ATP Synthase is an essential molecule in cell and molecular biology. It is responsible for the production of ATP during cellular respiration, a molecule that provides the energy required to drive a number of cellular processes. In this paper, I explore the rotational physics of ATP Synthase’s rotor, a part of the protein that spins during the production of ATP. Firstly, I discuss some elementary rotational kinematics of the rotor. I then derive two alternate formulations for the total linear acceleration of the rotor. Finally, I derive formulas for the moment of inertia, angular momentum, net torque, and kinetic energy of the rotor. Through this, I hope to provide a theoretical and mathematical insight into the mechanics of ATP Synthase during the production of ATP.

Anterior Cruciate Ligament (ACL) injuries rank among the most prevalent and severe types of injuries, significantly impacting both athletes and non-athletes alike. These injuries not only result in immediate physical impairment, such as intense pain, substantial swelling, and a marked loss of mobility, but also carry long-term health consequences that can alter a person's quality of life. Chronic pain, persistent instability, and an increased risk of developing osteoarthritis are among the lasting effects that can follow an ACL injury. An in-depth understanding of the biophysics behind ACL injuries is paramount for devising effective prevention and treatment protocols. Biophysics, which combines principles from physics with biological systems, provides crucial insights into the mechanical and structural integrity of the ACL and its susceptibility to injury under various conditions. This systematic review aims to collate and synthesize the current knowledge surrounding the biophysical mechanisms that underlie ACL injuries. The review encompasses a range of factors, including the biomechanical forces that place stress on the ligament, anatomical structures that may predispose individuals to injury, and physiological conditions that affect ligament health and resilience. Each of these factors plays a crucial role in the incidence and severity of ACL injuries. Biomechanical forces, for example, can involve sudden changes in direction or impact during physical activity, leading to excessive stress on the ACL. Anatomical factors might include variations in bone structure or ligament alignment that inherently increase the risk of injury. Additionally, physiological conditions such as muscle strength, flexibility, and overall ligament health can influence the likelihood and extent of an ACL injury. The findings of this review underscore the necessity of adopting integrated approaches in both injury prevention and rehabilitation. Such approaches must consider the multifaceted nature of ACL injuries, involving not only mechanical and anatomical aspects but also physiological and possibly even genetic factors. By emphasizing a multi-faceted understanding, interventions can be more effectively tailored to address the complex interplay of elements that contribute to ACL injuries. This holistic approach can lead to better outcomes for those at risk of or recovering from ACL injuries, enhancing the efficacy of prevention strategies and rehabilitation protocols.

We report on the state of the art of proteins recognized as potential targets for the development of leishmania treatments through the search of biologically active chemical species, either from experimental in vitro, in vivo, or in silico sources. We classify the gathered information, in several ways: vector taxonomy and geographical distribution, leishmania parasite taxonomic and geographical distribution and enzymatic function (oxidoreductases, transferases, hydrolases, lyases, isomerases, ligases and cytokines). Our aim is to provide a much needed reference layout for research efforts aimed to understand the background of ligand-protein activation/inactivation processes, in this specific case, related with enzymes known to be part of biochemical cascade reactions initiated following a leishmania infectious episode.

Semaglutide is a glucagon-like peptide 1 analog used for the treatment of patients with type 2 diabetes mellitus. With 94% sequence similarity to human GLP-1, semaglutide is a glucagon-like peptide-1 receptor (GLP-1R) agonist, which binds directly to GLP-1R, causing various beneficial downstream effects that reduce blood glucose. Practically, it is favourable for semaglutide to bind not just directly, but also tighter, to its receptor GLP-1R. Therefore, incorporating currently available experimental structural data of semaglutide-GLP-1R, this short article reports for the first time that biophysically, semaglutide is able to bind tighter to GLP-1R with just a simple Val27-Arg28 exchange in its peptide backbone.

This study investigates the impact of temperature-induced quantum proton tunneling probability on DNA amplification during polymerase chain reactions (PCR). Using a simulation model based on a Gaussian wavefunction and finite-difference time-domain method, quantum tunneling of protons across square potential barriers is examined. The results unveil consistent probability distributions for quantum tunneling across various PCR temperatures, with distinct oscillation patterns emerging post-barrier crossing. Acknowledging limitations in initial conditions due to temperature-dependent proton energy, the study highlights the need for refined models and experimental validation. These findings accentuate the potential interplay between quantum mechanics and biological systems, prompting further research to understand quantum tunnelling’s comprehensive effect on genetic variations and molecular processes.

Although many details remain still elusive, it became increasingly evident in recent years that mechanosensing of microenvironmental biophysical cues and subsequent mechanotransduction are strongly involved in the regulation of neuronal cell development and functioning. This review gives an overview about the current understanding of brain and neuronal cell mechanobiology and how it impacts on neurogenesis, neuronal migration, differentiation, and maturation. Therein; we are focussing particularly on the events in the cell/microenvironment interface and the decisive extracellular matrix (ECM) parameters (i.e. rigidity and nanometric spatial organisation of adhesion sites) that modulate integrin adhesion complex-based mechanosensing and mechanotransductive signalling. It will also be outlined how biomaterial approaches mimicking essential ECM features help to understand these processes and how they can be used to control and guide neuronal cell behaviour by providing appropriate biophysical cues. In addition, principal biophysical methods will be highlighted that have been crucial for the study of neuronal mechanobiology.

Single particle tracking (SPT) is a powerful class of methods for studying the dynamics of biomolecules inside living cells. The techniques reveal the trajectories of individual particles, with a resolution well below the diffraction limit of light, and from them the parameters defining the motion model, such as diffusion coefficients and confinement lengths. Most existing algorithms assume these parameters are constant throughout an experiment. However, it has been demonstrated that they often vary with time as the tracked particles move through different regions in the cell or as conditions inside the cell change in response to stimuli. In this work, we propose an estimation algorithm to determine time-varying parameters of systems that discretely switch between different linear models of motion with Gaussian noise statistics, covering dynamics such as diffusion, directed motion, and Ornstein-Uhlenbeck dynamics. Our algorithm consists of three stages. In the first stage, we use a sliding window approach, combined with Expectation Maximization (EM) to determine maximum likelihood estimates of the parameters as a function of time. These results are only used to roughly estimate the number of model switches that occur in the data to guide the selection of algorithm parameters in the second stage. In the second stage, we use change detection (CD) techniques to identify where the models switch, taking advantage of the off-line nature of the analysis of SPT data to create non-causal algorithms with better precision than a purely causal approach. Finally, we apply EM to each set of data between the change points to determine final parameter estimates. We demonstrate our approach using experimental data generated in the lab under controlled conditions.

Earth-abundant transition metals like iron, nickel, copper, molybdenum, and vanadium have been identified as essential constituents of the cellular gas metabolism in all kingdoms of life. Associated with biological macromolecules, gas-processing metalloenzymes (GPMs) are formed that catalyse a variety of redox reactions. This includes the reduction of O₂ to water by cytochrome c oxidase (‘complex IV’), the reduction of N₂ to NH₄ by nitrogenase, as well as the reduction of protons to H₂ (and oxidation of the later) by hydrogenase. GPMs perform at ambient temperature and pressure, in the presence of water, and often extremely low educt concentrations, thus serving as natural examples for efficient catalysis. Facilitating the design of biomimetic catalysts, biophysicist thrive to understand the reaction principles of GPMs making use of various techniques. In this perspective, I will introduce Fourier-transform infrared spectroscopy in attenuated total reflection configuration (ATR FTIR) for the analysis of GPMs like cytochrome c oxidase, nitrogenase, and hydrogenase. Infrared spectroscopy provides information about the geometry and redox state of the catalytic cofactors, the protonation state of amino acid residues, the hydrogen-bonding network, and protein structural changes. I developed an approach to probe and trigger the reaction of GPMs by gas exchange experiments, exploring the reactivity of these enzymes with their natural reactants. This allows recording sensitive ATR FTIR difference spectra with seconds time resolution. Finally yet importantly, infrared spectroscopy is an electronically non-invasive technique that allows investigating protein samples under biologically relevant conditions, i.e., at ambient temperature and pressure, and in the presence of water.

To solve the problem of the origin of homochirality of living systems and life itself, it is necessary to rethink the conceptual foundations of physics dialectically. The review shows how this can be done within the framework of the paradigm of energoforms, which sets the principles of construction and mathematical formalization of the Heraclitus primary element of matter. The algorithm for modeling energoforms and structures of elementary particles was created based on the laws of dialectics, electromagnetic induction and the rules of quantum physics. The algorithm allowed to calculate the parameters of chiral structures of neutron, proton, electron, neutrino and photon in the ground and excited states, as well as the parameters of more than one hundred nuclei. As an adequate solution to the problems of neutrino physics and the nature of the universal chiral factor, we hypothesized the instability of the low-energy solar neutrino and the biogenicity of its decay products – chiral energoforms. The hypothesis was based on correlations of seasonal and daily variations of solar neutrino flux, optical activity of aqueous solutions of sugars and metabolism of plants and animals. In humans, chiral morphofunctional features that limit homeostasis and the rhythm of energy-informational connections of the brain with the external environment were revealed. It was shown that the mechanism of sensitivity of living matter to the action of chiral factor is based on the ability to self-organization of its cooperative water-containing systems consisting of spin homogeneous chiral elements.

Oxidative stress, characterized by an imbalance between the production of reactive oxygen species (ROS) and the body's antioxidant defenses, significantly affects cellular function and viability. It plays a pivotal role in modulating action potentials (APs), essential for properly functioning excitable cells such as neurons, smooth muscles, pancreatic beta cells, and myocytes. The interaction between oxidative stress and AP dynamics is crucial for understanding the pathophysiology of various conditions, including neurodegenerative diseases, cardiac arrhythmias, and ischemia-reperfusion injuries. This review explores how oxidative stress influences APs, focusing on alterations in ion channel functionality, signaling pathways, and membrane properties. By integrating current research, we aim to elucidate how oxidative stress contributes to disease progression and discuss potential therapeutic interventions targeting this interaction.

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.

The generation and maintenance of membrane potential is a fundamental part of Membrane Pump Theory. One of the key points of this hypothesis is based on a natural or facilitated molecular diffusion through several types of ion channels and pumps like the Na/K ATPase. Chemistry, physics and especially electrochemistry, however, bring strong contradictions to this theoretical assumption. By respecting the principles of chemistry and electrostatics, it becomes obvious that this theoretical hypothesis cannot work. The ionic diffusion that would be at the origin of this potential cannot take place. Indeed, the topology and the forces involved definitively exclude the current model, which must absolutely be revised according to the current state of our knowledge and allow an advance in the understanding of the phenomena and open new research perspectives.

The most common and taught membrane theory assumes that the membrane behaves as a kind of electrical capacitance that is exposed to an electrical current generated by an ionic flow. If this statement is verifiable, it can be confirmed by the laws of physics, mathematics and in particular electricity. We will demonstrate that this hypothesis is not verified and that it is necessary to modify biophysics according to already established and experimentally verified principles of physics.

High-throughput screening technologies continues to produce large amounts of multiomics data from different populations and cell types for various diseases, such as cancer. However, analysis of such data encounters difficulties due to cancer heterogeneity, further exacerbated by human biological complexity and genomic variability. There is a need to redefine the drug discovery development pipeline, bringing an Artificial Intelligence (AI)-powered informational view that integrates relevant biological information and explores new ways to develop effective anticancer approaches. Here, we show SynPred, an interdisciplinary approach that leverages specifically designed ensembles of AI-algorithms, links omics and biophysical traits to predict synergistic anticancer drug synergy. SynPred exhibits state-of-the-art performance metrics: accuracy – 0.85, precision – 0.77, recall – 0.75, AUROC – 0.82, and F1-score - 0.76 in an independent test set. Moreover, data interpretability was achieved by deploying the most current and robust feature importance approaches. A simple web-based application available online at http://www.moreiralab.com/resources/synpred/ was constructed to predict synergistic anticancer drug combinations requiring only the upload of the two drug SMILES to be tested, allowing easy access by non-expert researchers.

Of the roughly 20,000 canonical human protein sequences, as of September 15, 2020, 6,937 proteins have had their full or partial, medium- to high-resolution structures determined by x-ray crystallography or other methods. Which of these proteins dominate the Protein Data Bank (the PDB) and why? In this paper, we list the 273 top human protein structures based on the number of their PDB entries. This set of proteins accounts for more than 40% of all available human PDB entries and represent past trends as well as current status for protein structural biology. We briefly discuss the relationship which some of the prominent protein structures have with protein research as a whole and mention their relevance to human diseases. The top-10 soluble and membrane proteins are all well-known (most of their first structures being deposited more than 30 years ago). Overall, there is no dramatic change in recent trends in the PDB. Remarkably, the number of structure depositions has grown nearly exponentially over the last 10 or more years (with a doubling time of 7 yrs for proteins from all organisms). Growth in human protein structures is slightly faster (at 5.9 yrs, while E.Coli and Mouse+Rat protein structures accumulate more slowly, Zebrafish protein structures are growing most, at a doubling every 3.7 years, albeit starting from only approx. 100 structure entries in 2010). The information may be informative to senior scientists but also inspire researchers who are new to protein science, providing the year 2020 snap-shot for the state of protein structural biology.

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.

Membrane potential generation and maintenance are fundamental parts of membrane theory. However, since its inception, theories and assumptions have remained simple, whereas the amount of knowledge available has only grown. Unfortunately, this simplification and reductionism lead to its loss of validity on both microscopic and macroscopic scales. This article shows that assumptions made outside the context of factual reality lead to contradictions that cannot be compared with science. Membrane theory must be thoroughly revised, taking into account all forgotten and unanswered assumptions and questions to conform to facts and science to maintain credibility.

Life sciences are increasingly benefiting from multidisciplinary research, combining biology, chemistry and physics. At their intersection, biothermodynamics applies the quantitative framework of thermodynamics to life processes and is a potentially interesting subject for life science students. Here a method is proposed to teach undergraduate life science students the basics of biothermodynamics, building and expanding on the concept of Gibbs energy learned as a part of the physical or general chemistry course. The discussion begins with the role of Gibbs energy as the driving force of metabolism and growth. Moreover, a biological example is proposed of the enthalpy-entropy combinations resulting in negative Gibbs energy, which should be interesting to life science students. Gibbs energy is then used to explain microorganism growth rates, using a simple thermodynamic growth model. Finally, multiplication of viruses is considered, including SARS-CoV-2, using Gibbs energy to explain the hijacking of host cell metabolism. Implementation of the proposed biothermodynamics material in classroom is discussed. The paper should be useful as material for making lectures for undergraduate students and as a starting point for anyone beginning to do research in biothermodynamics.

Influenza is a highly contagious, acute respiratory illness, which represents one of the main health issues worldwide. Even though some antivirals are available, the alarming increase of virus strains resistant to them highlights the need to find new drugs. Previously, Superti et al. have deeper investigated the mechanism of the anti-Influenza virus effect of bovine Lactoferrin (bLf) and the role of its tryptic fragments (the N and C-lobes) in the antiviral activity. Recently, through a truncation library, we identified the tetrapeptides, SKHS (1) and SLDC (2), derived from bLf C-lobe fragment 418-429, which were able to bind hemagglutinin (HA) and inhibit cell infection in a concentration range of femto- to picomolar. Starting from these results, in this work, we initiated a systematic SAR study on the peptides mentioned above, through an Alanine scanning approach. We carried out binding affinity measurements by microscale thermophoresis (MST) and Surface Plasmon Resonance (SPR), and hemagglutination inhibition (HI) and virus neutralization (NT) assays on synthesized peptides. Computational studies were performed to identify possible lig-and-HA interactions. Results obtained led to the identification of an interesting peptide endowed with broad anti-Influenza activity and able to inhibit viral infection to a greater extent of reference peptide.

Insulin lispro was the first fast acting insulin analogue to obtain regulatory approval for therapeutic use. This article puts forward a novel biophysical mechanism where the net impact of the simple B28Pro-B29Lys exchange from regular insulin to insulin lispro is the establishment of a novel set of interfacial electrostatic interactions between Lys28 of insulin lispro and Asp12 of insulin receptor (IR). In addition, a set of structural analysis was presented in this article to further strengthen the binding of insulin lispro to IR, where two polar amino acid residues (Gln51 and Asn74 of insulin lispro) were put forward as two potential targets for site-directed mutagenesis of insulin lispro at its binding interface with IR.

The optimization of ligand-receptor binding affinities is essential for enhancing therapeutic efficacy and specificity, particularly in the design of peptide-based drugs. ziconotide, a potent blocker of the N-type calcium channel CaV2.2, has demonstrated therapeutic potential in the treatment of moderate to severe chronic pain. However, there is limited structural and binding affinity data available for scaling the design of ziconotide analogues with improved efficacy and specificity. In this study, I present a structural biophysics-based approach to develop a scalable in silico framework for generating and analyzing ligand-receptor binding affinity landscapes, focusing on ziconotide and its receptor, CaV2.2. This framework integrates advanced computational structural tools and binding affinity calculations to produce high-accuracy structural and intermolecular binding data. The insights gained from this scalable approach will guide the design of ziconotide analogues with enhanced efficacy, offering a powerful computational workflow for high-throughput ligand optimization. Additionally, when combined with artificial intelligence (AI) algorithms, this computational workflow generates high-accuracy structural and biophysical data, towards an AI- and structural biophysics-driven paradigm shift in peptide discovery and design.

The vagina is an essential component of the female reproductive system and is responsible for providing female sexual satisfaction. Vaginal smooth muscle contraction plays a crucial role in various physiological processes, including sexual arousal, childbirth, and urinary continence. In pathophysiological conditions such as pelvic floor disorders, aberrations in vaginal smooth muscle function can lead to urinary incontinence and pelvic organ prolapse. A set of cellular and sub-cellular physiological mechanisms regulates the contractile properties of the vaginal smooth muscle cells. Calcium influx is a crucial determinant of smooth muscle contraction, facilitated through voltage-dependent calcium channels and calcium release from intracellular stores. Comprehensive reviews on smooth muscle biophysics are relatively scarce within the scientific literature, likely due to the complexity and specialized nature of the topic. The objective of this review is to provide a comprehensive description of alterations in the cellular physiology of vaginal smooth muscle contraction. The benefit associated with this particular approach is that conducting a comprehensive examination of the cellular mechanisms underlying contractile activation will enable the creation of more targeted therapeutic agents to control vaginal contraction disorders.

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 generation and maintenance of membrane potential is a fundamental part of Membrane Pump Theory. One of the key points of this hypothesis is based on a natural or facilitated molecular diffusion through several types of ion channels and pumps like the Na/K ATPase. Following the principles of chemistry, electrostatics and geometry, it becomes clear that ion channels cannot function in this way. The ions channels cannot by their location have both a filter function and be ion concentrators, and the Na/K pump by its position in the membrane and by the proposed assumptions is not able to perform its regulatory function. The current model must absolutely be revised according to the current state of our knowledge and allow an advance in the understanding of the phenomena opening new research perspectives.

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.

The definition, origin and recreation of life remain elusive. As others have suggested, only once we put life into reductionist physical terms will we be able to solve those questions. To that end, this work proposes the phenomenon of life to be the product of two dissipative mechanisms. From them, one reinterprets extant biological life and deduces a testable scenario for its origin. The proposed theory of life allows its replication, reinterprets ecological evolution, creates new constraints on the search for life and lays the foundations for groundbreaking technologies.

This interdisciplinary work communicates identification and quantification of elements responsible for the bioactive potency of leaves from pointed gourd, trichosanthes dioica,using laser induced breakdown spectroscopy (LIBS). Calibration-free LIBS determines the presence of various trace and major elements, their concentrations, and ratios in which they are present in the leaves. The presence of specific elemental ratios of magnesium/sodium and magnesium/potassium could be promising for managing diabetes mellitus. Variable doses of 500, 750, 1000, and 1250 mg per kg body weight of aqueous extract from trichosanthes dioica leaves are administered. A dose of 1000 mg per kg body weight is found to be the most effective. Based on encouraging results, the extract could be harvested to serve as anti-diabetic medication for diabetes and associated symptoms.

The advancement of computational drug discovery and design necessitates continuous innovation to enhance the accuracy and scope of predictive models for early-stage drug research and development. This article introduces a novel workflow for in silico generation of structural and intermolecular binding affinity data with reasonable accuracy, combining two computational tools: Modeller and Prodigy. By leveraging synthetic structural and biophysical data, this approach addresses the limitations of existing experimental datasets, generating extensive, high-quantity binding affinity data with reasonable accuracy for biomolecular binding pairs, which broadens the horizon of computational biomolecule design and discovery by enabling extensive exploration of the sequence space of biomolecular binding pairs, and narrows the gap between experimental binding affinity data and its unexplored territories. Overall, this article presents a methodological advance to enhance the accuracy and scope of computational biomolecule discovery and design, paves the way for the development of preclinical candidates with improved efficacy and specificity, and holds transformative potential for further advancements in artificial intelligence-enabled biomolecule discovery and design in the future.

Preeclampsia, a hypertensive disease of pregnancy of unknown etiology, is intensely studied as a model of cardiovascular (CVD) disease not only due to multiple shared pathologic elements, but also because changes that develop over decades in CVD disease appear and resolve within days in preeclampsia. Those affected by preeclampsia and their offspring experience increased lifetime risks of CVD. At the systemic level, preeclampsia is characterized by increased cellular, membrane, and blood levels of cholesterol, however, cholesterol dependent signaling such as canonical Wnt/βcatenin, Hedgehog, and endothelial nitric oxide synthase is downregulated indicating a cholesterol deficit with upregulation of cholesterol synthesis and efflux. Hypoxia related signaling in preeclampsia also appears paradoxical with increased Hypoxia Inducible Factors (HIF) in the placenta but measurably increased oxygen in maternal blood in placental villous spaces. This review addresses the molecular mechanisms by which excessive systemic cholesterol and deficient cholesterol dependent signaling may arise from the effects of dietary lipid variance causing the cellular hypoxia which characterizes preeclampsia.

e Magnetic Ion Modulation (MIM) Theory presents a unified and groundbreaking mathematical framework for controlling and predicting ion behavior in both biological and non-biological systems using external magnetic fields. Central to the theory is the **Ion Modulation Constant** (Tn), a context-specific scaling factor that quantifies the number of ions modulated under specific conditions. This constant, although not universal, allows for accurate modeling of ion modulation in systems ranging from neuronal ion channels to plasma physics and quantum computing. The MIM equation incorporates ion properties such as magnetic moment and permeability, providing insights into neuromodulation for treating chronic pain and epilepsy, plasma confinement in fusion research, and stabilization of ion qubits in quantum computing. This paper includes detailed derivations of the Ion Modulation Constant, examples of its application to specific ions, and a comprehensive exploration of potential experimental validations.