcumin had a significantly decay in its absorbance with the application of light, and a slighter one caused by the ultrasound. Finally, all treatments impacted the biofilm components and structure, reducing the cells and matrix. In conclusion, SPDT was more effective in the inactivation, and all treatments had the same effect in the virulence factors evaluated.

Glycerol has found its applications as a heat-transfer fluid in heat exchangers, and as a compo-nent of functional solutions in biosensor analysis. Flowing non-aqueous fluids are known to be able to induce electromagnetic fields due to triboelectric effect. These triboelectrically generated electromagnetic fields can affect biological macromolecules. Horseradish peroxidase (HRP) is widely employed as a convenient model object for studying how external electric, magnetic, and electromagnetic fields affect enzymes. Herein, we have studied whether the flow of glycerol in a ground-shielded cylindrical coil affects the HRP enzyme incubated at a 2 cm distance near the coil’s side. Atomic force microscopy (AFM) has been employed in order to study the effect of glycerol flow on HRP at the nanoscale. Increased aggregation of HRP on mica has been observed after the incubation of the enzyme near the coil. Moreover, the enzymatic activity of HRP has also been affected. The results reported can find their application in biotechnology, food technology and life sciences application, considering the development of triboelectric generators, en-zyme-based biosensors and bioreactors with surface-immobilized enzymes. Our data can also be of interest for scientists studying triboelectric phenomena.

Phytochromes are biological photoswitches that translate light into a physiological function. Spec-troscopic techniques are essential tools in molecular research on these photoreceptors. This review is directed to summarize how resonance Raman and IR spectroscopy contributed to the under-standing of structure, dynamics, and reaction mechanism of phytochromes, outlining the substan-tial experimental and theoretical challenges and describing the strategies to master them. It is shown that the potential of the various vibrational spectroscopic techniques can be most efficient-ly exploited in integral approaches by combination with theoretical methods as well as other ex-perimental techniques.

The β-amyloid cleaving enzyme 1 (BACE1) is regarded as an important target of drug design toward treatment of Alzheimer's disease (AD). In this study, three separate molecular dynamics (MD) simulations and calculations of binding free energies were carried out to comparatively probe identification mechanism of BACE1 on three inhibitors 60W, 954 and 60X. The analyses on MD trajectories indicate that the presence of three inhibitors influences structural stability, flexibility and internal dynamics of BACE1. Binding free energies calculated by using solvated interaction energy (SIE) and molecular mechanics generalized Born surface area (MM-GBSA) methods reveal that the hydrophobic interactions provide decisive forces for inhibitor-BACE1 binding. The calculations of residue-based free energy decomposition suggest that the sidechains of residues L91, D93, S96, V130, Q134, W137, F169 and I179 play key roles in inhibitor-BACE1 binding, which provides a direction for future drug design toward treatment of AD.

Graphene quality for use in biosensors was assessed in fabricated chips by a set of methods that includes atomic force microscopy (AFM), Raman spectroscopy, and low-frequency noise. It is shown that local areas of residues on the graphene surface, which arisen due to the interaction of graphene with a photoresist at the initial stage of chips development, leads to a spread of chips resistance (R) within 1-10 kΩ and to an increase in the root mean square (RMS) roughness up to 10 times that can significantly impair reproducibility of graphene parameters in biosensors chips. It was observed that the control of the photoresist residues after photolithography (PLG) by AFM and subsequent additional cleaning allow reducing the spread of R values in chips to 1–1.6 kΩ and obtaining RMS roughness similar to the roughness in pristine graphene before PLG. Monitoring of the spectral density of low-frequency voltage fluctuation (SU), which provides integral information about the defect system and quality of the material, makes it possible to identify chips with low graphene quality and with inhomogeneously distributed compressive stresses areas by the type of frequency dependence SU (f).

The review presents the most developed by date hypotheses on the mechanisms of the influence of low-frequency magnetic fields (LFMF) on organisms. Biophysical models that explain the resonance-like responses of biological systems to LFMF with a specific frequency and amplitude are described. Two groups can be distinguished among these models: one considers ions-cofactors of proteins as the primary targets for the LFMF influence, and the other regards the magnetic moments of particles in biomolecules. Attention is paid to the dependence of resonance-like LFMF effects on the cell type. A radical-pair mechanism of the magnetic field's influence on biochemical processes is described with the example of cryptochrome. Conditions for this mechanism's applicability to explain the biological effects of LFMF are given. A model of the influence of LFMF on radical pairs in biochemical oscillators, which can explain the frequency-amplitude efficiency windows of LFMF, is proposed.