Drug-Membrane Permeability across Chemical Space

Transient theory for scanning electrochemical microscopy of biological membrane transport: uncovering membrane-permeant interactions

Scanning electrochemical microscopy (SECM) has emerged as a powerful method to quantitatively investigate the transport of molecules and ions across various biological membranes as represented by living cells. Advantageously, SECM allows for the in situ and non-destructive imaging and measurement of high membrane permeability under simple steady-state conditions, thereby facilitating quantitative data analysis. The SECM method, however, has not provided any information about the interactions of a transported species, i.e., a permeant, with a membrane through its components, e.g., lipids, channels, and carriers. Herein, we propose theoretically that SECM enables the quantitative investigation of membrane-permeant interactions by employing transient conditions. Specifically, we model the membrane-permeant interactions based on a Langmuir-type isotherm to define the strength and kinetics of the interactions as well as the concentration of interaction sites. Finite element simulation predicts that each of the three parameters uniquely affects the chronoamperometric current response of an SECM tip to a permeant. Significantly, this prediction implies that all three parameters are determinable from an experimental chronoamperometric response of the SECM tip. Complimentarily, the steady-state current response of the SECM tip yields the overall membrane permeability based on the combination of the three parameters. Interestingly, our simulation also reveals the optimum strength of membrane-permeant interactions to maximize the transient flux of the permeant from the membrane to the tip.

Predicting permeation of compounds across the outer membrane of P. aeruginosa using molecular descriptors

The ability Gram-negative pathogens have at adapting and protecting themselves against antibiotics has increasingly become a public health threat. Data-driven models identifying molecular properties that correlate with outer membrane (OM) permeation and growth inhibition while avoiding efflux could guide the discovery of novel classes of antibiotics. Here we evaluate 174 molecular descriptors in 1260 antimicrobial compounds and study their correlations with antibacterial activity in Gram-negative Pseudomonas aeruginosa. The descriptors are derived from traditional approaches quantifying the compounds' intrinsic physicochemical properties, together with, bacterium-specific from ensemble docking of compounds targeting specific MexB binding pockets, and all-atom molecular dynamics simulations in different subregions of the OM model. Using these descriptors and the measured inhibitory concentrations, we design a statistical protocol to identify predictors of OM permeation/inhibition. We find consistent rules across most of our data highlighting the role of the interaction between the compounds and the OM. An implementation of the rules uncovered in our study is shown, and it demonstrates the accuracy of our approach in a set of previously unseen compounds. Our analysis sheds new light on the key properties drug candidates need to effectively permeate/inhibit P. aeruginosa, and opens the gate to similar data-driven studies in other Gram-negative pathogens.

Colabind: A Cloud-Based Approach for Prediction of Binding Sites Using Coarse-Grained Simulations with Molecular Probes

Binding site prediction is a crucial step in understanding protein-ligand and protein-protein interactions (PPIs) with broad implications in drug discovery and bioinformatics. This study introduces Colabind, a robust, versatile, and user-friendly cloud-based approach that employs coarse-grained molecular dynamics simulations in the presence of molecular probes, mimicking fragments of drug-like compounds. Our method has demonstrated high effectiveness when validated across a diverse range of biological targets spanning various protein classes, successfully identifying orthosteric binding sites, as well as known druggable allosteric or PPI sites, in both experimentally determined and AI-predicted protein structures, consistently placing them among the top-ranked sites. Furthermore, we suggest that careful inspection of the identified regions with a high affinity for specific probes can provide valuable insights for the development of pharmacophore hypotheses. The approach is available at https://github.com/porekhov/CG_probeMD.

ADMET-PrInt: Evaluation of ADMET Properties: Prediction and Interpretation

Great progress in the development of computational strategies for drug design applications has revolutionized the process of searching for new drugs. Although the focus of in silico strategies is still put on the provision of the desired activity of a compound to the considered target, characterization of a compound in terms of its physicochemical and ADMET properties becomes an indispensable element of computer-aided drug design protocols. In the study, an online application ADMET-PrInt for in silico assessment of selected compound features: cardiotoxicity, solubility, genotoxicity, membrane permeability, and plasma protein binding was prepared. In addition to the prediction of particular property, ADMET-PrInt enables also the identification of compound features influencing this property thanks to the application of two explainability approaches: local interpretabile model-agnostic explanations and counterfactual analysis. It is an important factor for medicinal chemists, as it greatly facilitates the process of optimization of the compound structure in terms of the evaluated properties. The intuitive webpage, available at admet.if-pan.krakow.pl, allows making use of all predictive and interpretability models also by nonexperts and nonprogrammers.

Synthesis of Fe-N doped porous carbon/silicate composites regulated by minerals in coal gasification fine slag for synergistic electrocatalytic treatment of phenolic wastewater

Coal gasification fine slag (CGFS), as a difficult-to-dispose solid waste in the coal chemical industry, consists of minerals and residual carbon. Due to the aggregate structure of minerals blocking pores and encapsulating active substances, the high-value utilization of CGFS still remains a challenge. Based on the intrinsic characteristics of CGFS, this study synthesized Fe-N doped porous carbon/silicate composites (Fe-NC) by alkali activation and pyrolysis for electrocatalytic degradation of phenolic wastewater. Meanwhile, minerals were utilized to regulate the surface chemical and pore structure, turning their disadvantages into advantages, which caused a sharp increase in m-cresol mineralization. The positive effect of minerals on composite properties was investigated by characterization techniques, electrochemical analyses and density functional theory (DFT) calculations. It was found that the mesoporous structure of the mineral-regulated composites was further developed, with more carbon defects and reactive substances on its surface. Most importantly, silicate mediated iron conversion through strong interaction with H2O2, high work function gradient with electroactive iron, and excellent superoxide radical (•O2-) production capacity. It effectively improved the reversibility and kinetics of the entire electrocatalytic reaction. Within the Fe-NC311 electrocatalytic system, the m-cresol removal rate reached 99.55 ± 1.24%, surpassing most reported Fe-N-doped electrocatalysts. In addition, the adsorption and electrooxidation experiment confirmed that the synergistic effect of Fe-N doped porous carbon and silicate simultaneously promoted the capture of pollutants and the transformation of electroactive molecules, and hence effectively shortened the diffusion path of short-lived radicals, which was further supported by molecular dynamics simulation. Therefore, this research provides new insights into the problem of mineral limitations and opens an innovative approach for CGFS recycling and environmental remediation.

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).

Development of nano-delivery systems for loaded bioactive compounds: using molecular dynamics simulations

Over the past decade, a remarkable surge in the development of functional nano-delivery systems loaded with bioactive compounds for healthcare has been witnessed. Notably, the demanding requirements of high solubility, prolonged circulation, high tissue penetration capability, and strong targeting ability of nanocarriers have posed interdisciplinary research challenges to the community. While extensive experimental studies have been conducted to understand the construction of nano-delivery systems and their metabolic behavior in vivo, less is known about these molecular mechanisms and kinetic pathways during their metabolic process in vivo, and lacking effective means for high-throughput screening. Molecular dynamics (MD) simulation techniques provide a reliable tool for investigating the design of nano-delivery carriers encapsulating these functional ingredients, elucidating the synthesis, translocation, and delivery of nanocarriers. This review introduces the basic MD principles, discusses how to apply MD simulation to design nanocarriers, evaluates the ability of nanocarriers to adhere to or cross gastrointestinal mucosa, and regulates plasma proteins in vivo. Moreover, we presented the critical role of MD simulation in developing delivery systems for precise nutrition and prospects for the future. This review aims to provide insights into the implications of MD simulation techniques for designing and optimizing nano-delivery systems in the healthcare food industry.

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.

Fluorescent Nanoassembly of Tetrazole-Based Dyes with Amphoteric Surfactants: Investigation of Cyanide Sensing and Antitubercular Activity

Tetrazole-based easily synthesizable fluorogenic probes have been developed that can form self-assembled nanostructures in the aqueous medium. Though the compounds could achieve detection of cyanide ions in apolar solvents, such as, THF, significant interference was observed from other basic anions, such as F-, AcO-, H2PO4-, etc. On the other hand, a highly specific response was observed for CN- ions in the aqueous medium. However, the sensitivity was so poor that it could hardly be useful for real-life sample analysis. Interestingly, the co-assembly of such probe molecules with hydroxyethyl-anchored amphoteric surfactants could drastically improve the sensitivity toward CN- ions in water without dampening their excellent selectivity. Also, it was observed that the degree of fluorescence response for CN- ions depends on the nature of the polyaromatic scaffolds (naphthyl vs anthracenyl), the nature of the surfactant assembly (micelle vs vesicle), etc. The mechanistic investigation indicates the hydrogen bonding interaction between the tetrazole -NH group and cyanide ions in the aqueous medium, which can effectively change the electronics of the tetrazole unit, resulting in alteration in the extent of charge transfer interaction. Then, the biocompatible composite materials (dye-surfactant assemblies at different ratios) were tested for antituberculosis activity. Fortunately, in a few cases, the compositions were found to be as effective as the commercially available antituberculosis drug, ethambutol.

Beyond Rule-of-five: Permeability Assessment of Semipeptidic Macrocycles

Compounds beyond the rule-of-five are generating interest as they expand the molecular toolbox for modulating targets previously considered "undruggable". Macrocyclic peptides are an efficient class of molecules for modulating protein-protein interactions. However, predicting their permeability is difficult as they differ from small molecules. Although constrained by macrocyclization, they generally retain some conformational flexibility associated with an enhanced ability to cross biological membranes. In this study, we investigated the relationship between the structure of semi-peptidic macrocycles and their membrane permeability through structural modifications. Based on a scaffold of four amino acids and a linker, we synthesized 56 macrocycles incorporating modifications in either stereochemistry, N-methylation, or lipophilicity and assessed their passive permeability using the parallel artificial membrane permeability assay (PAMPA). Our results show that some semi-peptidic macrocycles have adequate passive permeability even with properties outside the Lipinski rule of five. We found that N-methylation in position 2 and the addition of lipophilic groups to the side chain of tyrosine led to an improvement in permeability with a decrease in tPSA and 3D-PSA. This enhancement could be attributed to the shielding effect of the lipophilic group on some regions of the macrocycle, which in turn, facilitates a favorable macrocycle conformation for permeability, suggesting some degree of chameleonic behavior.

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.

Orientation-controlled membrane anchoring of bioorthogonal catalysts on live cells via liposome fusion-based transport

An obstacle to conducting diverse bioorthogonal reactions in living systems is the sensitivity of artificial metal catalysts. It has been reported that artificial metallocatalysts can be assembled in "cleaner" environments in cells for stabilized performance, which is powerful but is limited by the prerequisite of using specific cells. We report here a strategy to establish membrane-anchored catalysts with precise spatial control via liposome fusion-based transport (MAC-LiFT), loading bioorthogonal catalytic complexes onto either or both sides of the membrane leaflets. We show that the inner face of the cytoplasmic membrane serves as a reliable shelter for metal centers, protecting the complexes from deactivation thus substantially lowering the amount of catalyst needed for effective intracellular catalysis. This MAC-LiFT approach makes it possible to establish catalyst-protective systems with exclusively exogenous agents in a wide array of mammalian cells, allowing convenient and wider use of diverse bioorthogonal reactions in live cellular systems.

Sucrose Osmotic Self-Oscillation Drives Membrane Permeability

Molecular permeation through phospholipid membranes is a fundamental biological process for small molecules. Sucrose is one of the most widely used sweeteners and a key factor in the pathogenesis of obesity and diabetes, yet a detailed understanding of its mechanism involved in permeability into phospholipid membranes is still lacking. Here, using giant unimolecular vesicles (GUVs) reconstituting membrane properties, we compared the osmotic behavior of sucrose in GUVs and HepG2 cells to explore the effect of sucrose on membrane stability in the absence of protein enhancers. The results suggested that the particle size and potential of GUVs and the cellular membrane potential changed significantly with increasing the sucrose concentration (p < 0.05). In microscopic images of cells containing GUVs and sucrose, the fluorescence intensity of vesicles was 537 ± 17.69 after 15 min, and the value was significantly higher than that of microscopic images of cells without sucrose addition (p < 0.05). These changes suggested that the permeability of the phospholipid membrane became larger under a sucrose environment. This study provides a theoretical basis for better insight on the role of sucrose in the physiological environment.

Trends in Molecular Properties, Bioavailability, and Permeability across the Bayer Compound Collection

For oral drugs, medicinal chemists aim to design compounds with high oral bioavailability, of which permeability is a key determinant. Taking advantage of >2000 compounds tested in rat bioavailability studies and >20,000 compounds tested in Caco2 assays at Bayer, we have examined the molecular properties governing bioavailability and permeability. In addition to classical parameters such as logD and molecular weight, we also investigated the relationship between calculated pK_a and permeability. We find that neutral compounds retain permeability up to a molecular weight limit of 700, while stronger acids and bases are restricted to weights of 400-500. We also investigate trends for common properties such as hydrogen bond donors and acceptors, polar surface area, aromatic ring count, and rotatable bonds, including compounds which exceed Lipinski's rule of five (Ro5). These property-structure relationships are combined to provide design guidelines for bioavailable drugs in both traditional and "beyond rule of 5" (bRo5) chemical space.

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.

Degrader-Antibody Conjugates: Emerging New Modality

In order to deliver chemotherapeutics more efficiently, small-molecule-drug conjugates (SMDCs) and antibody-drug conjugates (ADCs) have been synthesized and explored. These conjugates not only provide selective delivery but also improve the therapeutic index of toxins. By merging this conjugate concept with target protein degradation (TPD), the degrader-antibody conjugate (DAC) field has emerged, and clinical trials have even begun in recent years. In this Perspective, we provide the concepts, applications, and recent advances in the area of DACs.

Broad chemical transferability in structure-based coarse-graining

Compared to top-down coarse-grained (CG) models, bottom-up approaches are capable of offering higher structural fidelity. This fidelity results from the tight link to a higher-resolution reference, making the CG model chemically specific. Unfortunately, chemical specificity can be at odds with compound-screening strategies, which call for transferable parametrizations. Here we present an approach to reconcile bottom-up, structure-preserving CG models with chemical transferability. We consider the bottom-up CG parametrization of 3,441 C7O2 small-molecule isomers. Our approach combines atomic representations, unsupervised learning, and a large-scale extended-ensemble force-matching parametrization. We first identify a subset of 19 representative molecules, which maximally encode the local environment of all gas-phase conformers. Reference interactions between the 19 representative molecules were obtained from both homogeneous bulk liquids and various binary mixtures. An extended-ensemble parametrization over all 703 state points leads to a CG model that is both structure-based and chemically transferable. Remarkably, the resulting force field is on average more structurally accurate than single-state-point equivalents. Averaging over the extended ensemble acts as a mean-force regularizer, smoothing out both force and structural correlations that are overly specific to a single state point. Our approach aims at transferability through a set of CG bead types that can be used to easily construct new molecules, while retaining the benefits of a structure-based parametrization.

CLiB - a novel cardiolipin-binder isolated via data-driven and in vitro screening

Cardiolipin, the mitochondria marker lipid, is crucially involved in stabilizing the inner mitochondrial membrane and is vital for the activity of mitochondrial proteins and protein complexes. Directly targeting cardiolipin by a chemical-biology approach and thereby altering the cellular concentration of "available" cardiolipin eventually allows to systematically study the dependence of cellular processes on cardiolipin availability. In the present study, physics-based coarse-grained free energy calculations allowed us to identify the physical and chemical properties indicative of cardiolipin selectivity and to apply these to screen a compound database for putative cardiolipin-binders. The membrane binding properties of the 22 most promising molecules identified in the in silico approach were screened in vitro, using model membrane systems finally resulting in the identification of a single molecule, CLiB (CardioLipin-Binder). CLiB clearly affects respiration of cardiolipin-containing intact bacterial cells as well as of isolated mitochondria. Thus, the structure and function of mitochondrial membranes and membrane proteins might be (indirectly) targeted and controlled by CLiB for basic research and, potentially, also for therapeutic purposes.

Genetic validation of Aspergillus fumigatus phosphoglucomutase as a viable therapeutic target in invasive aspergillosis

Aspergillus fumigatus is the causative agent of invasive aspergillosis, an infection with mortality rates of up to 50%. The glucan-rich cell wall of A. fumigatus is a protective structure that is absent from human cells and is a potential target for antifungal treatments. Glucan is synthesized from the donor uridine diphosphate glucose, with the conversion of glucose-6-phosphate to glucose-1-phosphate by the enzyme phosphoglucomutase (PGM) representing a key step in its biosynthesis. Here, we explore the possibility of selectively targeting A. fumigatus PGM (AfPGM) as an antifungal treatment strategy. Using a promoter replacement strategy, we constructed a conditional pgm mutant and revealed that pgm is required for A. fumigatus growth and cell wall integrity. In addition, using a fragment screen, we identified the thiol-reactive compound isothiazolone fragment of PGM as targeting a cysteine residue not conserved in the human ortholog. Furthermore, through scaffold exploration, we synthesized a para-aryl derivative (ISFP10) and demonstrated that it inhibits AfPGM with an IC50 of 2 μM and exhibits 50-fold selectivity over the human enzyme. Taken together, our data provide genetic validation of PGM as a therapeutic target and suggest new avenues for inhibiting AfPGM using covalent inhibitors that could serve as tools for chemical validation.

Data-driven discovery of cardiolipin-selective small molecules by computational active learning

Subtle variations in the lipid composition of mitochondrial membranes can have a profound impact on mitochondrial function. The inner mitochondrial membrane contains the phospholipid cardiolipin, which has been demonstrated to act as a biomarker for a number of diverse pathologies. Small molecule dyes capable of selectively partitioning into cardiolipin membranes enable visualization and quantification of the cardiolipin content. Here we present a data-driven approach that combines a deep learning-enabled active learning workflow with coarse-grained molecular dynamics simulations and alchemical free energy calculations to discover small organic compounds able to selectively permeate cardiolipin-containing membranes. By employing transferable coarse-grained models we efficiently navigate the all-atom design space corresponding to small organic molecules with molecular weight less than ≈500 Da. After direct simulation of only 0.42% of our coarse-grained search space we identify molecules with considerably increased levels of cardiolipin selectivity compared to a widely used cardiolipin probe 10-N-nonyl acridine orange. Our accumulated simulation data enables us to derive interpretable design rules linking coarse-grained structure to cardiolipin selectivity. The findings are corroborated by fluorescence anisotropy measurements of two compounds conforming to our defined design rules. Our findings highlight the potential of coarse-grained representations and multiscale modelling for materials discovery and design.

Membrane thickness, lipid phase and sterol type are determining factors in the permeability of membranes to small solutes

Cell membranes provide a selective semi-permeable barrier to the passive transport of molecules. This property differs greatly between organisms. While the cytoplasmic membrane of bacterial cells is highly permeable for weak acids and glycerol, yeasts can maintain large concentration gradients. Here we show that such differences can arise from the physical state of the plasma membrane. By combining stopped-flow kinetic measurements with molecular dynamics simulations, we performed a systematic analysis of the permeability of a variety of small molecules through synthetic membranes of different lipid composition to obtain detailed molecular insight into the permeation mechanisms. While membrane thickness is an important parameter for the permeability through fluid membranes, the largest differences occur when the membranes transit from the liquid-disordered to liquid-ordered and/or to gel state, which is in agreement with previous work on passive diffusion of water. By comparing our results with in vivo measurements from yeast, we conclude that the yeast membrane exists in a highly ordered and rigid state, which is comparable to synthetic saturated DPPC-sterol membranes.

Membrane permeability of small molecules depends on the composition of the lipid bilayer. Here, authors compare permeability measured on membranes in different physical states and conclude that the yeast membrane exists in a highly ordered phase.

Targeting GPCRs and Their Signaling as a Therapeutic Option in Melanoma

Simple Summary

Sixteen G-protein-coupled receptors (GPCRs) have been involved in melanogenesis or melanomagenesis. Here, we review these GPCRs, their associated signaling, and therapies.

Abstract

G-protein-coupled receptors (GPCRs) serve prominent roles in melanocyte lineage physiology, with an impact at all stages of development, as well as on mature melanocyte functions. GPCR ligands are present in the skin and regulate melanocyte homeostasis, including pigmentation. The role of GPCRs in the regulation of pigmentation and, consequently, protection against external aggression, such as ultraviolet radiation, has long been established. However, evidence of new functions of GPCRs directly in melanomagenesis has been highlighted in recent years. GPCRs are coupled, through their intracellular domains, to heterotrimeric G-proteins, which induce cellular signaling through various pathways. Such signaling modulates numerous essential cellular processes that occur during melanomagenesis, including proliferation and migration. GPCR-associated signaling in melanoma can be activated by the binding of paracrine factors to their receptors or directly by activating mutations. In this review, we present melanoma-associated alterations of GPCRs and their downstream signaling and discuss the various preclinical models used to evaluate new therapeutic approaches against GPCR activity in melanoma. Recent striking advances in our understanding of the structure, function, and regulation of GPCRs will undoubtedly broaden melanoma treatment options in the future.

Atomistic Model of Solute Transport across the Blood-Brain Barrier

The blood-brain barrier remains a major roadblock to the delivery of drugs to the brain. While in vitro and in vivo measurements of permeability are widely used to predict brain penetration, very little is known about the mechanisms of passive transport. Detailed insight into interactions between solutes and cell membranes could provide new insight into drug design and screening. Here, we perform unbiased atomistic MD simulations to visualize translocation of a library of 24 solutes across a lipid bilayer representative of brain microvascular endothelial cells. A temperature bias is used to achieve steady state of all solutes, including those with low permeability. Based on free-energy surface profiles, we show that the solutes can be classified into three groups that describe distinct mechanisms of transport across the bilayer. Simulations down to 310 K for solutes with fast permeability were used to justify the extrapolation of values at 310 K from higher temperatures. Comparison of permeabilities at 310 K to experimental values obtained from in vitro transwell measurements and in situ brain perfusion revealed that permeabilities obtained from simulations vary from close to the experimental values to more than 3 orders of magnitude faster. The magnitude of the difference was dependent on the group defined by free-energy surface profiles. Overall, these results show that MD simulations can provide new insight into the mechanistic details of brain penetration and provide a new approach for drug discovery.

Amide-to-Ester Substitution as a Strategy for Optimizing PROTAC Permeability and Cellular Activity

Criteria for predicting the druglike properties of "beyond Rule of 5" Proteolysis Targeting Chimeras (PROTAC) degraders are underdeveloped. PROTAC components are often combined via amide couplings due to their reliability. Amides, however, can give rise to poor absorption, distribution, metabolism, and excretion (ADME) properties. We hypothesized that a bioisosteric amide-to-ester substitution could lead to improvements in both physicochemical properties and bioactivity. Using model compounds, bearing either amides or esters, we identify parameters for optimal lipophilicity and permeability. We applied these learnings to design a set of novel amide-to-ester-substituted, VHL-based BET degraders with the goal to increase permeability. Our ester PROTACs retained intracellular stability, were overall more potent degraders than their amide counterparts, and showed an earlier onset of the hook effect. These enhancements were driven by greater cell permeability rather than improvements in ternary complex formation. This largely unexplored amide-to-ester substitution provides a simple strategy to enhance PROTAC permeability and bioactivity and may prove beneficial to other beyond Ro5 molecules.

Oil-Membrane Protection of Electrochemical Sensors for Fouling- and pH-Insensitive Detection of Lipophilic Analytes

To take full advantage of the reagent- and label-free sensing capabilities of electrochemical sensors, a frequent and remaining challenge is interference and degradation of the sensors due to uncontrolled pH or salinity in the sample solution or foulants from the sample solution. Here, we present an oil-membrane sensor protection technique that allows for the permeation of hydrophobic (lipophilic) analytes into a sealed sensor compartment containing ideal salinity and pH conditions while simultaneously blocking common hydrophilic interferents (proteins, acids, bases, etc.) In this paper, we validate the oil-membrane sensor protection technique by demonstrating continuous cortisol detection via electrochemical aptamer-based (EAB) sensors. The encapsulated EAB cortisol sensor exhibits a 5 min concentration-on rise time and maintains a measurement signal of at least 7 h even in the extreme condition of an acidic solution of pH 3.

Mechanistic Understanding from Molecular Dynamics in Pharmaceutical Research 2: Lipid Membrane in Drug Design

We review the use of molecular dynamics (MD) simulation as a drug design tool in the context of the role that the lipid membrane can play in drug action, i.e., the interaction between candidate drug molecules and lipid membranes. In the standard "lock and key" paradigm, only the interaction between the drug and a specific active site of a specific protein is considered; the environment in which the drug acts is, from a biophysical perspective, far more complex than this. The possible mechanisms though which a drug can be designed to tinker with physiological processes are significantly broader than merely fitting to a single active site of a single protein. In this paper, we focus on the role of the lipid membrane, arguably the most important element outside the proteins themselves, as a case study. We discuss work that has been carried out, using MD simulation, concerning the transfection of drugs through membranes that act as biological barriers in the path of the drugs, the behavior of drug molecules within membranes, how their collective behavior can affect the structure and properties of the membrane and, finally, the role lipid membranes, to which the vast majority of drug target proteins are associated, can play in mediating the interaction between drug and target protein. This review paper is the second in a two-part series covering MD simulation as a tool in pharmaceutical research; both are designed as pedagogical review papers aimed at both pharmaceutical scientists interested in exploring how the tool of MD simulation can be applied to their research and computational scientists interested in exploring the possibility of a pharmaceutical context for their research.

Data-driven equation for drug-membrane permeability across drugs and membranes

Drug efficacy depends on its capacity to permeate across the cell membrane. We consider the prediction of passive drug-membrane permeability coefficients. Beyond the widely recognized correlation with hydrophobicity, we additionally consider the functional relationship between passive permeation and acidity. To discover easily interpretable equations that explain the data well, we use the recently proposed sure-independence screening and sparsifying operator (SISSO), an artificial-intelligence technique that combines symbolic regression with compressed sensing. Our study is based on a large in silico dataset of 0.4 × 10⁶ small molecules extracted from coarse-grained simulations. We rationalize the equation suggested by SISSO via an analysis of the inhomogeneous solubility-diffusion model in several asymptotic acidity regimes. We further extend our analysis to the dependence on lipid-membrane composition. Lipid-tail unsaturation plays a key role but surprisingly contributes stepwise rather than proportionally. Our results are in line with previously observed changes in permeability, suggesting the distinction between liquid-disordered and liquid-ordered permeation. Together, compressed sensing with analytically derived asymptotes establish and validate an accurate, broadly applicable, and interpretable equation for passive permeability across both drug and lipid-tail chemistry.

Mastering the Gram-negative bacterial barrier - Chemical approaches to increase bacterial bioavailability of antibiotics

To win the battle against resistant, pathogenic bacteria, novel classes of anti-infectives and targets are urgently needed. Bacterial uptake, distribution, metabolic and efflux pathways of antibiotics in Gram-negative bacteria determine what we here refer to as bacterial bioavailability. Understanding these mechanisms from a chemical perspective is essential for anti-infective activity and hence, drug discovery as well as drug delivery. A systematic and critical discussion of in bacterio, in vitro and in silico assays reveals that a sufficiently accurate holistic approach is still missing. We expect new findings based on Gram-negative bacterial bioavailability to guide future anti-infective research.

Perspectives on High-Throughput Ligand/Protein Docking With Martini MD Simulations

Molecular docking is central to rational drug design. Current docking techniques suffer, however, from limitations in protein flexibility and solvation models and by the use of simplified scoring functions. All-atom molecular dynamics simulations, on the other hand, feature a realistic representation of protein flexibility and solvent, but require knowledge of the binding site. Recently we showed that coarse-grained molecular dynamics simulations, based on the most recent version of the Martini force field, can be used to predict protein/ligand binding sites and pathways, without requiring any a priori information, and offer a level of accuracy approaching all-atom simulations. Given the excellent computational efficiency of Martini, this opens the way to high-throughput drug screening based on dynamic docking pipelines. In this opinion article, we sketch the roadmap to achieve this goal.

Antioxidant and anti-sickling activity of glucal-based triazoles compounds - An in vitro and in silico study

The sickle cell disease (SCD) has a genetic cause, characterized by a replacement of glutamic acid to valine in the β-chain of hemoglobin. The disease has no effective treatment so far, and patients suffer a range from acute to chronic complications that include chronic hemolytic anemia, vaso-occlusive ischemia, pain, acute thoracic syndrome, cerebrovascular accident, nephropathy, osteonecrosis and reduced lifetime. The oxidation in certain regions of the hemoglobin favors the reactive oxygen species (ROS) formation, which is the cause of many clinical manifestations. Antioxidants have been studied to reduce the hemoglobin ROS levels, and in this sense, we have searched for new antioxidants glucal-based triazoles compounds with anti-sickling activity. Thirty analogues were synthetized and tested in in vitro antioxidant assays. Two of them were selected based in their effects and concentration-response activity and conducted to in cell assays. Both molecules did not cause any hemolysis and could reduce the red blood cell damage caused by hydrogen peroxide, in a model of oxidative stress induction that mimics the SCD. Moreover, one molecule (termed 11m), besides reducing the hemolysis, was able to prevent the cell damage caused by the hydrogen peroxide. Later on, by in silico pharmacokinetics analysis, we could see that 11m has appropriated proprieties for druggability and the probable mechanism of action is the binding to Peroxiredoxin-5, an antioxidant enzyme that reduces the hydrogen peroxide levels, verified after molecular docking assays. Thus, starting from 30 glucal-based triazoles molecules in a structure-activity relationship, we could select one with antioxidant proprieties that could act on RBC to reduce the oxidative stress, being useful for the treatment of SCD.

Molecular simulations of lipid membrane partitioning and translocation by bacterial quorum sensing modulators

Quorum sensing (QS) is a bacterial communication process mediated by both native and non-native small-molecule quorum sensing modulators (QSMs), many of which have been synthesized to disrupt QS pathways. While structure-activity relationships have been developed to relate QSM structure to the activation or inhibition of QS receptors, less is known about the transport mechanisms that enable QSMs to cross the lipid membrane and access intracellular receptors. In this study, we used atomistic MD simulations and an implicit solvent model, called COSMOmic, to analyze the partitioning and translocation of QSMs across lipid bilayers. We performed umbrella sampling at atomistic resolution to calculate partitioning and translocation free energies for a set of naturally occurring QSMs, then used COSMOmic to screen the water-membrane partition and translocation free energies for 50 native and non-native QSMs that target LasR, one of the LuxR family of quorum-sensing receptors. This screening procedure revealed the influence of systematic changes to head and tail group structures on membrane partitioning and translocation free energies at a significantly reduced computational cost compared to atomistic MD simulations. Comparisons with previously determined QSM activities suggest that QSMs that are least likely to partition into the bilayer are also less active. This work thus demonstrates the ability of the computational protocol to interrogate QSM-bilayer interactions which may help guide the design of new QSMs with engineered membrane interactions.

A machine-learning-assisted study of the permeability of small drug-like molecules across lipid membranes

Study of the permeability of small organic molecules across lipid membranes plays a significant role in designing potential drugs in the field of drug discovery. Approaches to design promising drug molecules have gone through many stages, from experiment-based trail-and-error approaches, to the well-established avenue of the quantitative structure-activity relationship, and currently to the stage guided by machine learning (ML) and artificial intelligence techniques. In this work, we present a study of the permeability of small drug-like molecules across lipid membranes by two types of ML models, namely the least absolute shrinkage and selection operator (LASSO) and deep neural network (DNN) models. Molecular descriptors and fingerprints are used for featurization of organic molecules. Using molecular descriptors, the LASSO model uncovers that the electro-topological, electrostatic, polarizability, and hydrophobicity/hydrophilicity properties are the most important physical properties to determine the membrane permeability of small drug-like molecules. Additionally, with molecular fingerprints, the LASSO model suggests that certain chemical substructures can significantly affect the permeability of organic molecules, which closely connects to the identified main physical properties. Moreover, the DNN model using molecular fingerprints can help develop a more accurate mapping between molecular structures and their membrane permeability than LASSO models. Our results provide deep understanding of drug-membrane interactions and useful guidance for the inverse molecular design of drug-like molecules. Last but not least, while the current focus is on the permeability of drug-like molecules, the methodology of this work is general and can be applied for other complex physical chemistry problems to gain molecular insights.

Computational and Experimental Approaches to Investigate Lipid Nanoparticles as Drug and Gene Delivery Systems

Lipid nanoparticles (LNPs) have been widely applied in drug and gene delivery. More than twenty years ago, DoxilTM was the first LNPs-based drug approved by the US Food and Drug Administration (FDA). Since then, with decades of research and development, more and more LNP-based therapeutics have been used to treat diverse diseases, which often offer the benefits of reduced toxicity and/or enhanced efficacy compared to the active ingredients alone. Here, we provide a review of recent advances in the development of efficient and robust LNPs for drug/gene delivery. We emphasize the importance of rationally combining experimental and computational approaches, especially those providing multiscale structural and functional information of LNPs, to the design of novel and powerful LNP-based delivery systems.

The importance of intramolecular hydrogen bonds on the translocation of the small drug piracetam through a lipid bilayer

The number of hydrogen bond donors and acceptors is a fundamental molecular descriptor to predict the oral bioavailability of small drug candidates. In fact, the most widely used oral bioavailability rules (such as the Lipinsky's rule-of-five and the Veber rules) make use of this molecular descriptor. It is generally assumed that hydrogen bond donors and acceptors impact on passive diffusion across cell membranes, a fundamental event during drug absorption and distribution. Although the relationship between the number of these motifs and the probability of having good oral bioavailability has been studied and described for more than 20 years, little attention has been given to their spatial distribution in the molecule. In this paper, we used molecular dynamics to describe the effect of intramolecular hydrogen bonding on the passive diffusion of a small drug (piracetam) through a lipid membrane. The results indicated that the formation of an intramolecular hydrogen bond decreases the barrier for translocation by ca. 4 kcal mol-1 and increases the permeability of the tested molecule, partially compensating the desolvation penalty arising from the penetration of the drug into the biological membrane core. This effect was apparent in simulations where the formation of this interaction was prevented with the help of modified potentials, and in simulations with a similar compound to piracetam that was not able to form this intramolecular hydrogen bond due to a larger distance between the hydrogen bond donor and acceptor groups. These results were also supported by coarse-grained methods, which are becoming an important resource for sampling a larger chemical space of molecules, with reduced computational effort. Furthermore, entropy and enthalpy derived profiles were also obtained as the compounds translocated across the membrane, suggesting that, even though the process of formation of internal hydrogen bonds is entropically unfavorable, the enthalpic gain is such that the formation of these interactions is beneficial for the passive diffusion across cell membranes.

Reversal of Ovarian Cancer Cell Lines Multidrug Resistance Phenotype by the Association of Apiole with Chemotherapies

Multidrug resistance (MDR) is the main obstacle in anticancer therapy. The use of drug combinations to circumvent tumor resistance is a well-established principle in the clinic. Among the therapeutic targets, glycoprotein-P (P-gp), an energy-dependent transmembrane efflux pump responsible for modulating MDR, is highlighted. Many pharmacological studies report the ability of calcium channel blockers to reverse tumor resistance to chemotherapy drugs. Isolated for the first time from parsley, the phenylpropanoid apiole is described as a potent calcium channel inhibitor. Taking this into account, herein, the ability of apiole to potentiate the action of well-established chemotherapeutics in the clinic, as well as the compound’s relationship with the reversal of the resistance phenomenon by blocking P-gp, is reported. The association of apiole with both chemotherapeutic drugs doxorubicin and vincristine resulted in synergistic effect, in a concentration-dependent manner, as evaluated by the concentration reduction index. Molecular docking analysis demonstrated the affinity between apiole and the active site of P-gp, corroborating the inhibitory effect. Moreover, apiole demonstrated druglikeness, according to ADME analysis. In conclusion, apiole possibly blocks the active P-gp site, with strong binding energy, which, in turn, inhibits doxorubicin and vincristine efflux, increasing the antiproliferative response of these chemotherapeutic agents.

Improving Membrane Permeation in the Beyond Rule-of-Five Space by Using Prodrugs to Mask Hydrogen Bond Donors

A wide range of drug targets can be effectively modulated by peptides and macrocycles. Unfortunately, the size and polarity of these compounds prevents them from crossing the cell membrane to reach target sites in the cell cytosol. As such, these compounds do not conform to standard measures of drug-likeness and exist in beyond the rule-of-five space. In this work, we investigate whether prodrug moieties that mask hydrogen bond donors can be applied in the beyond rule-of-five domain to improve the permeation of macrocyclic compounds. Using a cyclic peptide model, we show that masking hydrogen bond donors in the natural polar amino acid residues (His, Ser, Gln, Asn, Glu, Asp, Lys, and Arg) imparts membrane permeability to the otherwise impermeable parent molecules, even though the addition of the masking group increases the overall compound molecular weight and the number of hydrogen bond acceptors. We demonstrate this strategy in PAMPA and Caco2 membrane permeability assays and show that masking with groups that reduce the number of hydrogen-bond donors at the cost of additional mass and hydrogen bond acceptors, a donor-acceptor swap, is effective.

Protein-ligand binding with the coarse-grained Martini model

The detailed understanding of the binding of small molecules to proteins is the key for the development of novel drugs or to increase the acceptance of substrates by enzymes. Nowadays, computer-aided design of protein–ligand binding is an important tool to accomplish this task. Current approaches typically rely on high-throughput docking essays or computationally expensive atomistic molecular dynamics simulations. Here, we present an approach to use the recently re-parametrized coarse-grained Martini model to perform unbiased millisecond sampling of protein–ligand interactions of small drug-like molecules. Remarkably, we achieve high accuracy without the need of any a priori knowledge of binding pockets or pathways. Our approach is applied to a range of systems from the well-characterized T4 lysozyme over members of the GPCR family and nuclear receptors to a variety of enzymes. The presented results open the way to high-throughput screening of ligand libraries or protein mutations using the coarse-grained Martini model.

Computer-aided design of protein-ligand binding is important for the development of novel drugs. Here authors present an approach to use the recently re-parametrized coarse-grained Martini model to perform unbiased millisecond sampling of protein-ligand binding interactions of small drug-like molecules.

Interaction of Camptothecin with Model Cellular Membranes

Accurate and efficient prediction of drug partitioning in model membranes is of significant interest to the pharmaceutical industry. Herein, we utilize advanced sampling methods, specifically, the adaptive biasing force methodology to calculate the potential of mean force for a model hydrophobic anticancer drug, camptothecin (CPT), across three model interfaces. We consider an octanol bilayer, a thick octanol/water interface, and a model 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC)/water interface. We characterize the enthalpic and entropic contributions of the drug to the potential of mean force. We show that the rotational entropy of the drug is inversely related to the probability of hydrogen bond formation of the drug with the POPC membrane. In addition, in long-time microsecond simulations of a high concentration of CPT above the POPC membrane, we show that strong drug-drug aromatic interactions shift the spatial orientation of the drug with the membrane. Stacks of hydrophobic drugs form, allowing penetration of the drug just under the POPC head groups. These results imply that inhomogeneous membrane models need to take into account the effect of drug aggregation on the membrane environment.

Inserting Small Molecules across Membrane Mixtures: Insight from the Potential of Mean Force

Small solutes have been shown to alter the lateral organization of cell membranes and reconstituted phospholipid bilayers; however, the mechanisms by which these changes happen are still largely unknown. Traditionally, both experiment and simulation studies have been restricted to testing only a few compounds at a time, failing to identify general molecular descriptors or chemical properties that would allow extrapolating beyond the subset of considered solutes. In this work, we probe the competing energetics of inserting a solute in different membrane environments by means of the potential of mean force. We show that these calculations can be used as a computationally efficient proxy to establish whether a solute will stabilize or destabilize domain phase separation. Combined with umbrella-sampling simulations and coarse-grained molecular dynamics simulations, we are able to screen solutes across a wide range of chemistries and polarities. Our results indicate that for the system under consideration, preferential partitioning and therefore effectiveness in altering membrane phase separation are strictly linked to the location of insertion in the bilayer (i.e., midplane or interface). Our approach represents a fast and simple tool for obtaining structural and thermodynamic insight into the partitioning of small molecules between lipid domains and its relation to phase separation, ultimately providing a platform for identifying the key determinants of this process.

Permeation of nanoparticles across the intestinal lipid membrane: dependence on shape and surface chemistry studied through molecular simulations

Nanoparticles are being explored for topical and oral drug delivery applications as they can cross various biological barriers, for example, the intestinal epithelium. The ability of nanoparticles to cross barriers depends on their morphological and surface properties such as size, surface chemistry and shape, among others. The effect of nanoparticle size on their membrane permeability has been well studied both experimentally and theoretically. However, less attention has been given to understand the role of nanoparticle shape in their translocation across biological barrier membranes. Here, we report on the influence of the nanoparticle's shape, surface chemistry and concentration on their permeation across a human intestinal apical cell membrane model. A representative multicomponent lipid bilayer model of the human intestinal apical membrane was built. The free energy of permeation of nanoparticles across the model lipid bilayer was calculated using multiple umbrella sampling simulations. The interaction of these nanoparticles with the model lipid bilayer was captured using extensive microsecond unrestrained molecular dynamics simulations. We observed that: (a) irrespective of the surface chemistry, the efficacy of nanoparticle penetration across the lipid layer was in the order of rod > disc > sphere; (b) irrespective of the shape, apolar and nonpolar nanoparticles were found to locate in the interior of the lipid bilayer, whereas charged and polar nanoparticles were either adsorbed on the lipid headgroups or remained in the water layer; (c) apolar and nonpolar disc shaped nanoparticles had higher efficacy in permeation across the lipid bilayer as compared to disc and sphere shaped nanoparticles; and (d) at a higher concentration of nanoparticles, sphere and disc shaped nanoparticles exhibited more agglomeration as compared to rod shaped nanoparticles. Based on these outcomes, a few nanoparticles were designed which penetrated readily into the lipid layer and these nanoparticles were also able to co-deliver a therapeutic protein inside the lipid layer. The apical model lipid membrane and protocols used in this study can thus be utilized for the in silico design of nanoparticles for the oral delivery of therapeutics.

Molecular dynamics trajectories for 630 coarse-grained drug-membrane permeations

The permeation of small-molecule drugs across a phospholipid membrane bears much interest both in the pharmaceutical sciences and in physical chemistry. Connecting the chemistry of the drug and the lipids to the resulting thermodynamic properties remains of immediate importance. Here we report molecular dynamics (MD) simulation trajectories using the coarse-grained (CG) Martini force field. A wide, representative coverage of chemistry is provided: across solutes-exhaustively enumerating all 105 CG dimers-and across six phospholipids. For each combination, umbrella-sampling simulations provide detailed structural information of the solute at all depths from the bilayer midplane to bulk water, allowing a precise reconstruction of the potential of mean force. Overall, the present database contains trajectories from 15,120 MD simulations. This database may serve the further identification of structure-property relationships between compound chemistry and drug permeability.

Dynamic coupling of a hydration layer to a fluid phospholipid membrane: intermittency and multiple time-scale relaxations

Understanding the coupling of a hydration layer and a lipid membrane is crucial to gaining access to membrane dynamics and understanding its functionality towards various biological processes.