Molecular Dynamics Simulations of Membrane Permeability

Molecular Dynamics Simulations of the Eye Lens Water Channel Aquaporin 0 from Fish

Aquaporin 0 (AQP0) plays a key role in water circulation in the eye lens through a variety of functions. In contrast to mammalian genomes, zebrafish contains two aqp0 genes leading to a separation of AQP0 multiple functions between the two gene products, Aqp0a and Aqp0b. A notable feature of the zebrafish AQP0 paralogs is the increased water permeability of Aqp0b relative to Aqp0a as well as a severa lfold increase relative to mammalian AQP0. Here, we report equilibrium molecular dynamics (MD) simulations on the microsecond timescale to identify the structural basis underlying the differences in water permeability between zebrafish AQP0 paralogs and between AQP0 mammalian and fish orthologs. Our simulations are able to reproduce the experimental trends in water permeability. Our results suggest that a substitution of a key Y23 residue in mammalian AQP0 for F23 in fish AQP0 orthologs introduces significant changes in the conformational dynamics of the CS-I structural motif, which, in conjunction with different levels of hydration of the channel vestibule, can account for the differences in permeabilities between fish and mammalian AQP0 orthologs and between zebrafish AQP0 paralogs.

Application of Generative Artificial Intelligence in Predicting Membrane Partitioning of Drugs: Combining Denoising Diffusion Probabilistic Models and MD Simulations Reduces the Computational Cost to One-Third

The optimal interaction of drugs with plasma membranes and membranes of subcellular organelles is a prerequisite for desirable pharmacology. Importantly, for drugs targeting the transmembrane lipid-facing sites of integral membrane proteins, the relative affinity of a drug to the bilayer lipids compared to the surrounding aqueous phase affects the partitioning, access, and binding of the drug to the target site. Molecular dynamics (MD) simulations, including enhanced sampling techniques such as steered MD, umbrella sampling (US), and metadynamics, offer valuable insights into the interactions of drugs with the membrane lipids and water in atomistic detail. However, these methods are computationally prohibitive for the high-throughput screening of drug candidates. This study shows that applying denoising diffusion probabilistic models (DDPMs), a generative AI method, to US simulation data reduces the computational cost significantly. Specifically, the models used only partial (one-third) data from the US simulations and reproduced the complete potential of mean force (PMF) profiles for three FDA-approved drugs (β2-adrenergic agonists) and ∼20 biologically relevant chemicals with known experimentally characterized bilayer locations. Intriguingly, the model can predict the solvation-free energies for partitioning and crossing the bilayer, preferred bilayer locations (low-energy well), and orientations of the ligands with high accuracy. The results indicate that DDPMs can be used to characterize the complete membrane partitioning profile of drug molecules using fewer umbrella sampling simulations at select positions along the bilayer normal (z-axis), irrespective of their amphiphilic-lipophilic-cephalophilic characteristics.

Quantifying Induced Dipole Effects in Small Molecule Permeation in a Model Phospholipid Bilayer

The cell membrane functions as a semipermeable barrier that governs the transport of materials into and out of cells. The bilayer features a distinct dielectric gradient due to the amphiphilic nature of its lipid components. This gradient influences various aspects of small molecule permeation and the folding and functioning of membrane proteins. Here, we employ polarizable molecular dynamics simulations to elucidate the impact of the electronic environment on the permeation process. We simulated eight distinct amino-acid side chain analogs within a 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine bilayer using the Drude polarizable force field (FF). Our approach includes both unbiased and umbrella sampling simulations. By using a polarizable FF, we sought to investigate explicit dipole responses in relation to local electric fields along the membrane normal. We evaluate molecular dipole moments, which exhibit variation based on their localization within the membrane, and compare the outcomes with analogous simulations using the nonpolarizable CHARMM36 FF. This comparative analysis aims to discern characteristic differences in the free energy surfaces of permeation for the various amino-acid analogs. Our results provide the first systematic quantification of the impact of employing an explicitly polarizable FF in this context compared to the fixed-charge convention inherent to nonpolarizable FFs, which may not fully capture the influence of the membrane dielectric gradient.

Molecular design of hydroxamic acid-based derivatives as urease inhibitors of Helicobacter pylori

Helicobacter pylori is the main causative agent of gastric cancer, especially non-cardiac gastric cancers. This bacterium relies on urease producing much ammonia to colonize the host. Herein, the study provides valuable insights into structural patterns driving urease inhibition for high-activity molecules designed via exploring known inhibitors. Firstly, an ensemble model was devised to predict the inhibitory activity of novel compounds in an automated workflow (R2 = 0.761) that combines four machine learning approaches. The dataset was characterized in terms of chemical space, including molecular scaffolds, clustering analysis, distribution for physicochemical properties, and activity cliffs. Through these analyses, the hydroxamic acid group and the benzene ring responsible for distinct activity were highlighted. Activity cliff pairs uncovered substituents of the benzene ring on hydroxamic acid derivatives are key structures for substantial activity enhancement. Moreover, 11 hydroxamic acid derivatives were designed, named mol1-11. Results of molecular dynamic simulations showed that the mol9 exhibited stabilization of the active site flap's closed conformation and are expected to be promising drug candidates for Helicobacter pylori infection and further in vitro, in vivo, and clinical trials to demonstrate in future.

Injectable hydrogel based on liposome self-assembly for controlled release of small hydrophilic molecules

Controlled release of low molecular weight hydrophilic drugs, administered locally, allows maintenance of high concentrations at the target site, reduces systemic side effects, and improves patient compliance. Injectable hydrogels are commonly used as a vehicle. However, slow release of low molecular weight hydrophilic drugs is very difficult to achieve, mainly due to a rapid diffusion of the drug out of the drug delivery system. Here we present an injectable and self-healing hydrogel based entirely on the self-assembly of liposomes. Gelation of liposomes, without damaging their structural integrity, was induced by modifying the cholesterol content and surface charge. The small hydrophilic molecule, sodium fluorescein, was loaded either within the extra-liposomal space or encapsulated into the aqueous cores of the liposomes. This encapsulation strategy enabled the achievement of controlled and adjustable release profiles, dependent on the mechanical strength of the gel. The hydrogel had a high mechanical strength, minimal swelling, and slow degradation. The liposome-based hydrogel had prolonged mechanical stability in vivo with benign tissue reaction. This work presents a new class of injectable hydrogel that holds promise as a versatile drug delivery system. STATEMENT OF SIGNIFICANCE: The porous nature of hydrogels poses a challenge for delivering small hydrophilic drug, often resulting in initial burst release and shorten duration of release. This issue is particularly pronounced with physically crosslinked hydrogels, since their matrix can swell and dissipate rapidly, but even in cases where the polymers in the hydrogel are covalently cross-linked, small molecules can be rapidly released through its porous mesh. Here we present an injectable self-healing hydrogel based entirely on the self-assembly of liposomes. Small hydrophilic molecules were entrapped inside the extra-liposomal space or loaded into the aqueous cores of the liposomes, allowing controlled and tunable release profiles.

A methodology of quantifying membrane permeability based on returning probability theory and molecular dynamics simulation

We propose a theoretical approach to estimate the permeability coefficients of substrates (permeants) for crossing membranes from donor (D) phase to acceptor (A) phase by means of molecular dynamics (MD) simulation. A fundamental aspect of our approach involves reformulating the returning probability (RP) theory, a rigorous bimolecular reaction theory, to describe permeation phenomena. This reformulation relies on the parallelism between permeation and bimolecular reaction processes. In the present method, the permeability coefficient is represented in terms of the thermodynamic and kinetic quantities for the reactive (R) phase that exists within the inner region of a membrane. One can evaluate these quantities using multiple MD trajectories starting from phase R. We apply the RP theory to the permeation of ethanol and methylamine at different concentrations (infinitely dilute and 1 mol % conditions of permeants). Under the 1 mol% condition, the present method yields a larger permeability coefficient for ethanol (0.12 ± 0.01 cm s-1) than for methylamine (0.069 ± 0.006 cm s-1), while the values of the permeability coefficient are satisfactorily close to those obtained from the brute-force MD simulations (0.18 ± 0.03 and 0.052 ± 0.005 cm s-1 for ethanol and methylamine, respectively). Moreover, upon analyzing the thermodynamic and kinetic contributions to the permeability, we clarify that a higher concentration dependency of permeability for ethanol, as compared to methylamine, arises from the sensitive nature of ethanol's free-energy barrier within the inner region of the membrane against ethanol concentration.

Improving the Accuracy of Permeability Data to Gain Predictive Power: Assessing Sources of Variability in Assays Using Cell Monolayers

The ability to predict the rate of permeation of new compounds across biological membranes is of high importance for their success as drugs, as it determines their efficacy, pharmacokinetics, and safety profile. In vitro permeability assays using Caco-2 monolayers are commonly employed to assess permeability across the intestinal epithelium, with an extensive number of apparent permeability coefficient (Papp) values available in the literature and a significant fraction collected in databases. The compilation of these Papp values for large datasets allows for the application of artificial intelligence tools for establishing quantitative structure-permeability relationships (QSPRs) to predict the permeability of new compounds from their structural properties. One of the main challenges that hinders the development of accurate predictions is the existence of multiple Papp values for the same compound, mostly caused by differences in the experimental protocols employed. This review addresses the magnitude of the variability within and between laboratories to interpret its impact on QSPR modelling, systematically and quantitatively assessing the most common sources of variability. This review emphasizes the importance of compiling consistent Papp data and suggests strategies that may be used to obtain such data, contributing to the establishment of robust QSPRs with enhanced predictive power.

Leveraging Machine Learning-Guided Molecular Simulations Coupled with Experimental Data to Decipher Membrane Binding Mechanisms of Aminosterols

Understanding the molecular mechanisms of the interactions between specific compounds and cellular membranes is essential for numerous biotechnological applications, including targeted drug delivery, elucidation of the drug mechanism of action, pathogen identification, and novel antibiotic development. However, estimation of the free energy landscape associated with solute binding to realistic biological systems is still a challenging task. In this work, we leverage the Time-lagged Independent Component Analysis (TICA) in combination with neural networks (NN) through the Deep-TICA approach for determining the free energy associated with the membrane insertion processes of two natural aminosterol compounds, trodusquemine (TRO), and squalamine (SQ). These compounds are particularly noteworthy because they interact with the outer layer of neuron membranes, protecting them from the toxic action of misfolded proteins involved in neurodegenerative disorders, in both their monomeric and oligomeric forms. We demonstrate how this strategy could be used to generate an effective collective variable for describing solute absorption in the membrane and for estimating free energy landscape of translocation via on-the-fly probability enhanced sampling (OPES) method. In this context, the computational protocol allowed an exhaustive characterization of the aminosterol entry pathway into a neuron-like lipid bilayer. Furthermore, it provided accurate prediction of membrane binding affinities, in close agreement with the experimental binding data obtained by using fluorescently labeled aminosterols and large unilamellar vesicles (LUVs). The findings contribute significantly to our understanding of aminosterol entry pathways and aminosterol-lipid membrane interactions. Finally, the computational methods deployed in this study further demonstrate considerable potential for investigating membrane binding processes.

Simulating the Skin Permeation Process of Ionizable Molecules

It is commonly assumed that ionizable molecules, such as drugs, permeate through the skin barrier in their neutral form. By using molecular dynamics simulations of the charged and neutral states separately, we can study the dynamic protonation behavior during the permeation process. We have studied three weak acids and three weak bases and conclude that the acids are ionized to a larger extent than the bases, when passing through the headgroup region of the lipid barrier structure, at pH values close to their pKa. It can also be observed that even if these dynamic protonation simulations are informative, in the cases studied herein they are not necessary for the calculation of permeability coefficients. It is sufficient to base the calculations only on the neutral form, as is commonly done.

An Investigation into the Transdermal Behavior of Active Ingredients by Combination of Experiments and Multiscale Simulations

Transdermal behavior is a critical aspect of studying delivery systems and evaluating the efficacy of cosmetics. However, existing methods face challenges such as lengthy experiments, high cost, and limited model accuracy. Therefore, developing accurate transdermal models is essential for formulation development and effectiveness assessment. In this study, we developed a multiscale model to describe the transdermal behavior of active ingredients in the stratum corneum. Molecular dynamics simulations were used to construct lipid bilayers and determine the diffusion coefficients of active ingredients in different regions of these bilayers. These diffusion coefficients were integrated into a multilayer lipid pathway model using finite element simulations. The simulation results were in close agreement with our experimental results for three active ingredients (mandelic acid (MAN), nicotinamide (NIC), and pyruvic acid (PYR)), demonstrating the effectiveness of our multiscale model. This research provides valuable insights for advancing transdermal delivery methods.

Interaction of MRI Contrast Agent [Gd(DOTA)]- with Lipid Membranes: A Molecular Dynamics Study

Contrast agents are important imaging probes in clinical MRI, allowing the identification of anatomic changes that otherwise would not be possible. Intensive research on the development of new contrast agents is being made to image specific pathological markers or sense local biochemical changes. The most widely used MRI contrast agents are based on gadolinium(III) complexes. Due to their very high charge density, they have low permeability through tight biological barriers such as the blood-brain barrier, hampering their application in the diagnosis of neurological disorders. In this study, we explore the interaction between the widely used contrast agent [Gd(DOTA)]- (Dotarem) and POPC lipid bilayers by means of molecular dynamics simulations. This metal complex is a standard reference where several chemical modifications have been introduced to improve key properties such as bioavailability and targeting. The simulations unveil detailed insights into the agent's interaction with the lipid bilayer, offering perspectives beyond experimental methods. Various properties, including the impact on global and local bilayer properties, were analyzed. As expected, the results indicate a low partition coefficient (KP) and high permeation barrier for this reference compound. Nevertheless, favorable interactions are established with the membrane leading to moderately long residence times. While coordination of one inner-sphere water molecule is maintained for the membrane-associated chelate, the physical-chemical attributes of [Gd(DOTA)]- as a MRI contrast agent are affected. Namely, increases in the rotational correlation times and in the residence time of the inner-sphere water are observed, with the former expected to significantly increase the water proton relaxivity. This work establishes a reference framework for the use of simulations to guide the rational design of new contrast agents with improved relaxivity and bioavailability and for the development of liposome-based formulations for use as imaging probes or theranostic agents.

PyRETIS 3: Conquering rare and slow events without boundaries

We present and discuss the advancements made in PyRETIS 3, the third instalment of our Python library for an efficient and user-friendly rare event simulation, focused to execute molecular simulations with replica exchange transition interface sampling (RETIS) and its variations. Apart from a general rewiring of the internal code towards a more modular structure, several recently developed sampling strategies have been implemented. These include recently developed Monte Carlo moves to increase path decorrelation and convergence rate, and new ensemble definitions to handle the challenges of long-lived metastable states and transitions with unbounded reactant and product states. Additionally, the post-analysis software PyVisa is now embedded in the main code, allowing fast use of machine-learning algorithms for clustering and visualising collective variables in the simulation data.

Cryptotanshinone-Induced Permeabilization of Model Phospholipid Membranes: A Biophysical Study

The Danshen terpenoid cryptotanshinone (CPT) is gaining enormous interest in light of its various outstanding biological activities. Among those, CPT has been shown to interact with cell membranes and, for instance, to have antibacterial activity. Several works have shown that CPT alone, or in combination with other drugs, can effectively act as an antibiotic against various infectious bacteria. Some authors have related the mechanism underlying this action to CPT-membrane interaction. This work shows that CPT readily partitions into phosphatidylcholine membranes, but there is a limiting capacity of accommodation of ca. 1 mol CPT to 3 mol phospholipid. The addition of CPT to unilamellar liposomes composed of 1-palmitoyl-2-oleoylphosphatidylcholine (POPC) causes membrane permeabilization, as shown by fluorescent probe leakage. This process has been kinetically studied, as well as its modulation by incorporation of phosphatidylethanolamine or phosphatidylglycerol, as a model for pathogenic cell membranes. The thermotropic behavior of 1,2-dimyristoylphosphatidylcholine (DMPC) model membranes is weakly affected by CPT, but the terpenoid causes significant dehydration of the polar region of the bilayer and weak disordering of the acyl chain palisade, as observed in Fourier-transform infrared spectroscopy (FTIR) results. Small-angle X-ray scattering (SAXS) shows that CPT increases DMPC bilayer thickness, which could be due to localization near the phospholipid/water interface. Molecular dynamics (MD) simulations show that the lateral diffusion coefficient of the phospholipid increases with the presence of CPT. CPT extends from the polar head region to the center of the bilayer, being centered between the carbonyl groups and the unsaturated region of the POPC, where there is greater overlap. Interestingly, the free energy profiles of a water molecule crossing the lipid membrane show that the POPC membrane becomes more permeable in the presence of CPT. In summary, our results show that CPT perturbs the physicochemical properties of the phospholipid membrane and compromises its barrier function, which could be of relevance to explain part of its antimicrobial or anticancer activities.

Interfacial Glucose to Regulate Hydrated Lipid Bilayer Properties: Influence of Concentrations

A series of atomistic molecular dynamics (MD) simulations were carried out with a hydrated 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) bilayer with the variation of glucose concentrations from 0 to 30 wt % in the presence of 0.3 M NaCl. The study suggested that although the thickness of the lipid bilayer dropped significantly with the increase in glucose concentration, it expanded laterally at high glucose levels due to the intercalation of glucose between the headgroups of adjacent lipids. We adopted the surface assessment via the grid evaluation method to compute the deviation of the bilayer's key structural features for the different amounts of glucose present. This suggested that the accumulation of glucose molecules near the headgroups influences the local lipid bilayer undulation and crimping of the lipid tails. We find that the area compressibility modulus increases with the glucose level, causing enhanced bilayer rigidity arising from the slow lateral diffusion of lipids. The restricted lipid motion at high glucose concentrations controls the sustainability of the curved bilayer surface. Calculations revealed that certain orientations of CO → of interfacial glucose with the PN → of lipid headgroups are preferred, which helps the glucose to form direct hydrogen bonds (HBs) with the lipid headgroups. Such lipid-glucose (LG) HBs relax slowly at low glucose concentrations and exhibit a higher lifetime, whereas fast structural relaxation of LG HBs with a shorter lifetime was noticed at a higher glucose level. In contrast, lipid-water (LW) HBs exhibited a higher lifetime at a higher glucose level, which gradually decreased with the glucose level lowering. The study interprets that the glucose concentration-driven LW and LG interactions are mutually inclusive. Our detailed analysis will exemplify small saccharide concentration-driven membrane stabilizing efficiency, which is, in general, helpful for drug delivery study.

Electroosmotic Sensing of Uncharged Peptides and Differentiating Their Phosphorylated States Using Nanopores

The correct characterization and identification of different kinds of proteins is crucial for the survival and development of living organisms, and proteomics research promotes the analysis and understanding of future genome functions. Nanopore technique has been proved to accurately identify individual nucleotides. However, accurate and rapid protein sequencing is difficult due to the variability of protein structures that contains more than 20 amino acids, and it remains very challenging especially for uncharged peptides as they can not be electrophoretically driven through the nanopore. Graphene nanopores have the advantages of high accuracy, sensitivity and low cost in identifying protein phosphorylation modifications. Here, by using all-atom molecular dynamics simulations, charged graphene nanopores are employed to electroosmotically capture and sense uncharged peptides. By further mimicking AFM manipulation of single molecules, it is also found that the uncharged peptides and their phosphorylated states could also be differentiated by both the ionic current and pulling force signals during their pulling processes through the nanopore with a slow and constant velocity. The results shows ability of using nanopores to detect and discriminate single amino acid and its phosphorylation, which is essential for the future low-cost and high-throughput sequencing of protein residues and their post-translational modifications.

Gaussian accelerated molecular dynamics simulations facilitate prediction of the permeability of cyclic peptides

Despite their widespread use as therapeutics, clinical development of small molecule drugs remains challenging. Among the many parameters that undergo optimization during the drug development process, increasing passive cell permeability (i.e., log(P)) can have some of the largest impact on potency. Cyclic peptides (CPs) have emerged as a viable alternative to small molecules, as they retain many of the advantages of small molecules (oral availability, target specificity) while being highly effective at traversing the plasma membrane. However, the relationship between the dominant conformations that typify CPs in an aqueous versus a membrane environment and cell permeability remain poorly characterized. In this study, we have used Gaussian accelerated molecular dynamics (GaMD) simulations to characterize the effect of solvent on the free energy landscape of lariat peptides, a subset of CPs that have recently shown potential for drug development (Kelly et al., JACS 2021). Differences in the free energy of lariat peptides as a function of solvent can be used to predict permeability of these molecules, and our results show that permeability is most greatly influenced by N-methylation and exposure to solvent. Our approach lays the groundwork for using GaMD as a way to virtually screen large libraries of CPs and drive forward development of CP-based therapeutics.

Generating Concentration Gradients across Membranes for Molecular Dynamics Simulations of Periodic Systems

The plasma membrane forms the boundary between a living entity and its environment and acts as a barrier to permeation and flow of substances. Several computational means of calculating permeability have been implemented for molecular dynamics (MD) simulations-based approaches. Except for double bilayer systems, most permeability studies have been performed under equilibrium conditions, in large part due to the challenges associated with creating concentration gradients in simulations utilizing periodic boundary conditions. To enhance the scientific understanding of permeation and complement the existing computational means of characterizing membrane permeability, we developed a non-equilibrium method that enables the generation and maintenance of steady-state gradients in MD simulations. We utilize PBCs advantageously by imposing a directional bias to the motion of permeants so that their crossing of the boundary replenishes the gradient, like a previous study on ions. Under these conditions, a net flow of permeants across membranes may be observed to determine bulk permeability by a direct application of J=PΔc. In the present study, we explore the results of its application to an exemplary O2 and POPC bilayer system, demonstrating accurate and precise permeability measurements. In addition, we illustrate the impact of permeant concentration and the choice of thermostat on the permeability. Moreover, we demonstrate that energetics of permeation can be closely examined by the dissipation of the gradient across the membrane to gain nuanced insights into the thermodynamics of permeability.

Effect of Glycone Diversity on the Interaction of Triterpenoid Saponins and Lipid Bilayers

Triterpenoid saponins are organic compounds widely available in the plant kingdom. These molecules have received extensive attention due to their antibacterial activity against both Gram-negative and Gram-positive bacteria. Recent studies identified the antibacterial activity of saponins closely relates to their interaction with bacterial membrane lipids; however, molecular details of this interaction remain unclear. Increased understanding of the mechanisms to disrupt bacterial lipid bilayers can help to mitigate development of antibiotic resistance. Here, we examined the effect of chemical structure and deprotonation states of saponin on its interaction with a bacterial membrane model using molecular dynamics simulations. We run multiple simulations with a ternary lipid mixture of POPE/POPG/DPPG (80/15/5 mol %) and different saponin molecules. While all saponin structures can permanently bind the membrane, their location and orientation inside the bilayer depend on the sugar chains attached to their backbone. Similarly, cluster formation and stability also depend on the chemical structure of the saponin molecule. Deprotonation site affects interactions with the bilayer by modulating hydrophilicity of the molecules. At the low concentrations simulated in this work, there is no statistically significant change in the membrane properties upon saponin(s) binding, but the molecules do preferentially partition to POPE lipid environment.

Impact of the Unstirred Water Layer on the Permeation of Small-Molecule Drugs

Over the last two decades, numerous molecular dynamics (MD) simulation-based investigations have attempted to predict the membrane permeability to small-molecule drugs as indicators of their bioavailability, a majority of which utilize the inhomogeneous solubility diffusion (ISD) model. However, MD-based membrane permeability is routinely 3-4 orders of magnitude larger than the values measured with the intestinal perfusion technique. There have been contentious discussions on the sources of the large discrepancies, and the two indisputable, potentially dominant ones are the fixed protonation state of the permeant and the neglect of the unstirred water layer (UWL). Employing six small-molecule drugs of different biopharmaceutical classification system classes, the current MD study relies on the ISD model but introduces the (de)protonation of the permeant by characterizing the permeation free energy of both neutral and charged states. In addition, the role of the UWL as a potential resistance against permeation is explored. The new MD protocol closely mimics the nature of small-molecule permeation and yields estimates that agree well with in vivo intestinal permeability.

Overlay databank unlocks data-driven analyses of biomolecules for all

Tools based on artificial intelligence (AI) are currently revolutionising many fields, yet their applications are often limited by the lack of suitable training data in programmatically accessible format. Here we propose an effective solution to make data scattered in various locations and formats accessible for data-driven and machine learning applications using the overlay databank format. To demonstrate the practical relevance of such approach, we present the NMRlipids Databank-a community-driven, open-for-all database featuring programmatic access to quality-evaluated atom-resolution molecular dynamics simulations of cellular membranes. Cellular membrane lipid composition is implicated in diseases and controls major biological functions, but membranes are difficult to study experimentally due to their intrinsic disorder and complex phase behaviour. While MD simulations have been useful in understanding membrane systems, they require significant computational resources and often suffer from inaccuracies in model parameters. Here, we demonstrate how programmable interface for flexible implementation of data-driven and machine learning applications, and rapid access to simulation data through a graphical user interface, unlock possibilities beyond current MD simulation and experimental studies to understand cellular membranes. The proposed overlay databank concept can be further applied to other biomolecules, as well as in other fields where similar barriers hinder the AI revolution.

Digestion of lipid micelles leads to increased membrane permeability

Lipid-based drug carriers are an attractive option to solubilise poorly water soluble therapeutics. Previously, we reported that the digestion of a short tail PC lipid (2C6PC) by the PLA2 enzyme has a significant effect on the structure and stability of the micelles it forms. Here, we studied the interactions of micelles of varying composition representing various degrees of digestion with a model ordered (70 mol% DPPC & 30 mol% cholesterol) and disordered (100% DOPC) lipid membrane. Micelles of all compositions disassociated when interacting with the two different membranes. As the percentage of digestion products (C6FA and C6LYSO) in the micelle increased, the disassociation occurred more rapidly. The C6FA inserts preferentially into both membranes. We find that all micelle components increase the area per lipid, increase the disorder and decrease the thickness of the membranes, and the 2C6PC lipid molecules have the most significant impact. Additionally, there is an increase in permeation of water into the membrane that accompanies the insertion of C6FA into the DOPC membranes. We show that the natural digestion of lipid micelles result in molecular species that can enhance the permeability of lipid membranes that in turn result in an enhanced delivery of drugs.

How is Membrane Permeation of Small Ionizable Molecules Affected by Protonation Kinetics?

According to the pH-partition hypothesis, the aqueous solution adjacent to a membrane is a mixture of the ionization states of the permeating molecule at fixed Henderson-Hasselbalch concentrations, such that each state passes through the membrane in parallel with its own specific permeability. An alternative view, based on the assumption that the rate of switching ionization states is instantaneous, represents the permeation of ionizable molecules via an effective Boltzmann-weighted average potential (BWAP). Such an assumption is used in constant-pH molecular dynamics simulations. The inhomogeneous solubility-diffusion framework can be used to compute the pH-dependent membrane permeability for each of these two limiting treatments. With biased WTM-eABF molecular dynamics simulations, we computed the potential of mean force and diffusivity of each ionization state of two weakly basic small molecules: nicotine, an addictive drug, and varenicline, a therapeutic for treating nicotine addiction. At pH = 7, the BWAP effective permeability is greater than that determined by pH-partitioning by a factor of 2.5 for nicotine and 5 for varenicline. To assess the importance of ionization kinetics, we present a Smoluchowski master equation that includes explicitly the protonation and deprotonation processes coupled with the diffusive motion across the membrane. At pH = 7, the increase in permeability due to the explicit ionization kinetics is negligible for both nicotine and varenicline. This finding is reaffirmed by combined Brownian dynamics and Markov state model simulations for estimating the permeability of nicotine while allowing changes in its ionization state. We conclude that for these molecules the pH-partition hypothesis correctly captures the physics of the permeation process. The small free energy barriers for the permeation of nicotine and varenicline in their deprotonated neutral forms play a crucial role in establishing the validity of the pH-partitioning mechanism. Essentially, BWAP fails because ionization kinetics are too slow on the time scale of membrane crossing to affect the permeation of small ionizable molecules such as nicotine and varenicline. For the singly protonated state of nicotine, the computational results agree well with experimental measurements (P1 = 1.29 × 10-7 cm/s), but the agreement for neutral (P0 = 6.12 cm/s) and doubly protonated nicotine (P2 = 3.70 × 10-13 cm/s) is slightly worse, likely due to factors associated with the aqueous boundary layer (neutral form) or leaks through paracellular pathways (doubly protonated form).

Computational approaches to investigate fluoride binding, selectivity and transport across the membrane

The use of molecular dynamics (MD) simulations to study biomolecular systems has proven reliable in elucidating atomic-level details of structure and function. In this chapter, MD simulations were used to uncover new insights into two phylogenetically unrelated bacterial fluoride (F-) exporters: the CLCF F-/H+ antiporter and the Fluc F- channel. The CLCF antiporter, a member of the broader CLC family, has previously revealed unique stoichiometry, anion-coordinating residues, and the absence of an internal glutamate crucial for proton import in the CLCs. Through MD simulations enhanced with umbrella sampling, we provide insights into the energetics and mechanism of the CLCF transport process, including its selectivity for F- over HF. In contrast, the Fluc F- channel presents a novel architecture as a dual topology dimer, featuring two pores for F- export and a central non-transported sodium ion. Using computational electrophysiology, we simulate the electrochemical gradient necessary for F- export in Fluc and reveal details about the coordination and hydration of both F- and the central sodium ion. The procedures described here delineate the specifics of these advanced techniques and can also be adapted to investigate other membrane protein systems.

Passive permeability controls synthesis for the allelochemical sorgoleone in sorghum root exudate

Competition for soil nutrients and water with other plants foster competition within the biosphere for access to these limited resources. The roots for the common grain sorghum produce multiple small molecules that are released via root exudates into the soil to compete with other plants. Sorgoleone is one such compound, which suppresses weed growth near sorghum by acting as a quinone analog and interferes with photosynthesis. Since sorghum also grows photosynthetically, and may be susceptible to sorgoleone action if present in tissues above ground, it is essential to exude sorgoleone efficiently. However, since the P450 enzymes that synthesize sorgoleone are intracellular, the release mechanism for sorgoleone remain unclear. In this study, we conducted an in silico assessment for sorgoleone and its precursors to passively permeate biological membranes. To facilitate accurate simulation, CHARMM parameters were newly optimized for sorgoleone and its precursors. These parameters were used to conduct 1 μs of unbiased molecular dynamics simulations to compare the permeability of sorgoleone with its precursors molecules. We find that interleaflet transfer is maximized for sorgoleone, suggesting that the precursor molecules may remain in the same leaflet for access by biosynthetic P450 enzymes. Since no sorgoleone was extracted during unbiased simulations, we compute a permeability coefficient using the inhomogeneous solubility diffusion model. The requisite free energy and diffusivity profiles for sorgoleone through a sorghum membrane model were determined through Replica Exchange Umbrella Sampling (REUS) simulations. The REUS calculations highlight that any soluble sorgoleone would quickly insert into a lipid bilayer, and would readily transit. When sorgoleone forms aggregates in root exudate as indicated by our equilibrium simulations, aggregate formation would lower the effective concentration in aqueous solution, creating a concentration gradient that would facilitate passive transport. This suggests that sorgoleone synthesis occurs within sorghum root cells and that sorgoleone is exuded by permeating through the cell membrane without the need for a transport protein once the extracellular sorgoleone aggregate is formed.

[Development of Membrane Permeability Coefficient by Means of Novel Molecular Dynamics Methods]

The membrane permeability, and its evaluation, is crucial factor in the process of uptake of compounds from outside to inside the cell and in the inhibition of the activity of disease-causing target proteins. Although molecular dynamics (MD) simulations have been shown to be able to reproduce the conformational changes of compounds occurring during membrane permeation, it is still challenging to extract the membrane permeability at an affordable computational workload solely by conventional MD. Indeed, the time scale accessible by MD is far below the one characterizing the actual permeation process. Phenomena occurring in living organisms escaping the reach of standard MD are generally referred to as biological rare events, and the membrane permeation process is one of them. To overcome this time-scale problem, several enhanced sampling methods have been proposed over the years to improve conformational sampling. In this review, a hybrid sampling method that combines the parallel cascade selection MD (PaCS-MD) and the outlier flooding method (OFLOOD), introduced and developed by our group, is proposed as a tool to study the membrane permeation from structural sampling (rare-event sampling). The obtained trajectories are used to estimate the free energy profiles for the membrane permeation and to compute the membrane permeation coefficients. Moreover, we present an example of application of the free energy reaction network method as a versatile way for incorporating explicitly into reaction coordinates the degrees of freedom related to internal motion.

Designing Reaction Coordinate for Ion-Induced Pore-Assisted Mechanism of Halide Ions Permeation through Lipid Bilayer by Umbrella Sampling

Ion permeation mechanism through lipid membranes helps to understand cellular processes. We propose new reaction coordinates that allow ions to permeate according to their water affinity and interaction with the hydrophilic layer. Simulations were done for three different halides (F-, Cl-, and I-) in two different lipid bilayers, 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) and 1,2-dinervonoyl-sn-glycero-3-phosphocholine (DNPC). It is found that the involvement of the water molecules decreases the free energy barrier. The ions were found to follow different pathways for permeation. Formation of proper pores required a collaboration effort of the hydration shell water molecules and the hydrophilic lipid layer, which was favored in the case of Cl- ions. The optimum charge density and good water affinity of Cl- with respect to F- and I- ions helped to form the pore. The effect was prominently seen in the case of DNPC membrane because of its higher hydrophobic thickness. The umbrella sampling results were compared with other methods such as the Markov state model (MSM) and well-tempered metadynamics (WT-metaD).

Molecular assessment of drug-phospholipid interactions consequent to cancer treatment: a study of anthracycline-induced cardiotoxicity

Cardiotoxicity limits the use of anthracyclines as potent chemotherapeutics. We employ classical molecular dynamics to explore anthracycline interactions with a realistic myocardial membrane and compare to an ideal membrane widely used in literature. The interaction of these two membranes with four anthracyclines; doxorubicin, epirubicin, daunorubicin, and idarubicin are studied. Careful analysis was conducted on three forms of each drug; pristine, primary metabolite, and cationic salt. By examining the molecular residence time near the membrane's surface, the average number of molecule/membrane hydrogen bonds, the immobilization of the molecules near the membrane, and the location of those molecules relative to the mid-plane of the membrane we found out that salt forms exhibit the highest cardiotoxic probability, followed by the metabolites and pristine forms. Additionally, all forms have more affinity to the upper layer of the realistic myocardial membrane. Meanwhile, an ideal membrane consisting of a single type of phospholipids is not capable of capturing the specific interactions of each drug form. These findings confirm that cardiotoxic mechanisms are membrane-layer and drug-form dependent.

tICA-Metadynamics for Identifying Slow Dynamics in Membrane Permeation

Molecular simulations are commonly used to understand the mechanism of membrane permeation of small molecules, particularly for biomedical and pharmaceutical applications. However, despite significant advances in computing power and algorithms, calculating an accurate permeation free energy profile remains elusive for many drug molecules because it can require identifying the rate-limiting degrees of freedom (i.e., appropriate reaction coordinates). To resolve this issue, researchers have developed machine learning approaches to identify slow system dynamics. In this work, we apply time-lagged independent component analysis (tICA), an unsupervised dimensionality reduction algorithm, to molecular dynamics simulations with well-tempered metadynamics to find the slowest collective degrees of freedom of the permeation process of trimethoprim through a multicomponent membrane. We show that tICA-metadynamics yields translational and orientational collective variables (CVs) that increase convergence efficiency ∼1.5 times. However, crossing the periodic boundary is shown to introduce artifacts in the translational CV that can be corrected by taking absolute values of molecular features. Additionally, we find that the convergence of the tICA CVs is reached with approximately five membrane crossings and that data reweighting is required to avoid deviations in the translational CV.

A Programmable Oxygenation Device Facilitates Oxygen Generation and Replenishment to Promote Wound Healing

Inadequate oxygenation is one of the chief culprits for delayed wound healing. However, current oxygen therapies, such as hyperbaric oxygen therapy and topical oxygen therapy, face hurdles in providing sustained and long-term oxygenation to reverse wound hypoxia. Furthermore, their efficacy in rejuvenating wound injury is restricted by limited penetration of oxygen in the wound bed. Herein, this study proposes a programmable and portable oxygenation device (named GUFO oxydevice) by ingeniously integrating i) a controllable oxygen generation and unidirectional transmission system (COGT-UTS), and ii) a supramolecular assembled perfluorinated hyperbranched polymer/gelatin (GUF) hydrogel in which the perfluorinated hyperbranched polymer (FHBP) acts as an oxygen reservoir to ensure sustained and convenient oxygen replenishment and thus directly regulate the hypoxic wound microenvironment. Accelerating the wound healing process by GUFO oxydevice is achieved in both a diabetic rat and an acute porcine wound model without any secondary tissue damages. The present study demonstrates that the GUFO oxydevice holds promise as a practically feasible candidate for wound treatment.

Physical Chemistry of Chloroquine Permeation through the Cell Membrane with Atomistic Detail

We provide a molecular-level description of the thermodynamics and mechanistic aspects of drug permeation through the cell membrane. As a case study, we considered the antimalaria FDA approved drug chloroquine. Molecular dynamics simulations of the molecule (in its neutral and protonated form) were performed in the presence of different lipid bilayers, with the aim of uncovering key aspects of the permeation process, a fundamental step for the drug's action. Free energy values obtained by well-tempered metadynamics simulations suggest that the neutral form is the only permeating protomer, consistent with experimental data. H-bond interactions of the drug with water molecules and membrane headgroups play a crucial role for permeation. The presence of the transmembrane potential, investigated here for the first time in a drug permeation study, does not qualitatively affect these conclusions.

Structure-Based Multilevel Descriptors for High-throughput Screening of Elastomers

To discover new materials, high-throughput screening (HTS) with machine learning (ML) requires universally available descriptors that can accurately predict the desired properties. For elastomers, experimental and simulation data in current descriptors may not be available for all candidates of interest, hindering elastomer discovery through HTS. To address this challenge, we introduce structure-based multilevel (SM) descriptors of elastomers derived solely from molecular structure that is universally available. Our SM descriptors are hierarchically organized to capture both local soft and hard segment structures as well as the global structures of elastomers. With the SM-Morgan Fingerprint (SM-MF) descriptor, one of our SM descriptors, a machine learning model accurately predicts elastomer toughness with a remarkable accuracy of 0.91. Furthermore, an HTS pipeline is established to swiftly screen elastomers with targeted toughness. We also demonstrate the generality and applicability of SM descriptors by using them to construct HTS pipelines for screening elastomers with a targeted critical strain or Young's modulus. The user-friendliness and low computational cost of SM descriptors make them a promising tool to significantly enhance HTS in the search for novel materials.

Capric Acid and Myristic Acid Permeability Enhancers in Curved Liposome Membranes

Liposomes are considered as advanced drug delivery systems for cancer treatment. A generation of pH-sensitive liposomes is being developed that use fatty acids (FAs) as a trigger for drug release in tumor tissues. However, FAs are also known to enhance permeability, and it is unclear whether FAs in liposomes may cause drug leakage or premature drug release. The passive permeability of the drug through the membrane of the liposome is thus a crucial factor for timely drug delivery. To investigate how the curvature and lipid composition of liposomes affect their passive permeability, coarse-grained molecular dynamics were performed. The permeability was determined with a counting method. Flat bilayers and three liposomes with varying diameters were studied, which had varying lipid compositions of dipalmitoylphosphatidylcholine, cholesterol, and deprotonated or neutral saturated FAs. The investigated permeants were water and two other small permeants, which have different free energy profiles (solubility) across the membrane. First, for the curvature effect, our results showed that curvature increases the water permeability by reducing the membrane thickness. The permeability increase for water is about a factor of 1.7 for the most curved membranes. However, a high curvature decreases permeability for permeants with free energy profiles that are a mix of wells and barriers in the headgroup region of the membrane. Importantly, the type of experimental setup is expected to play a dominant role in the permeability value, i.e., whether permeants are escaping or entering the liposomes. Second, for the composition effect, FAs decrease both the area per lipid (APL) and the membrane thickness, resulting in permeability increases of up to 55%. Cholesterol has a similar effect on the APL but has the opposite impact on membrane thickness and permeability. Therefore, FAs and cholesterol have opposing effects on permeability, with cholesterol's effect being slightly stronger in our simulated bilayers. As all permeability values were well within a factor of 2, and with liposomes usually being larger and less curved in experimental applications, it can be concluded that the passive drug release from a pH-sensitive liposome does not seem to be significantly affected by the presence of FAs.

A least-squares-fitting procedure for an efficient preclinical ranking of passive transport across the blood-brain barrier endothelium

The treatment of various disorders of the central nervous system (CNS) is often impeded by the limited brain exposure of drugs, which is regulated by the human blood-brain barrier (BBB). The screening of lead compounds for CNS penetration is challenging due to the biochemical complexity of the BBB, while experimental determination of permeability is not feasible for all types of compounds. Here we present a novel method for rapid preclinical screening of libraries of compounds by utilizing advancements in computing hardware, with its foundation in transition-based counting of the flux. This method has been experimentally validated for in vitro permeabilities and provides atomic-level insights into transport mechanisms. Our approach only requires a single high-temperature simulation to rank a compound relative to a library, with a typical simulation time converging within 24 to 72 h. The method offers unbiased thermodynamic and kinetic information to interpret the passive transport of small-molecule drugs across the BBB.

Advances in Computational Approaches for Estimating Passive Permeability in Drug Discovery

Passive permeation of cellular membranes is a key feature of many therapeutics. The relevance of passive permeability spans all biological systems as they all employ biomembranes for compartmentalization. A variety of computational techniques are currently utilized and under active development to facilitate the characterization of passive permeability. These methods include lipophilicity relations, molecular dynamics simulations, and machine learning, which vary in accuracy, complexity, and computational cost. This review briefly introduces the underlying theories, such as the prominent inhomogeneous solubility diffusion model, and covers a number of recent applications. Various machine-learning applications, which have demonstrated good potential for high-volume, data-driven permeability predictions, are also discussed. Due to the confluence of novel computational methods and next-generation exascale computers, we anticipate an exciting future for computationally driven permeability predictions.

Martini 3 Coarse-Grained Force Field for Cholesterol

Cholesterol plays a crucial role in biomembranes by regulating various properties, such as fluidity, rigidity, permeability, and organization of lipid bilayers. The latest version of the Martini model, Martini 3, offers significant improvements in interaction balance, molecular packing, and inclusion of new bead types and sizes. However, the release of the new model resulted in the need to reparameterize many core molecules, including cholesterol. Here, we describe the development and validation of a Martini 3 cholesterol model, addressing issues related to its bonded setup, shape, volume, and hydrophobicity. The proposed model mitigates some limitations of its Martini 2 predecessor while maintaining or improving the overall behavior.

Translocation Processes of Pt(II)-Based Drugs through Human Breast Cancer Cell Membrane: In Silico Experiments

Breast cancer is one of the most frequent modalities of cancer worldwide, with notable mortality. The medication based on platinum drugs (cisplatin (cddp), carboplatin (cpx), and oxaliplatin (oxa)) is a conventional chemotherapy despite severe side effects and the development of drug resistance. In order to provide a deeper molecular description of the influx and efflux processes of platinum drugs through breast cancer tissues, this study focuses on molecular dynamics (MD) simulations of the passive translocation process through a realistic plasma membrane prototype of human breast cancer cell (c_memb). The results showed that the permeation events were mainly mediated by neutral lipids (DOPC, DOPE, and cholesterol), producing a low and temporary membrane deformation. The drug insertion in the region of polar heads was the most favorable stage of the translocation mechanism, especially for cddp and oxa with potential wells of -8.6 and -9.8 kcal mol-1, respectively. However, the potentials of mean force (PMF) revealed unfavorable kinetics for the permeation of these drugs through lipid tails, with energy barriers of 28.3 (cddp), 32.2 (cpx), and 30.4 kcal mol-1 (oxa). The low permeability coefficients (P) of cpx and oxa, which were 3 and 1 orders of magnitude inferior than for cddp, resulted from the high energy barriers for their traslocation processes through the membrane. The obtained results provide a more accurate picture of the permeation of Pt(II)-based drugs through breast cancer cells, which may be relevant for the design and evaluation of new platinum complexes.

Molecular Docking-Based Identification of Potential Natural Neuroprotective Molecules for Parkinson's Disease

BACKGROUND

Parkinson's disease (PD) is a common progressive neurodegenerative and the prevailing treatments are ineffective in the early stages of the disease. Therefore, other strategies must be devised to halt the steady decrease of dopaminergic neurons in the brain. In Parkinson's disease, a dysregulated ACE/Ang II/AT1R axis in the brain causes free radical damage, apoptosis, and neuronal destruction. Current PD treatments only alleviate symptoms and do not reverse the degradation mechanism of dopaminergic neurons. As a result, it is critical to discover alternate, dependable medicines for the treatment of Parkinson's disease.

METHOD

In the present study, homology modelling of MAS receptor, in silico docking and molecular dynamic studies (MDS) were employed to determine the efficacy of flavonoids as MASR activators.

RESULT

The flavonoids Pterosupin and Amentoflavone exhibited best binding and therefore, the stability of these complexes were evaluated with MDS studies. The Pterosupin-MASR complex demonstrated better stability, stronger interactions and minimal fluctuation than the Amentoflavone-MASR complex.

CONCLUSION

The data from the present study indicated that the flavonoid Pterosupin possesses better binding, favourable pharmacokinetic properties and stability. However, subsequent in vitro and in vivo assessments are necessary to validate its efficacy.

Challenges in Permeability Assessment for Oral Drug Product Development

Drug permeation across the intestinal epithelium is a prerequisite for successful oral drug delivery. The increased interest in oral administration of peptides, as well as poorly soluble and poorly permeable compounds such as drugs for targeted protein degradation, have made permeability a key parameter in oral drug product development. This review describes the various in vitro, in silico and in vivo methodologies that are applied to determine drug permeability in the human gastrointestinal tract and identifies how they are applied in the different stages of drug development. The various methods used to predict, estimate or measure permeability values, ranging from in silico and in vitro methods all the way to studies in animals and humans, are discussed with regard to their advantages, limitations and applications. A special focus is put on novel techniques such as computational approaches, gut-on-chip models and human tissue-based models, where significant progress has been made in the last few years. In addition, the impact of permeability estimations on PK predictions in PBPK modeling, the degree to which excipients can affect drug permeability in clinical studies and the requirements for colonic drug absorption are addressed.

Targeted Protein Degradation: Advances, Challenges, and Prospects for Computational Methods

The therapeutic approach of targeted protein degradation (TPD) is gaining momentum due to its potentially superior effects compared with protein inhibition. Recent advancements in the biotech and pharmaceutical sectors have led to the development of compounds that are currently in human trials, with some showing promising clinical results. However, the use of computational tools in TPD is still limited, as it has distinct characteristics compared with traditional computational drug design methods. TPD involves creating a ternary structure (protein-degrader-ligase) responsible for the biological function, such as ubiquitination and subsequent proteasomal degradation, which depends on the spatial orientation of the protein of interest (POI) relative to E2-loaded ubiquitin. Modeling this structure necessitates a unique blend of tools initially developed for small molecules (e.g., docking) and biologics (e.g., protein-protein interaction modeling). Additionally, degrader molecules, particularly heterobifunctional degraders, are generally larger than conventional small molecule drugs, leading to challenges in determining drug-like properties like solubility and permeability. Furthermore, the catalytic nature of TPD makes occupancy-based modeling insufficient. TPD consists of multiple interconnected yet distinct steps, such as POI binding, E3 ligase binding, ternary structure interactions, ubiquitination, and degradation, along with traditional small molecule properties. A comprehensive set of tools is needed to address the dynamic nature of the induced proximity ternary complex and its implications for ubiquitination. In this Perspective, we discuss the current state of computational tools for TPD. We start by describing the series of steps involved in the degradation process and the experimental methods used to characterize them. Then, we delve into a detailed analysis of the computational tools employed in TPD. We also present an integrative approach that has proven successful for degrader design and its impact on project decisions. Finally, we examine the future prospects of computational methods in TPD and the areas with the greatest potential for impact.

Natamycin interferes with ergosterol-dependent lipid phases in model membranes

Natamycin is an antifungal polyene macrolide that is used as a food preservative but also to treat fungal keratitis and other yeast infections. In contrast to other polyene antimycotics, natamycin does not form ion pores in the plasma membrane, but its mode of action is poorly understood. Using nuclear magnetic resonance (NMR) spectroscopy of deuterated sterols, we find that natamycin slows the mobility of ergosterol and cholesterol in liquid-ordered (Lo) membranes to a similar extent. This is supported by molecular dynamics (MD) simulations, which additionally reveal a strong impact of natamycin dimers on sterol dynamics and water permeability. Interference with sterol-dependent lipid packing is also reflected in a natamycin-mediated increase in membrane accessibility for dithionite, particularly in bilayers containing ergosterol. NMR experiments with deuterated sphingomyelin (SM) in sterol-containing membranes reveal that natamycin reduces phase separation and increases lipid exchange in bilayers with ergosterol. In ternary lipid mixtures containing monounsaturated phosphatidylcholine, saturated SM, and either ergosterol or cholesterol, natamycin interferes with phase separation into Lo and liquid-disordered (Ld) domains, as shown by NMR spectroscopy. Employing the intrinsic fluorescence of natamycin in ultraviolet-sensitive microscopy, we can visualize the binding of natamycin to giant unilamellar vesicles (GUVs) and find that it has the highest affinity for the Lo phase in GUVs containing ergosterol. Our results suggest that natamycin specifically interacts with the sterol-induced ordered phase, in which it disrupts lipid packing and increases solvent accessibility. This property is particularly pronounced in ergosterol containing membranes, which could underlie the selective antifungal activity of natamycin.

Intestinal Villi-Inspired Mathematically Base-Layer Engineered Microneedles (IMBEMs) for Effective Molecular Exchange during Biomarker Enrichment and Drug Deposition in Diversified Mucosa

The mucosa-interfacing systems based on bioinspired engineering design for sampling/drug delivery have manifested crucial potential for the monitoring of infectious diseases and the treatment of mucosa-related diseases. However, their efficiency and validity are severely restricted by limited contact area for molecular transfer and dissatisfactory capture/detachment capability. Herein, inspired by the multilayer villus structure of the small intestine that enables high nutrient absorption, a trigonometric function-based periodic pattern was fabricated and integrated on the base layer of the microneedle patch, exhibiting a desirable synergistic effect with needle tips for deep sample enrichment and promising molecular transfer, significantly improving the device-mucosa bidirectional interaction. Moreover, mathematical modeling and finite element analysis were adopted to visualize and quantify the microcosmic molecular transmission process, guiding parameter optimization in actual situation. Encouragingly, these intestinal villi-inspired mathematically base-layer engineered microneedles (IMBEMs) have demonstrated distinguished applicability among mucosa tissue with varying surface curvatures, tissue toughness, and local environments, and simultaneously, have gained favorable support from healthy volunteers receiving preliminary test of IMBEMs patches. Overall, validated by numerous in vitro and in vivo tests, the IMBEMs were confirmed to act as a promising candidate to facilitate mucosa-based sampling and topical drug delivery, indicating highly clinical translation potential.

tICA-Metadynamics for Identifying Slow Dynamics in Membrane Permeation

Molecular simulations are commonly used to understand the mechanism of membrane permeation of small molecules, particularly for biomedical and pharmaceutical applications. However, despite significant advances in computing power and algorithms, calculating an accurate permeation free energy profile remains elusive for many drug molecules because it can require identifying the rate-limiting degrees of freedom (i.e., appropriate reaction coordinates). To resolve this issue, researchers have developed machine learning approaches to identify slow system dynamics. In this work, we apply time-lagged independent component analysis (tICA), an unsupervised dimensionality reduction algorithm, to molecular dynamics simulations with well-tempered metadynamics to find the slowest collective degrees of freedom of the permeation process of trimethoprim through a multicomponent membrane. We show that tICA-metadynamics yields translational and orientational collective variables (CVs) that increase convergence efficiency ∼1.5 times. However, crossing the periodic boundary is shown to introduce artefacts in the translational CV that can be corrected by taking absolute values of molecular features. Additionally, we find that the convergence of the tICA CVs is reached with approximately five membrane crossings, and that data reweighting is required to avoid deviations in the translational CV.

Recent advances on molecular dynamics-based techniques to address drug membrane permeability with atomistic detail

Several factors affect the passive membrane permeation of small molecules, including size, charge, pH, or the presence of specific chemical groups. Understanding these features is paramount to identifying or designing drug candidates with optimal ADMET properties and this can be achieved through experimental/knowledge-based methodologies or using computational approaches. Empirical methods often lack detailed information about the underlying molecular mechanism. In contrast, Molecular Dynamics-based approaches are a powerful strategy, providing an atomistic description of this process. This technique is continuously growing, featuring new related methodologies. In this work, the recent advances in this research area will be discussed.

Predictions from First-Principles of Membrane Permeability to Small Molecules: How Useful Are They in Practice?

Predicting from first-principles the rate of passive permeation of small molecules across the biological membrane represents a promising strategy for screening lead compounds upstream in the drug-discovery and development pipeline. One popular avenue for the estimation of permeation rates rests on computer simulations in conjunction with the inhomogeneous solubility-diffusion model, which requires the determination of the free-energy change and position-dependent diffusivity of the substrate along the translocation pathway through the lipid bilayer. In this Perspective, we will clarify the physical meaning of the membrane permeability inferred from such computer simulations, and how theoretical predictions actually relate to what is commonly measured experimentally. We will also examine why these calculations remain both technically challenging and overly computationally expensive, which has hitherto precluded their routine use in nonacademic settings. We finally synopsize possible research directions to meet these challenges, increase the predictive power of physics-based rates of passive permeation, and, by ricochet, improve their practical usefulness.

Path sampling with memory reduction and replica exchange to reach long permeation timescales

Assessing kinetics in biological processes with molecular dynamics simulations remains a computational and conceptual challenge, given the large time and length scales involved. For kinetic transport of biochemical compounds or drug molecules, the permeability through the phospholipid membranes is a key kinetic property, but long timescales are hindering the accurate computation. Technological advances in high-performance computing therefore need to be accompanied by theoretical and methodological developments. In this contribution, the replica exchange transition interface sampling (RETIS) methodology is shown to give perspective toward observing longer permeation pathways. It is first reviewed how RETIS, a path-sampling methodology that gives in principle exact kinetics, can be used to compute membrane permeability. Next, recent and current developments in three RETIS aspects are discussed: several new Monte Carlo moves in the path-sampling algorithm, memory reduction by reducing pathlengths, and exploitation of parallel computing with CPU-imbalanced replicas. Finally, the memory reduction presenting a new replica exchange implementation, coined REPPTIS, is showcased with a permeant needing to pass a membrane with two permeation channels, either representing an entropic or energetic barrier. The REPPTIS results showed clearly that inclusion of some memory and enhancing ergodic sampling via replica exchange moves are both necessary to obtain correct permeability estimates. In an additional example, ibuprofen permeation through a dipalmitoylphosphatidylcholine membrane was modeled. REPPTIS succeeded in estimating the permeability of this amphiphilic drug molecule with metastable states along the permeation pathway. In conclusion, the presented methodological advances allow for deeper insight into membrane biophysics even if the pathways are slow, as RETIS and REPPTIS push the permeability calculations to longer timescales.

Fostering discoveries in the era of exascale computing: How the next generation of supercomputers empowers computational and experimental biophysics alike

Over a century ago, physicists started broadly relying on theoretical models to guide new experiments. Soon thereafter, chemists began doing the same. Now, biological research enters a new era when experiment and theory walk hand in hand. Novel software and specialized hardware became essential to understand experimental data and propose new models. In fact, current petascale computing resources already allow researchers to reach unprecedented levels of simulation throughput to connect in silico and in vitro experiments. The reduction in cost and improved access allowed a large number of research groups to adopt supercomputing resources and techniques. Here, we outline how large-scale computing has evolved to expand decades-old research, spark new research efforts, and continuously connect simulation and observation. For instance, multiple publicly and privately funded groups have dedicated extensive resources to develop artificial intelligence tools for computational biophysics, from accelerating quantum chemistry calculations to proposing protein structure models. Moreover, advances in computer hardware have accelerated data processing from single-molecule experimental observations and simulations of chemical reactions occurring throughout entire cells. The combination of software and hardware has opened the way for exascale computing and the production of the first public exascale supercomputer, Frontier, inaugurated by the Oak Ridge National Laboratory in 2022. Ultimately, the popularization and development of computational techniques and the training of researchers to use them will only accelerate the diversification of tools and learning resources for future generations.

The relationship between structure and rheology in a three-dimensional sheared lamellar mesophase

The evolution of a lamellar mesophase from an initially disordered state under shear is examined using simulations of a mesoscale model based on a concentration field ψ that distinguishes the hydrophilic and hydrophobic components. The Landau-Ginzburg free-energy functional is augmented by a term that is minimised for sinusoidal modulations in the concentration field with wavelength λ = (2π/k), and the dynamical equations are the model H equations. The structure and rheology are determined by the relative magnitudes of the diffusion time for coarsening, (λ²/D) and the inverse of the strain rate ^-1, and the Ericksen number, which is the ratio of the shear stress and the layer stiffness. When the diffusion time is small compared with the inverse of the strain rate, there is a local formation of misaligned layers, which are deformed by the imposed flow. There is near-perfect ordering with isolated defects at low values of the Ericksen number, but the defects result in a significant increase in viscosity due to the high layer stiffness. At high values of the Ericksen number, the concentration field is deformed by the mean shear before layers form via diffusion. Cylindrical structures aligned along the flow direction form after about 8-10 strain units, and these evolve into layers with disorder through diffusion perpendicular to the flow. The layers are not perfectly ordered, even after hundreds of strain units, due to the creation and destruction of defects via shear. The excess viscosity is low because the layer stiffness is small compared with the applied shear at a high Ericksen number. This study provides guidance on how the material parameters and imposed flow can be tailored to achieve the desired rheological behaviour.

CAD Modeling method of the electrospun membrane under multifractal dimension optimization control

Based on an analysis of large sample data, this paper improves the calculation method of the fractal dimension in an electrospun membrane and proposes a method to generate a computer-aided design (CAD) model of an electrospun membrane under the control of fractal dimension. Fifteen electrospun membrane samples of PMMA and PMMA/poly(vinylidene fluoride) (PVDF) materials were prepared under similar concentrations and voltage parameters, and 525 SEM images of the surface morphology with a resolution of 2560 × 1920 were taken as a dataset. The feature parameters, such as fiber diameter and direction, are extracted from the image. Second, based on the minimum value of the power law behavior, the pore perimeter data were preprocessed to calculate the fractal dimensions. A 2D model was reconstructed randomly based on the inverse transformation of the characteristic parameters. The genetic optimization algorithm adjusts the fiber arrangement to realize the control of characteristic parameters, such as the fractal dimension. Based on the 2D model, a long fiber network layer with a thickness consistent with the depth of the SEM shooting is generated in ABAQUS software. Finally, a solid CAD model of the electrospun membrane with realistic thickness was constructed by combining multiple fiber layers. The result shows that the improved fractal dimension exhibits multifractal characteristics and distinct sample differences, which are more similar to the experimental results. The proposed 2D modeling method of the long fiber network can allow the control of various characteristic parameters, including the fractal dimension, and can generate the required model quickly.

Permeation of a Homologous Series of NBD-Labeled Fatty Amines through Lipid Bilayers: A Molecular Dynamics Study

Permeation through biomembranes is ubiquitous for drugs to reach their active sites. Asymmetry of the cell plasma membrane (PM) has been described as having an important role in this process. Here we describe the interaction of a homologous series of 7-nitrobenz-2-oxa-1,3-diazol-4-yl (NBD)-labeled amphiphiles (NBD-Cn, n = 4 to 16) with lipid bilayers of different compositions (1-palmitoyl, 2-oleoyl-sn-glycero-3-phosphocholine (POPC):cholesterol (1:1) and palmitoylated sphingomyelin (SpM):cholesterol (6:4)), including an asymmetric bilayer. Both unrestrained and umbrella sampling (US) simulations (at varying distances to the bilayer center) were carried out. The free energy profile of NBD-Cn at different depths in the membrane was obtained from the US simulations. The behavior of the amphiphiles during the permeation process was described regarding their orientation, chain elongation, and H-bonding to lipid and water molecules. Permeability coefficients were also calculated for the different amphiphiles of the series, using the inhomogeneous solubility-diffusion model (ISDM). Quantitative agreement with values obtained from kinetic modeling of the permeation process could not be obtained. However, for the longer, and more hydrophobic amphiphiles, the variation trend along the homologous series was qualitatively better matched by the ISDM when the equilibrium location of each amphiphile was taken as reference (ΔG = 0), compared to the usual choice of bulk water.

Drude Polarizable Lipid Force Field with Explicit Treatment of Long-Range Dispersion: Parametrization and Validation for Saturated and Monounsaturated Zwitterionic Lipids

Accurate empirical force fields of lipid molecules are a critical component of molecular dynamics simulation studies aimed at investigating properties of monolayers, bilayers, micelles, vesicles, and liposomes, as well as heterogeneous systems, such as protein-membrane complexes, bacterial cell walls, and more. While the majority of lipid force field-based simulations have been performed using pairwise-additive nonpolarizable models, advances have been made in the development of the polarizable force field based on the classical Drude oscillator model. In the present study, we undertake further optimization of the Drude lipid force field, termed Drude2023, including improved treatment of the phosphate and glycerol linker region of PC and PE headgroups, additional optimization of the alkene group in monounsaturated lipids, and inclusion of long-range Lennard-Jones interactions using the particle-mesh Ewald method. Initial optimization targeted quantum mechanical (QM) data on small model compounds representative of the linker region. Subsequent optimization targeted QM data on larger model compounds, experimental data, and dihedral potentials of mean force from the CHARMM36 additive lipid force field using a parameter reweighting protocol. The use of both experimental and QM target data during the reweighting protocol is shown to produce physically reasonable parameters that reproduce a collection of experimental observables. Target data for optimization included surface area/lipid for DPPC, DSPC, DMPC, and DLPC bilayers and nuclear magnetic resonance (NMR) order parameters for DPPC bilayers. Validation data include prediction of membrane thickness, scattering form factors, electrostatic potential profiles, compressibility moduli, surface area per lipid, water permeability, NMR T1 relaxation times, diffusion constants, and monolayer surface tensions for a variety of saturated and unsaturated lipid mono- and bilayers. Overall, the agreement with experimental data is quite good, though the results are less satisfactory for the NMR T1 relaxation times for carbons near the ester groups. Notable improvements compared to the additive C36 force field were obtained for membrane dipole potentials, lipid diffusion coefficients, and water permeability with the exception of monounsaturated lipid bilayers. It is anticipated that the optimized polarizable Drude2023 force field will help generate more accurate molecular simulations of pure bilayers and heterogeneous systems containing membranes, advancing our understanding of the role of electronic polarization in these systems.