Macrocycle Cell Permeability Measured by Solvation Free Energies in Polar and Apolar Environments

Molecular Mechanism of P53 Peptide Permeation through Lipid Membranes from Solid-State NMR Spectroscopy and Molecular Dynamics Simulations

Macrocyclic peptides show promise in targeting high-value therapeutically relevant binding sites due to their high affinity and specificity. However, their clinical application is often hindered by low membrane permeability, which limits their effectiveness against intracellular targets. Previous studies focused on peptide conformations in various solvents, leaving a gap in understanding their interactions with and translocation through lipid bilayers. Addressing this, our study explores the membrane interactions of stapled peptides, a subclass of macrocyclic peptides, using solid-state nuclear magnetic resonance (ssNMR) spectroscopy and molecular dynamics (MD) simulations. We conducted ssNMR measurements on ATSP-7041M, a prototypical stapled peptide, to understand its interaction with lipid membranes, leading to an MD-informed model for peptide membrane permeation. Our findings reveal that ATSP-7041M adopts a stable α-helical structure upon membrane binding, facilitated by a cation-π interaction between its phenylalanine side chain and the lipid headgroup. This interaction makes the membrane-bound state energetically favorable, facilitating membrane affinity and insertion. The bound peptide displayed asymmetric insertion depths, with the C-terminus penetrating deeper (approximately 9 Å) than the N-terminus (approximately 4.3 Å) relative to the lipid headgroups. Contrary to expectations, peptide dynamics was not hindered by membrane binding and exhibited rapid motions similar to cell-penetrating peptides. These dynamic interactions and peptide-lipid affinity appear to be crucial for membrane permeation. MD simulations indicated a thermodynamically stable transmembrane conformation of ATSP-7041M, reducing the energy barrier for translocation. Our study offers an in silico view of ATSP-7041M's translocation from the extracellular to the intracellular region, highlighting the significance of peptide-lipid interactions and dynamics in enabling peptide transit through membranes.

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.

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.

Addressing Challenges of Macrocyclic Conformational Sampling in Polar and Apolar Solvents: Lessons for Chameleonicity

We evaluated a workflow to reliably sample the conformational space of a set of 47 peptidic macrocycles. Starting from SMILES strings, we use accelerated molecular dynamics simulations to overcome high energy barriers, in particular, the cis-trans isomerization of peptide bonds. We find that our approach performs very well in polar solvents like water and dimethyl sulfoxide. Interestingly, the protonation state of a secondary amine in the ring only slightly influences the conformational ensembles of our test systems. For several of the macrocycles, determining the conformational distribution in chloroform turns out to be considerably more challenging. Especially, the choice of partial charges crucially influences the ensembles in chloroform. We address these challenges by modifying initial structures and the choice of partial charges. Our results suggest that special care has to be taken to understand the configurational distribution in apolar solvents, which is a key step toward a reliable prediction of membrane permeation of macrocycles and their chameleonic properties.

Cell-permeable chameleonic peptides: Exploiting conformational dynamics in de novo cyclic peptide design

Membrane-traversing peptides offer opportunities for targeting intracellular proteins and oral delivery. Despite progress in understanding the mechanisms underlying membrane traversal in natural cell-permeable peptides, there are still several challenges to designing membrane-traversing peptides with diverse shapes and sizes. Conformational flexibility appears to be a key determinant of membrane permeability of large macrocycles. We review recent developments in the design and validation of chameleonic cyclic peptides, which can switch between alternative conformations to enable improved permeability through cell membranes, while still maintaining reasonable solubility and exposed polar functional groups for target protein binding. Finally, we discuss the principles, strategies, and practical considerations for rational design, discovery, and validation of permeable chameleonic peptides.

Controlling the Evolution of Selective Vancomycin Resistance through Successful Ophthalmic Eye-Drop Preparation of Vancomycin-Loaded Nanoliposomes Using the Active-Loading Method

Vancomycin is the front-line defense and drug of choice for the most serious and life-threatening methicillin-resistant Staphylococcus aureus (MRSA) infections. However, poor vancomycin therapeutic practice limits its use, and there is a consequent rise of the threat of vancomycin resistance by complete loss of its antibacterial activity. Nanovesicles as a drug-delivery platform, with their featured capabilities of targeted delivery and cell penetration, are a promising strategy to resolve the shortcomings of vancomycin therapy. However, vancomycin's physicochemical properties challenge its effective loading. In this study, we used the ammonium sulfate gradient method to enhance vancomycin loading into liposomes. Depending on the pH difference between the extraliposomal vancomycin-Tris buffer solution (pH 9) and the intraliposomal ammonium sulfate solution (pH 5-6), vancomycin was actively and successfully loaded into liposomes (up to 65% entrapment efficiency), while the liposomal size was maintained at 155 nm. Vancomycin-loaded nanoliposomes effectively enhanced the bactericidal effect of vancomycin; the minimum inhibitory concentration (MIC) value for MRSA decreased 4.6-fold. Furthermore, they effectively inhibited and killed heteroresistant vancomycin-intermediate S.aureous (h-VISA) with an MIC of 0.338 μg mL^-1. Moreover, MRSA could not develop resistance against vancomycin that was loaded into and delivered by liposomes. Vancomycin-loaded nanoliposomes could be a feasible solution for enhancing vancomycin's therapeutic use and controlling the emerging vancomycin resistance.

Computer aided drug design in the development of proteolysis targeting chimeras

Proteolysis targeting chimeras represent a class of drug molecules with a number of attractive properties, most notably a potential to work for targets that, so far, have been in-accessible for conventional small molecule inhibitors. Due to their different mechanism of action, and physico-chemical properties, many of the methods that have been designed and applied for computer aided design of traditional small molecule drugs are not applicable for proteolysis targeting chimeras. Here we review recent developments in this field focusing on three aspects: de-novo linker-design, estimation of absorption for beyond-rule-of-5 compounds, and the generation and ranking of ternary complex structures. In spite of this field still being young, we find that a good number of models and algorithms are available, with the potential to assist the design of such compounds in-silico, and accelerate applied pharmaceutical research.

Grid inhomogeneous solvation theory for cross-solvation in rigid solvents

Grid Inhomogeneous Solvation Theory (GIST) has proven useful to calculate localized thermodynamic properties of water around a solute. Numerous studies have leveraged this information to enhance structure-based binding predictions. We have recently extended GIST toward chloroform as a solvent to allow the prediction of passive membrane permeability. Here, we further generalize the GIST algorithm toward all solvents that can be modeled as rigid molecules. This restriction is inherent to the method and is already present in the inhomogeneous solvation theory. Here, we show that our approach can be applied to various solvent molecules by comparing the results of GIST simulations with thermodynamic integration (TI) calculations and experimental results. Additionally, we analyze and compare a matrix consisting of 100 entries of ten different solvent molecules solvated within each other. We find that the GIST results are highly correlated with TI calculations as well as experiments. For some solvents, we find Pearson correlations of up to 0.99 to the true entropy, while others are affected by the first-order approximation more strongly. The enthalpy-entropy splitting provided by GIST allows us to extend a recently published approach, which estimates higher order entropies by a linear scaling of the first-order entropy, to solvents other than water. Furthermore, we investigate the convergence of GIST in different solvents. We conclude that our extension to GIST reliably calculates localized thermodynamic properties for different solvents and thereby significantly extends the applicability of this widely used method.

Enhanced sampling without borders: on global biasing functions and how to reweight them

Molecular dynamics (MD) simulations are a powerful tool to follow the time evolution of biomolecular motions in atomistic resolution. However, the high computational demand of these simulations limits the timescales of motions that can be observed. To resolve this issue, so called enhanced sampling techniques are developed, which extend conventional MD algorithms to speed up the simulation process. Here, we focus on techniques that apply global biasing functions. We provide a broad overview of established enhanced sampling methods and promising new advances. As the ultimate goal is to retrieve unbiased information from biased ensembles, we also discuss benefits and limitations of common reweighting schemes. In addition to concisely summarizing critical assumptions and implications, we highlight the general application opportunities as well as uncertainties of global enhanced sampling.

Implications of the Conformationally Flexible, Macrocyclic Structure of the First-Generation, Direct-Acting Anti-Viral Paritaprevir on Its Solid Form Complexity and Chameleonic Behavior

Direct-acting antiviral regimens have transformed therapeutic management of hepatitis C across all prevalent genotypes. Most of the chemical matter in these regimens comprises molecules well outside the traditional drug development chemical space and presents significant challenges. Herein, the implications of high conformational flexibility and the presence of a 15-membered macrocyclic ring in paritaprevir are studied through a combination of advanced computational and experimental methods with focus on molecular chameleonicity and crystal form complexity. The ability of the molecule to toggle between high and low 3D polar surface area (PSA) conformations is underpinned by intramolecular hydrogen bonding (IMHB) interactions and intramolecular steric effects. Computational studies consequently show a very significant difference of over 75 Ų in 3D PSA between polar and apolar environments and provide the structural basis for the perplexingly favorable passive permeability of the molecule. Crystal packing and protein binding resulting in strong intermolecular interactions disrupt these intramolecular interactions. Crystalline Form I benefits from strong intermolecular interactions, whereas the weaker intermolecular interactions in Form II are partially compensated by the energetic advantage of an IMHB. Like Form I, no IMHB is observed within the receptor-bound conformation; instead, an intermolecular H-bond contributes to the potency of the molecule. The choice of metastable Form II is derisked through strategies accounting for crystal surface and packing features to manage higher form specific solid-state chemical reactivity and specific processing requirements. Overall, the results show an unambiguous link between structural features and derived properties from crystallization to dissolution, permeation, and docking into the protein pocket.

Effect of Flexibility, Lipophilicity, and the Location of Polar Residues on the Passive Membrane Permeability of a Series of Cyclic Decapeptides

Cyclic peptides have received increasing attention over the recent years as potential therapeutics for "undruggable" targets. One major obstacle is, however, their often relatively poor bioavailability. Here, we investigate the structure-permeability relationship of 24 cyclic decapeptides that share the same backbone N-methylation pattern but differ in their side chains. The peptides cover a large range of values for passive membrane permeability as well as lipophilicity and solubility. To rationalize the observed differences in permeability, we extracted for each peptide the population of the membrane-permeable conformation in water from extensive explicit-solvent molecular dynamics simulations and used this as a metric for conformational rigidity or "prefolding." The insights from the simulations together with lipophilicity measurements highlight the intricate interplay between polarity/lipophilicity and flexibility/rigidity and the possible compensating effects on permeability. The findings allow us to better understand the structure-permeability relationship of cyclic peptides and extract general guiding principles.

Cell Permeability of Isomeric Macrocycles: Predictions and NMR Studies

Conformation-dependent 3D descriptors have been shown to provide better predictions of the physicochemical properties of macrocycles than 2D descriptors. However, the computational identification of relevant conformations for macrocycles is nontrivial. Herein, we report that the Caco-2 cell permeability difference between a pair of diastereomeric macrocycles correlated with their solvent accessible 3D polar surface area and radius of gyration. The descriptors were calculated from the macrocycles’ solution-phase conformational ensembles and independently from ensembles obtained by conformational sampling. Calculation of the two descriptors for three other stereo- and regioisomeric macrocycles also allowed the correct ranking of their cell permeability. Methods for conformational sampling may thus allow ranking of passive permeability for moderately flexible macrocycles, thereby contributing to the prioritization of macrocycles for synthesis in lead optimization.

Benchmark of Generic Shapes for Macrocycles

Macrocycles target proteins that are otherwise considered undruggable because of a lack of hydrophobic cavities and the presence of extended featureless surfaces. Increasing efforts by computational chemists have developed effective software to overcome the restrictions of torsional and conformational freedom that arise as a consequence of macrocyclization. Moloc is an efficient algorithm, with an emphasis on high interactivity, and has been constantly updated since 1986 by drug designers and crystallographers of the Roche biostructural community. In this work, we have benchmarked the shape-guided algorithm using a dataset of 208 macrocycles, carefully selected on the basis of structural complexity. We have quantified the accuracy, diversity, speed, exhaustiveness, and sampling efficiency in an automated fashion and we compared them with four commercial (Prime, MacroModel, molecular operating environment, and molecular dynamics) and four open-access (experimental-torsion distance geometry with additional “basic knowledge” alone and with Merck molecular force field minimization or universal force field minimization, Cambridge Crystallographic Data Centre conformer generator, and conformator) packages. With three-quarters of the database processed below the threshold of high ring accuracy, Moloc was identified as having the highest sampling efficiency and exhaustiveness without producing thousands of conformations, random ring splitting into two half-loops, and possibility to interactively produce globular or flat conformations with diversity similar to Prime, MacroModel, and molecular dynamics. The algorithm and the Python scripts for full automatization of these parameters are freely available for academic use.