All-atom empirical potential for molecular modeling and dynamics studies of proteins

Molecular Insights Into β-Glucuronidase Inhibition by Alhagi Graecorum Flavonoids: A Computational and Experimental Approach

In this study, we aimed to investigate the inhibitory mechanisms of β-glucuronidase by flavonoids derived from Alhagi graecorum through both experimental and computational approaches. The activity of β-glucuronidase was assessed using an in vitro enzyme inhibition assay, where myricetin and chrysoeriol were identified as potent inhibitors based on their low IC50 values. Kinetic studies were conducted to determine the inhibition type, revealing that both compounds exhibit noncompetitive inhibition of β-glucuronidase-catalyzed hydrolysis of PNPG. Molecular docking was employed to explore the binding affinities of the flavonoids, showing that myricetin formed the highest number of polar interactions with the enzyme. Additionally, molecular dynamics (MD) simulations were performed to evaluate the stability of the enzyme-inhibitor complexes, demonstrating consistent trajectory behavior for both compounds, with significant energy stabilization. Interaction energy analyses highlighted the dominant role of electrostatic forces in myricetin's inhibition mechanism, while Van der Waals forces were more prominent for chrysoeriol. The MM/PBSA method was used to calculate the binding free energies, with myricetin and chrysoeriol exhibiting the lowest values. Potential energy landscape analysis further revealed that β-glucuronidase adopts a more closed conformation when bound to these inhibitors, limiting substrate access. These findings suggest that flavonoids from Alhagi graecorum hold promise for clinical applications, particularly in managing drug-induced enteropathy.

Structure and mechanism of vitamin-K-dependent γ-glutamyl carboxylase

γ-Glutamyl carboxylase (GGCX) is the sole identified enzyme that uses vitamin K (VK) as a cofactor in humans. This protein catalyses the oxidation of VK hydroquinone to convert specific glutamate residues to γ-carboxyglutamate residues in VK-dependent proteins (VDPs), which are involved in various essential biological processes and diseases1-3. However, the working mechanism of GGCX remains unclear. Here we report three cryogenic electron microscopy structures of human GGCX: in the apo state, bound to osteocalcin (a VDP) and bound to VK. The propeptide of the VDP binds to the lumenal domain of GGCX, which stabilizes transmembrane helices 6 and 7 of GGCX to create the VK-binding pocket. After binding of VK, residue Lys218 in GGCX mediates the oxidation of VK hydroxyquinone, which leads to the deprotonation of glutamate residues and the construction of γ-carboxyglutamate residues. Our structural observations and results from binding and cell biological assays and molecular dynamics simulations show that a cholesterol molecule interacts with the transmembrane helices of GGCX to regulate its protein levels in cells. Together, these results establish a link between cholesterol metabolism and VK-dependent pathways.

Stabilization of a protein by a single halogen-based aromatic amplifier

The utility of halogenation in protein design is investigated by a combination of quantitative atomistic simulations and experiment. Application to insulin is of complementary basic and translational interest. In a singly halogenated aromatic ring, regiospecific inductive effects were predicted to modulate multiple surrounding electrostatic (weakly polar) interactions, thereby amplifying changes in thermodynamic stability. In accordance with the simulations, we demonstrated stabilization of insulin by single halogen atoms at the ortho position of an invariant phenylalanine (2-F-PheB24, 2-Cl-PheB24, and 2-Br-PheB24; ΔΔGu = -0.5 to -1.0 kcal/mol) located at the edge of a protein crevice; corresponding meta and para substitutions had negligible effects. Although receptor-binding affinities were generally decreased (in accordance with packing of the native Phe at the hormone-receptor interface), the ortho-analogs retained biological activity in mammalian cells and in a rat model of diabetes mellitus. Further, the ortho-modified analogs exhibited enhanced resistance to fibrillation above room temperature in two distinct assays of physical stability. Regiospecific halo-aromatic stabilization may thus augment the shelf life of pharmaceutical insulin formulations under real-world conditions. This approach, extending principles of medicinal chemistry, promises to apply to a broad range of therapeutic proteins and vaccines whose biophysical stabilization would enhance accessibility in the developing world.

PEG-mCherry interactions beyond classical macromolecular crowding

The dense cellular environment influences bio-macromolecular structure, dynamics, interactions, and function. Despite advancements in understanding protein-crowder interactions, predicting their precise effects on protein structure and function remains challenging. Here, we elucidate the effects of PEG-induced crowding on the fluorescent protein mCherry using molecular dynamics simulations and fluorescence-based experiments. We identify and characterize specific PEG-induced structural and dynamical changes in mCherry. Importantly, we find interactions in which PEG molecules wrap around specific surface-exposed residues in a binding mode previously observed in protein crystal structures. Fluorescence correlation spectroscopy experiments capture PEG-induced changes, including aggregation, suggesting a potential role for the specific PEG-mCherry interactions identified in simulations. Additionally, mCherry fluorescence lifetimes are influenced by PEG and not by the bulkier crowder dextran or by another linear polymer, polyvinyl alcohol, highlighting the importance of crowder-protein soft interactions. This work augments our understanding of macromolecular crowding effects on protein structure and dynamics.

New N-Alkylketonetetrahydroisoquinoline derivatives exhibits antitumor effect by HA-CD44 interaction inhibition in MDA-MB-231 breast cancer

Molecular interactions at the cell surface, in particular between hyaluronic acid (HA) and the cluster of differentiation 44 (CD44) receptor, are crucial in several biological processes and diseases such as cancer. Thus, inhibition of the HA-CD44 interaction has become a promising therapeutic strategy. Etoposide was the only antitumor compound known to inhibit the binding of CD44 to HA, thereby disrupting key functions that drive malignancy. However, our recent research led to the development of N-alkyl and N-aryl THIQ derivatives, which represented a significant advancement in this field. Here, we further explore the structure-activity relationships of a series of newly designed N-alkylcarbonyl THIQ and study the structural parameters that define both the CD44 inhibitory and antiproliferative activities. Compounds 5d and 7d showed the most improvement of the antiproliferative activity compared to the N-alkylketone 1. Cell viability, competitive binding assays and molecular dynamics studies demonstrated effective inhibition of HA-CD44 binding by compounds 5d and 7d. This work not only expands the arsenal of potential therapeutic agents targeting HA-CD44 interactions but also highlights the potential for new treatments that could more effectively disrupt cancer progression.

Comparison between molecular dynamics potentials for simulation of graphene-based nanomaterials for biomedical applications

OBJECTIVE

This article provides a substantial review of recent research and comparison on molecular dynamics potentials to determine which are most suitable for simulating the phenomena in graphene-based nanomaterials (GBNs).

SIGNIFICANCE

GBNs gain significant attention due to their remarkable properties and potential applications, notably in nanomedicine. However, the physical and chemical characteristics toward macromolecules that justify their nanomedical applications are not yet fully understood. The molecular interaction through molecular dynamic simulation offers the benefits for simulating inorganic molecules like GBNs, with necessary adjustments to account for physical and chemical interactions, or thermodynamic conditions.

METHOD

In this review, we explore various molecular dynamics potentials (force fields) used to simulate interactions and phenomena in graphene-based nanomaterials. Additionally, we offer a brief overview of the benefits and drawbacks of each force fields that available for analysis to assess which one is suitable to study the molecular interaction of graphene-based nanomaterials.

RESULT

We identify and compare various molecular dynamics potentials that available for analyzing GBNs, providing insights into their suitability for simulating specific phenomena in graphene-based nanomaterials. The specification of each force fields and its purpose can be used for further application of molecular dynamics simulation on GBNs.

CONCLUSION

GBNs hold significant promise for applications like nanomedicine, but their physical and chemical properties must be thoroughly studied for safe clinical use. Molecular dynamics simulations, using either reactive or non-reactive MD potentials depending on the expected chemical changes, are essential for accurately modeling these properties, requiring careful selection based on the specific application.

Unraveling the molecular basis of substrate specificity and halogen activation in vanadium-dependent haloperoxidases

Vanadium-dependent haloperoxidases (VHPOs) are biotechnologically valuable and operationally versatile biocatalysts. VHPOs share remarkable active-site structural similarities yet display variable reactivity and selectivity. The factors dictating substrate specificity and, thus, a general understanding of VHPO reaction control still need to be discovered. This work's strategic single-point mutation in the cyanobacterial bromoperoxidase AmVHPO facilitates a selectivity switch to allow aryl chlorination. This mutation induces loop formation that interacts with the neighboring protein monomer, creating a tunnel to the active sites. Structural analysis of the substrate-R425S-mutant complex reveals a substrate-binding site at the interface of two adjacent units. There, residues Glu139 and Phe401 interact with arenes, extending the substrate residence time close to the vanadate cofactor and stabilizing intermediates. Our findings validate the long-debated existence of direct substrate binding and provide a detailed VHPO mechanistic understanding. This work will pave the way for a broader application of VHPOs in diverse chemical processes.

Prediction of hydration energies of adsorbates at Pt(111) and liquid water interfaces using machine learning

Aqueous phase heterogeneous catalysis is important to various industrial processes, including biomass conversion, Fischer-Tropsch synthesis, and electrocatalysis. Accurate calculation of solvation thermodynamic properties is essential for modeling the performance of catalysts for these processes. Explicit solvation methods employing multiscale modeling, e.g., involving density functional theory and molecular dynamics have emerged for this purpose. Although accurate, these methods are computationally intensive. This study introduces machine learning (ML) models to predict solvation thermodynamics for adsorbates on a Pt(111) surface, aiming to enhance computational efficiency without compromising accuracy. In particular, ML models are developed using a combination of molecular descriptors and fingerprints and trained on previously published water-adsorbate interaction energies, energies of solvation, and free energies of solvation of adsorbates bound to Pt(111). These models achieve root mean square error values of 0.09 eV for interaction energies, 0.04 eV for energies of solvation, and 0.06 eV for free energies of solvation, demonstrating accuracy within the standard error of multiscale modeling. Feature importance analysis reveals that hydrogen bonding, van der Waals interactions, and solvent density, together with the properties of the adsorbate, are critical factors influencing solvation thermodynamics. These findings suggest that ML models can provide rapid and reliable predictions of solvation properties. This approach not only reduces computational costs but also offers insights into the solvation characteristics of adsorbates at Pt(111)-water interfaces.

Efficient sampling of free energy landscapes with functions in Sobolev spaces

Molecular simulations of biological and physical phenomena generally involve sampling complicated, rough energy landscapes characterized by multiple local minima. In this work, we introduce a new family of methods for advanced sampling that draw inspiration from functional representations used in machine learning and approximation theory. As shown here, such representations are particularly well suited for learning free energies using artificial neural networks. As a system evolves through phase space, the proposed methods gradually build a model for the free energy as a function of one or more collective variables, from both the frequency of visits to distinct states and generalized force estimates corresponding to such states. Implementation of the methods is relatively simple and, more importantly, for the representative examples considered in this work, they provide computational efficiency gains of up to several orders of magnitude over other widely used simulation techniques.

Enantiorecognition in a multi-component environment

We demonstrate that enantiopreference in the binding of S,S over R,R astaxanthin (AXT) to albumin manifests itself only for racemic (but not enantiopure) carotenoids. The observed enantioselectivity is rationalized using chiroptical spectroscopies supported by molecular docking, molecular dynamics and quantum-chemical calculations. These methods offer a plausible explanation for the observed enantiopreference, as they reveal a unique binding mode of the (3S,3'S)-AXT form with the protein in contrast to multiple (random) possibilities for albumin binding with the (3R,3'R)-AXT, and also a higher interaction energy of the latter enantiomer with the protein.

Evolutionary rewiring of the dynamic network underpinning allosteric epistasis in NS1 of the influenza A virus

Viral proteins frequently mutate to evade host innate immune responses, yet the impact of these mutations on the molecular energy landscape remains unclear. Epistasis, the intramolecular communications between mutations, often renders the combined mutational effects unpredictable. Nonstructural protein 1 (NS1) is a major virulence factor of the influenza A virus (IAV) that activates host PI3K by binding to its p85β subunit. Here, we present a deep analysis of the impact of evolutionary mutations in NS1 that emerged between the 1918 pandemic IAV strain and its descendant PR8 strain. Our analysis reveals how the mutations rewired interresidue communications, which underlie long-range allosteric and epistatic networks in NS1. Our findings show that PR8 NS1 binds to p85β with approximately 10-fold greater affinity than 1918 NS1 due to allosteric mutational effects, which are further tuned by epistasis. NMR chemical shift perturbation and methyl-axis order parameter analyses revealed that the mutations induced long-range structural and dynamic changes in PR8 NS1, relative to 1918 NS1, enhancing its affinity to p85β. Complementary molecular dynamics simulations and graph theory-based network analysis for conformational dynamics on the submicrosecond timescales uncover how these mutations rewire the dynamic network, which underlies the allosteric epistasis. Significantly, we find that conformational dynamics of residues with high betweenness centrality play a crucial role in communications between network communities and are highly conserved across influenza A virus evolution. These findings advance our mechanistic understanding of the allosteric and epistatic communications between distant residues and provide insight into their role in the molecular evolution of NS1.

CHARMM-GUI EnzyDocker for Protein-Ligand Docking of Multiple Reactive States along a Reaction Coordinate in Enzymes

Enzymes play crucial roles in all biological systems by catalyzing a myriad of chemical reactions. These reactions range from simple one-step processes to intricate multistep cascades. Predicting mechanistically appropriate binding modes along a reaction pathway for substrate, product, and all reaction intermediates and transition states is a daunting task. To address this challenge, special docking programs like EnzyDock have been developed. Yet, running such docking simulations is complicated due to the nature of multistep enzyme processes. This work presents CHARMM-GUI EnzyDocker, a web-based cyberinfrastructure designed to streamline the preparation and running of EnzyDock docking simulations. The development of EnzyDocker has been achieved through integration of existing CHARMM-GUI modules, such as PDB Reader and Manipulator, Ligand Designer, and QM/MM Interfacer. In addition, new functionalities have been developed to facilitate a one-stop preparation of multistate and multiscale docking systems and enable interactive and intuitive ligand modifications and flexible protein residues selections. A simple setup related to multiligand docking is automatized through intuitive user interfaces. EnzyDocker offers support for standard classical docking and QM/MM docking with CHARMM built-in semiempirical engines. Automated consensus restraints for incorporating experimental knowledge into the docking are facilitated via a maximum common substructure algorithm. To illustrate the robustness of EnzyDocker, we conducted docking simulations of three enzyme systems: dihydrofolate reductase, SARS-CoV-2 Mpro, and the diterpene synthase CotB2. In addition, we have created four tutorial videos about these systems, which can be found at https://www.charmm-gui.org/demo/enzydock. EnzyDocker is expected to be a valuable and accessible web-based tool that simplifies and accelerates the setup process for multistate docking for enzymes.

Computational Study on the Catalytic Mechanism of UstD Catalyzing the Synthesis of γ-Hydroxy-α-Amino Acids

The catalytic mechanism of a pyridoxal 5'-phosphate (PLP)-dependent UstD was herein studied in atomic detail, employing the computational hybrid QM/MM methodology. UstD is a PLP-dependent enzyme that catalyzes the decarboxylative aldol reactions between l-aspartate and aldehyde or ketone derivatives to form γ-hydroxy-α-amino acids. In the reaction catalyzed by UstD, the loss of CO2 renders the C-C bond-forming reaction effectively irreversible, which makes UstD a special case among the enzymes catalyzing the C-C bond-forming reactions. This enzyme is currently seen as the optimal approach for the regioselective synthesis of γ-hydroxy-α-amino acids, which are very difficult to obtain by standard chemical methods. The results obtained herein showed that the catalytic mechanism of UstD might follow two paths to occur in three phases: (1) decarboxylation of substrate l-aspartate, (2) C-C bond formation by addition of aldehyde, and (3) the regeneration of catalytic sites. Although Path A and Path B showed a negligible difference in the energy barrier of the rate-determining step, Path A involves three additional steps in the overall pathway compared with Path B, which makes the reaction proceed more readily through Path B. According to the QM/MM energy profile of Path B, the rate-limiting step of the catalytic process is the decarboxylation of the side chain of l-aspartate, which has a calculated energy barrier of 19.19 kcal/mol. Two crucial residues, H263 and Y257, were identified to interact with the substrate aspartic acid. The knowledge about the transition states, intermediates, key residues, and protein conformational changes along the reaction path will be valuable for engineering UstD to improve the synthesis of γ-hydroxy-α-amino acids that serve as building blocks of various high-value chemicals such as antidiabetics and nutritional supplements.

Hydrogen bonds vs RMSD: Geometric reaction coordinates for protein folding

Reaction coordinates are a useful tool that allows the complex dynamics of a protein in high-dimensional phase space to be projected onto a much simpler model with only a few degrees of freedom, while preserving the essential aspects of that dynamics. In this way, reaction coordinates could provide an intuitive, albeit simplified, understanding of the complex dynamics of proteins. Together with molecular dynamics (MD) simulations, reaction coordinates can also be used to sample the phase space very efficiently and to calculate transition rates and paths between different metastable states. Unfortunately, ideal reaction coordinates for a system capable of these performances are not known a priori, and an efficient calculation in the course of an MD simulation is currently an active field of research. An alternative is to use geometric reaction coordinates, which, although generally unable to provide quantitative accuracy, are useful for simplified mechanistic models of protein dynamics and can thus help gain insights into the fundamental aspects of these dynamics. In this study, five such geometric reaction coordinates, such as the end-to-end distance, the radius of gyration, the solvent accessible surface area, the root-mean-square distance (RMSD), and the mean native hydrogen bond length, are compared. For this purpose, extensive molecular dynamics simulations were carried out for two peptides and a small protein in order to calculate and compare free energy profiles with the aid of the reaction coordinates mentioned. While none of the investigated geometrical reaction coordinates could be demonstrated to be an optimal reaction coordinate, the RMSD and the mean native hydrogen bond length appeared to perform more effectively than the other three reaction coordinates.

PX-MDsim: a rapid and efficient platform for large-scale construction of polyamide membranes via automated molecular dynamics simulations

Polyamide (PA) membranes have attracted extensive attention due to their excellent separation performance in water treatment through reverse osmosis and nanofiltration processes. Although numerous molecular simulation studies attempt to explore their advantages from the microstructure, large-scale construction and simulation of PA membranes remain challenging, mainly due to the complexity and computational intensity of cross-linking reactions of polymers in molecular dynamics simulations. This paper introduces an automated platform called PX-MDsim for modeling and simulation of PA membranes. PX-MDsim is based on the PXLink framework and extends its applicability to any monomer with amino (-NH2) and carboxyl (-COOH) groups. The platform, combined with the PXLink program, realizes the full-process automated cross-linking simulation from input preparation, initial system construction, force field generation, functional group identification, and charge distribution update. Moreover, the software was used to cross-link m-phenylenediamine and 1,4-bis(3-aminopropyl)piperazine with trimesic acid, respectively, and multiple membrane structures with different cross-linking degrees were obtained. Furthermore, the generated membrane microstructure was analyzed using methods such as pore size distribution and order parameter, and the obtained results verified the applicability and accuracy of PX-MDsim in constructing PA membrane structures. The platform is user-friendly and accessible to researchers without prior expertise in molecular dynamics simulation, and it offers new possibilities for polymer research and applications.

Dissecting current rectification through asymmetric nanopores

Rectification, the tendency of bidirectional ionic conductors to favor ion flow in a specific direction, is an intrinsic property of many ion channels and synthetic nanopores. Despite its frequent occurrence in ion channels and its phenomenological explanation using Eyring's rate theory, a quantitative relationship between the rectified current and the underlying ion-specific and voltage-dependent free energy profile has been lacking. In this study, we designed nanopores in which potassium and chloride current rectification can be manipulated by altering the electrostatic pore polarity. Using molecular dynamics-based free energy simulations, we quantified voltage-dependent changes of free energy barriers in six ion-nanopore systems. Our results illustrate how the energy barriers for inward and outward fluxes become unequal in the presence of an electromotive driving force, leading to varying degrees of rectification for cation and anion currents. By establishing a direct link between potential of mean force and current rectification rate, we demonstrate that rectification caused by energy barrier asymmetry depends on the nature of the permeating ion, can be tuned by pore polarity, does not require ion binding sites, conformational flexibility, or specific pore geometry, and, as such, may be widespread among ion channels.

Understanding Protein Adsorption on Carbon Nanotube Inner and Outer Surfaces by Molecular Dynamics Simulations

Biomolecules, such as proteins, can form complexes with carbon nanotubes (CNTs), which have numerous applications in nanobiotechnology. Proteins can be adsorbed onto either the inner walls or outer surfaces of CNTs via van der Waals interactions; however, the differences between these two processes remain poorly understood. In this work, we performed classical all-atom molecular dynamics simulations with explicit solvents to investigate the interaction between a model protein, the Yap65 WW domain, and (22,22) CNTs and larger. The Yap65 WW domain comprises three β-sheet segments and contains three key aromatic residues: TRP17, TYR28, and TRP39. Our findings reveal distinct interaction mechanisms for the inner and outer surfaces of large CNTs. The protein's interaction with the inner surface is governed by the interplay between surface curvature and adsorption orientation. In the confined space of the CNT channel, variations in tube curvature and adsorption orientation give rise to specific binding modes, resulting in varying degrees of protein conformational change. In contrast, on the outer surface of large CNTs, where space is less restricted, the adsorption orientation plays a more dominant role. Specifically, the orientation in which more aromatic residues directly interact with the surface suffer from the greater structural loss, regardless of the tube curvature. Finally, protein-CNT binding free energies were calculated using the Poisson-Boltzmann surface area (MM-PBSA) method and steered molecular dynamics simulations based on Jarzynski equality, demonstrating that protein desorption from CNTs is highly dependent on binding configurations. This study reveals the influence of confined space on protein adsorption and the critical role of CNT curvature in modulating β-sheet stability.

Insight into the Mechanism of d-Glucose Accelerated Exchange in GLUT1 from Molecular Dynamics Simulations

Transmembrane glucose transport, facilitated by glucose transporters (GLUTs), is commonly understood through the simple mobile carrier model (SMCM), which suggests that the central binding site alternates exposure between the inside and outside of the cell, facilitating glucose exchange. An alternative "multisite model" posits that glucose transport is a stochastic diffusion process between ligand-operated gates within the transporter's central channel. This study aims to test these models by conducting atomistic molecular dynamics simulations of multiple glucose molecules docked along the central cleft of GLUT1 at temperatures both above and below the lipid bilayer melting point. Our results show that glucose exchanges occur on a nanosecond time-scale as glucopyranose rings slide past each other within the channel cavities, with minimal protein conformational movement. While bilayer gelation slows net glucose transit, the frequency of positional exchanges remains consistent across both temperatures. This supports the observation that glucose exchange at 0 °C is much faster than net flux, aligning with experimental data that show approximately 100 times the rate of exchange flux relative to net flux at 0 °C compared to 37 °C.

Rhodamine6G and Hœchst33342 narrow BmrA conformational spectrum for a more efficient use of ATP

Multidrug ABC transporters harness the energy of ATP binding and hydrolysis to translocate substrates out of the cell and detoxify them. While this involves a well-accepted alternating access mechanism, molecular details of this interplay are still elusive. Rhodamine6G binding on a catalytic inactive mutant of the homodimeric multidrug ABC transporter BmrA triggers a cooperative binding of ATP on the two identical nucleotide-binding-sites, otherwise michaelian. Here, we investigate this asymmetric behavior via a structural-enzymology approach, solving cryoEM structures of BmrA at defined ATP ratios, highlighting the plasticity of BmrA as it undergoes the transition from inward to outward facing conformations. Analysis of continuous heterogeneity within cryoEM data and structural dynamics, reveals that Rhodamine6G narrows the conformational spectrum explored by the nucleotide-binding domains. We observe the same behavior for the other drug Hœchst33342. Following on these findings, the effect of drug-binding showed an ATPase stimulation and a maximal transport activity of the wild-type protein at the concentration-range where the cooperative transition occurs. Altogether, these findings provide a description of the influence of drug binding on the ATP-binding sites through a change in conformational dynamics.

Insights into Dermal Permeation of Skin Oil Oxidation Products from Enhanced Sampling Molecular Dynamics Simulation

The oxidation of human sebum, a lipid mixture covering our skin, generates a range of volatile and semivolatile carbonyl compounds that contribute largely to indoor air pollution in crowded environments. Kinetic models have been developed to gain a deeper understanding of this complex multiphase chemistry, but they rely partially on rough estimates of kinetic and thermodynamic parameters, especially those describing skin permeation. Here, we employ atomistic molecular dynamics simulations to study the translocation of selected skin oil oxidation products through a model stratum corneum membrane. We find these simulations to be nontrivial, requiring extensive sampling with up to microsecond simulation times, in spite of employing enhanced sampling techniques. We identify the high degree of order and stochastic, long-lived temporal asymmetries in the membrane structure as the leading causes for the slow convergence of the free energy computations. We demonstrate that statistical errors due to insufficient sampling are substantial and propagate to membrane permeabilities. These errors are independent of the enhanced sampling technique employed and very likely independent of the precise membrane model.

A Putative Binding Model of Nitazene Derivatives at the μ-Opioid Receptor

Nitazenes are a class of novel synthetic opioids with exceptionally high potency. Currently, an experimental structure of μOR-opioid receptor (μOR) in complex with a nitazene is lacking. Here we used a suite of computational tools, including consensus docking, conventional molecular dynamics (MD) and metadynamics simulations, to investigate the μOR binding modes of nitro-containing meto-, eto-, proto-, buto-, and isotonitazenes and nitro-less analogs, metodes-, etodes-, and protodesnitazenes. Docking generated three binding modes, whereby the nitro-substituted or unsubstituted benzimidazole group extends into SP1 (subpocket 1 between transmembrane helix or TM 2 and 3), SP2 (subpocket 2 between TM1, TM2, and TM7) or SP3 (subpocket 3 between TM5 and TM6). Simulations suggest that etonitazene and likely also other nitazenes favor the SP2-binding mode. Comparison to the experimental structures of μOR in complex with BU72, fentanyl, and mitragynine pseudoindoxyl (MP) allows us to propose a putative model for μOR-ligand recognition in which ligand can access hydrophobic SP1 or hydrophilic SP2, mediated by the conformational change of Gln1242.60. Interestingly, in addition to water-mediated hydrogen bonds, the nitro group in nitazenes forms a π-hole interaction with the conserved Tyr751.39. Our computational analysis provides new insights into the mechanism of μOR-opioid recognition, paving the way for investigations of the structure-activity relationships of nitazenes.

Self-Assembly of Mycolic Acid in Water: Monolayer or Bilayer

The enduring pathogenicity of Mycobacterium tuberculosis can be attributed to its lipid-rich cell wall, with mycolic acids (MAs) being a significant constituent. Different MAs' fluidity and structural adaptability within the bacterial cell envelope significantly influence their physicochemical properties, operational capabilities, and pathogenic potential. Therefore, an accurate conformational representation of various MAs in aqueous media can provide insights into their potential role within the intricate structure of the bacterial cell wall. We have carried out MD simulations of MAs in an aqueous solution and shed light on various structural properties such as thickness, order parameters, area-per-MAs, conformational changes, and principle component (PC) in the single-component and mixture MAs monolayer. The different conformational populations in the monolayer were estimated using the distance-based analysis between the function groups represented as W, U, and Z conformations that lead to the fold of the MAs chain in the monolayer. Additionally, we have also simulated the mixture of alpha-MA (α-MA or AMA), methoxy-MA (MMA), and keto-MA (KMA) with 50.90% AMA, 36.36% MMA, and 12.72% KMA composition. The thickness of the MAs monolayer was observed to range from 5 to 7 nm with an average 820 kg/m3 density for α-MA, MMA, and KMA quantitative agreement with experimental results. The mero chain (long chain), consisting of a functional group at the proximal and distal positions, tends to fold and exhibit a more disordered phase than the short chain. The keto-MA showed the greatest WUZ total conformations (35.32%) with decreasing trend of eZ > eU > aU > aZ folds in both single component and mixture. Our results are in quantitative agreement with the experimental observations. The sZ folds show the lowest conformational probability in monolayer assembly (0.75% in a single component and 1.1% in a mixture). However, eU and aU folds are most probable for AMA and MMA. One striking observation is the abundance of MA conformers beyond the known WUZ convention because of the wide range distribution of intramolecular distances and change in dihedral angles. From a thermodynamic perspective, all mycolic acid monolayers in this study within the microsecond-long simulation, MA molecules self-assembled, and the self-assembled monolayer was found to be stable. The conformation of MAs corresponding to lower free energy minima in the monolayer gives rise to tighter packing and a highly dense self-assembly. Such a highly packed assembly shows higher resistance for drug permeability. Therefore, we concluded that the monolayer formed by AMA will be more densely packed and may cause more resistance for the drug molecules.

Docking Structures Induced by Substitution Motifs of Softwood Xylan at Various Cellulose Surfaces

To understand xylan-cellulose interactions in softwood, the adsorption behavior of hexameric softwood xylan proxies with various substitutions was analyzed on the three surfaces of a hexagonal cellulose microfibril. The study found that all surfaces could bind xylan motifs, showing equally high affinity for the hydrophilic (110) and hydrophobic (100) surfaces and significantly lower affinity for the hydrophilic (11̅0) surface. Unsubstituted xylose hexamers had the highest affinity and most ordered adsorption structures, while substitutions generally reduced the affinity and regularity. An exception was a motif with two glucuronic acids two residues apart, which displayed high affinity and increased tendency to adopt a 2-fold screw on hydrophilic surfaces. Surface affinity correlated with the tightness of xylan-cellulose associations and the ratio of the xylan-cellulose to xylan-water interaction energies. Novel methods to quantify backbone conformations were proposed. Future work should address differences in simulation models and explore the competition between xylan and glucomannan for cellulose surfaces.

Influence of counterion substitution on the properties of imidazolium-based ionic liquid clusters

Due to their unique physiochemical properties that may be tailored for specific purposes, ionic liquids (ILs) have been investigated for various applications, including chemical separations, catalysis, energy storage, and space propulsion. The different cations and anions comprising ILs may be selected to optimize a range of desired properties, such as thermal stability, ionic conductivity, and volatility, leading to the designation of certain ILs as designer "green" solvents. The effect of counterions on the properties of ILs is of both fundamental scientific interest and technological importance. Herein, we report a systematic experimental and theoretical investigation of the size, charge, stability toward dissociation, and geometric/electronic structure of 1-ethyl-3-methyl imidazolium (EMIM)-based IL clusters containing two different atomic counterions (i.e., bromide [Br-] and iodide [I-]). This work extends our studies of EMIM+ cations with atomic chloride (Cl-) and molecular tetrafluoroborate (BF4-) anions reported previously by Baxter et al. [Chem. Mater. 34, 2612 (2022)] and Zhang et al. [J. Phys. Chem. Lett. 11, 6844 (2020)], respectively. Distributions of anionic IL clusters were generated in the gas phase using electrospray ionization and characterized by high mass resolution mass spectrometry, energy-resolved collision-induced dissociation, and negative ion photoelectron spectroscopy experiments. The experimental results reveal anion-dependent trends in the size distribution, relative abundance, ionic charge state, stability toward dissociation, and electron binding energies of the IL clusters. Complementary global optimization theory provides molecular-level insights into the bonding and electronic structure of a selected subset of clusters, including their low energy structures and electrostatic potential maps, and how these fundamental characteristics are influenced by anion substitution. Collectively, our findings demonstrate how the fundamental properties of ILs, which determine their suitability for many applications, may be tuned by substituting counterions. These observations are critical in the sub-nanometer cluster size regime where phenomena do not scale predictably to the bulk phase, and each atom counts toward determining behavior.

A thermodynamic cycle to predict the competitive inhibition outcomes of an evolving enzyme

Understanding competitive inhibition at the molecular level is essential for unraveling the dynamics of enzyme-inhibitor interactions and predicting the evolutionary outcomes of resistance mutations. In this study, we present a framework linking competitive inhibition to alchemical free energy perturbation (FEP) calculations, focusing on E. coli dihydrofolate reductase (DHFR) and its inhibition by trimethoprim (TMP). Using thermodynamic cycles, we relate experimentally measured binding constants ( K i and K m ) to free energy differences associated with wild-type and mutant forms of DHFR with a mean error of 0.9 kcal/mol, providing insights into the molecular underpinnings of TMP resistance. Our findings highlight the importance of local conformational dynamics in competitive inhibition. Mutations in DHFR affect substrate and inhibitor binding affinities differently, influencing the fitness landscape under selective pressure from TMP. Our FEP simulations reveal that resistance mutations stabilize inhibitor-bound or substrate-bound states through specific structural and/or dynamical effects. The interplay of these effects showcases significant epistasis in certain cases. The ability to separately assess substrate and inhibitor binding provides valuable insights, allowing for a more precise interpretation of mutation effects and epistatic interactions. Furthermore, we identify key challenges in FEP simulations, including convergence issues arising from charge-changing mutations and long-range allosteric effects. By integrating computational and experimental data, we provide an effective approach for predicting the functional impact of resistance mutations and their contributions to evolutionary fitness landscapes. These insights pave the way for constructing robust mutational scanning protocols and designing more effective therapeutic strategies against resistant bacterial strains.

Complementary Peptide Interactions Support the Ultra-Rigidity of Polymers of De Novo Designed Click-Functionalized Bundlemers

Computationally designed 29-residue peptides yield tetra-α-helical bundles with D2 symmetry. The "bundlemers" can be bifunctionally linked via thiol-maleimide cross-links at their N-termini, yielding supramolecular polymers with unusually large, micrometer-scale persistence lengths. To provide a molecularly resolved understanding of these systems, all-atom molecular modeling and simulations of linked bundlemers in explicit solvent are presented. A search over relative orientations of the bundlemers identifies a structure, wherein at the bundlemer-bundlemer interface, interior hydrophobic residues are in contact, and α-helices are aligned with a pseudocontiguous α-helix that spans the interface. Calculation of a potential of mean force confirms that the structure in which the bundlemers are in contact and colinearly aligned is a stable minimum. Analyses of hydrogen bonds and hydrophobic complementarity highlight the complementary interactions at the interface. The molecular insight provided reveals the molecular origins of bundlemer alignment within the supramolecular polymers.

Identification and prioritization of novel therapeutic candidates against glutamate racemase from Klebsiella pneumoniae

BACKGROUND

Klebsiella pneumoniae, a gram-negative bacterium in the Enterobacteriaceae family, is non-motile, encapsulated, and a major cause of nosocomial infections, particularly in intensive care units. The bacterium possesses a thick polysaccharide capsule and fimbriae, which contribute to its virulence, resistance to phagocytosis, and attachment to host cells. The bacterium has developed serious resistance to most antibiotics currently in use.

OBJECTIVE

This study aims to investigate the structural properties of MurI (glutamate racemase) from Klebsiella pneumoniae and to identify potential candidate inhibitors against the protein, which will help in the development of new strategies to combat the infections related to MDR strains of Klebsiella pneumoniae.

METHODS

The 3D structure of the protein was modelled using SWISS-MODEL, which utilizes the homology modelling technique. After refinement, the structure was subjected to virtual high throughput screening on the TACC server using Enamine AC collection. The obtained molecules were then put through various screening parameters to obtain promising lead candidates, and the selected molecules were then subjected to MD simulations. The data obtained from MD simulations was then assessed with the help of different global dynamics analyses. The protein-ligand complexes were also subjected to MM/PBSA-based binding free energy calculation using the g_mmpbsa program.

RESULTS

The screening parameters employed on the molecules obtained via virtual screening from the TACC server revealed that Z1542321346 and Z2356864560 out of four molecules have better potential to act as potential inhibitors for MurI protein. The binding free energy values, which came out to be -27.26±3.06 kcal/mol and -29.53±4.29 kcal/mol for Z1542321346 and Z2356864560 molecules, respectively, favoured these molecules in terms of inhibition potential towards targeted protein.

CONCLUSION

The investigation of MurI via computational approach and the subsequent analysis of potential inhibitors can pave the way for developing new therapeutic strategies to combat the infections and antibiotic resistance of Klebsiella pneumoniae. This study could significantly help the medical fraternity in the treatment of infections caused by this multidrug-resistant pathogen.

Structural insights into light-gating of potassium-selective channelrhodopsin

Structural information on channelrhodopsins' mechanism of light-gated ion conductance is scarce, limiting its engineering as optogenetic tools. Here, we use single-particle cryo-electron microscopy of peptidisc-incorporated protein samples to determine the structures of the slow-cycling mutant C110A of kalium channelrhodopsin 1 from Hyphochytrium catenoides (HcKCR1) in the dark and upon laser flash excitation. Upon photoisomerization of the retinal chromophore, the retinylidene Schiff base NH-bond reorients from the extracellular to the cytoplasmic side. This switch triggers a series of side chain reorientations and merges intramolecular cavities into a transmembrane K+ conduction pathway. Molecular dynamics simulations confirm K+ flux through the illuminated state but not through the resting state. The overall displacement between the closed and the open structure is small, involving mainly side chain rearrangements. Asp105 and Asp116 play a key role in K+ conductance. Structure-guided mutagenesis and patch-clamp analysis reveal the roles of the pathway-forming residues in channel gating and selectivity.

MD simulations for rational design of high-affinity HDAC4 inhibitors - Analysis of non-bonding interaction energies for building new compounds

This study investigates the contributions of non-bonding energy (NBE) to the efficacy of four HDAC4 co-crystallized inhibitors (HA3, 9F4, EBE, and TFG) through 100ns Molecular Dynamics (MD) simulations. These inhibitors contain hydroxamic acid (HA3, 9F4, EBE) or diol (TFG) as zinc-binding groups. In PDBs 2VQJ and 2VQM, the HDAC4 catalytic domain is in the 'open' conformation, while in PDBs 4CBT and 6FYZ, the same is in the 'closed' conformation. We identified HA3 as a weaker inhibitor because of the unfavorable NBE contributions from its carbonyl fragment (FR3) and hydroxamic fragment (FR1). To enhance NBE efficacy, we designed novel HA3 analogs (H01-H16) by introducing diverse fragments (-CF3, 2-hydroxyacetic acid, -NH-CH2-, 5-fluoro-2-phenyl pyrimidine, and chloroquinoline moieties). MD simulations revealed promising analogs (H02, H07, H08, H15) with strong NBEs and stable ligand-zinc retention (2.07-2.33 Å). These analogs exhibited strong relative binding free energies within their catalytic sites, highlighting their potential as novel HDAC4 inhibitors. The current study provides medicinal chemists with insights into non-covalent interactions, identifies key fragments for optimization, and offers a rational design strategy for developing more effective HDAC4 inhibitors.

Structural insights into ACE2 interactions and immune activation of SARS-CoV-2 and its variants: an in-silico study

The initial interaction between COVID-19 and the human body involves the receptor-binding domain (RBD) of the viral spike protein with the angiotensin-converting enzyme 2 (ACE2) receptor. Likewise, the spike protein can engage with immune-related proteins, such as toll-like receptors (TLRs) and pulmonary surfactant proteins A (SP-A) and D (SP-D), thereby triggering immune responses. In this study, we utilize computational methods to investigate the interactions between the spike protein and TLRs (specifically TLR2 and TLR4), as well as (SP-A) and (SP-D). The study is conducted on four variants of concern (VOC) to differentiate and identify common virus behaviours. An assessment of the structural stability of various variants indicates slight changes attributed to mutations, yet overall structural integrity remains preserved. Our findings reveal the spike protein's ability to bind with TLR4 and TLR2, prompting immune activation. In addition, our in-silico results reveal almost similar docking scores and therefore affinity for both ACE2-spike and TLR4-spike complexes. We demonstrate that even minor changes due to mutations in all variants, surfactant A and D proteins can function as inhibitors against the spike in all variants, hindering the ACE2-RBD interaction.Communicated by Ramaswamy H. Sarma.

Different receptor models show differences in ligand binding strength and location: a computational drug screening for the tick-borne encephalitis virus

The tick-borne encephalitis virus (TBE) is a neurotrophic disease that has spread more rapidly throughout Europe and Asia in the past few years. At the same time, no cure or specific therapy is known to battle the illness apart from vaccination. To find a pharmacologically relevant drug, a computer-aided drug screening was initiated. Such a procedure probes a possible binding of a drug to the RNA Polymerase of TBE. The crystal structure of the receptor, however, includes missing and partially modeled regions, which rendered the structure incomplete and of questionable use for a thorough drug screening procedure. The quality of the receptor model was addressed by studying three putative structures created. We show that the choice of receptor models greatly influences the binding affinity of potential drug molecules and that the binding location could also be significantly impacted. We demonstrate that some drug candidates are unsuitable for one model but show decent results for another. Without any prejudice on the three employed receptor models, the study reveals the imperative need to investigate the receptor structure before drug binding is probed whether experimentally or computationally.

The E3 ligase HUWE1 interacts with ubiquitin non-covalently via key residues in the HECT domain

HUWE1, a HECT E3 ligase, is critical for processes like protein degradation and tumor development. Contrary to previous findings which suggested minimal non-covalent interactions between the HUWE1 HECT domain and ubiquitin, we identified a non-covalent interaction between the HUWE1 HECT N-lobe and ubiquitin using NMR spectroscopy, revealing a conserved ubiquitin-binding mode shared across HECT E3 ligases. Molecular dynamics simulations not only confirmed the stability of this interaction but also uncovered conformational changes in key residues, which likely influence binding affinity. Additionally, we highlighted the roles of both conserved and unique residues in ubiquitin binding. These findings advance our understanding of the interactions between the HUWE1 HECT domain and ubiquitin, and highlight potential targets for therapeutic intervention in the ubiquitin-proteasome pathway.

Molecular dynamics simulations suggest novel allosteric modes in the Hsp70 chaperone protein

The Hsp70 chaperone protein system is an essential component of the protein folding and homeostasis machinery in E.Coli. Hsp70 is a three domain, 70 kDa protein which functions as an allosteric system cycling between an ADP-bound state where the three domains are loosely coupled via a flexible interdomain linker and an ATP-bound state where they are tightly coupled into a single entity. The structure-function model of this protein proposes an allosteric connection between the 45 kDa Nucleotide Binding Domain (NBD) and the 25 kDa Substrate Binding Domain (SBD) and Lid Domain which operates through the inter NBD-SBD linker. X-Ray crystallography and NMR spectroscopy have provided structures of the end states of the functional cycle of this protein, bound to ADP and ATP. We have used MD simulations to study the transitions between these end states and allosteric communication in this system. Our results largely validate the experimentally derived allosteric model of function, but shed additional light on the flow of allosteric information in the SBD + Lid. Specifically, we find that the Lid domain has a double-hinged structure with the potential for greater conformational flexibility than was hitherto expected.Communicated by Ramaswamy H. Sarma.

Voltage Sensor Conformations Induced by LQTS-associated Mutations in hERG Potassium Channels

Voltage sensors are essential for electromechanical coupling in hERG K + channels, critical to cardiac rhythm. These sensors detect changes in membrane voltage and move in response to the transmembrane electric field. Mutations in voltage-sensing arginines of hERG, associated with Long QT syndrome, alter channel gating, though mechanisms in these mutants remain unclear. Using fluorescence lifetime imaging microscopy (FLIM), transition metal FRET (tmFRET), dual stop-codon mediated noncanonical amino acid incorporation, and molecular dynamics (MD) simulations, we identified distinct intermediate voltage-sensor conformations caused by these mutations. Phasor plot analysis of the FLIM-tmFRET donor revealed multiple FRET states in mutant hERG channels, in contrast to the single high-FRET state observed in unmutated controls. These intermediate FRET states correspond to specific mutation sites and align with distinct intermediate voltage-sensor conformations identified in MD simulations. This study provides novel insights into cardiac channelopathies, highlighting structural underpinnings underlying voltage sensing in cardiac arrhythmias.

Molecular Understanding of Activity Changes of Alcohol Dehydrogenase in Deep Eutectic Solvents

Deep eutectic solvents (DESs) have emerged as promising solvents for biocatalysis. While their impact on enzyme solvation and stabilization has been studied for several enzyme classes, their role in substrate binding is yet to be investigated. Herein, molecular dynamics (MD) simulations of horse-liver alcohol dehydrogenase (HLADH) are performed in choline chloride-ethylene glycol (ChCl-EG) and choline chloride-glycerol (ChCl-Gly) at varying water concentrations. In the DES solutions, the active site was significantly constricted, and its flexibility reduced when compared to the aqueous medium. Importantly, the cavity size follows a similar trend as the catalytic activity of HLADH and as such explains previously observed activity changes. To understand the impact on the binding of the substrate (cyclohexanone), an umbrella sampling (US) setup was established to calculate the free energy changes along the substrate binding tunnel of HLADH. The US combined with replica exchange and NADH in its cofactor pocket provided the best sampling of the entire active site, explaining why the cyclohexanone binding on HLADH is reduced with increasing DES content. As different components in these multicomponent mixtures influence the substrate binding, we additionally applied the US setup to study the ability of the DES components to be present inside the substrate tunnel. The presented approach may become useful to understand enzyme behaviors in DESs and to enable the design of more enzyme-compatible and tunable solvents.

Fluoxetine Alters the Biophysics of DPPC and DPPG Bilayers through Phase-Dependent and Electrostatic Interactions

Lipid membranes can control the permeability of a pharmaceutical drug, whereas the drug can induce changes in the structural and biophysical properties of the membranes. Understanding this interplay of drug-lipid membrane interactions can be of great importance in drug design. Here, we present a molecular dynamics study to provide insights into the interactions between the antidepressant fluoxetine and 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) or 1,2-dipalmitoyl-sn-glycero-3-phosphoglycerol (DPPG) bilayers. It was found that, due to the electrostatic interaction, the headgroup of the zwitterionic DPPC lipid is more stable than that of the negatively charged DPPG lipid, allowing the gel phase to persist even at the elevated temperature. At 25 °C, fluoxetine cannot penetrate into the gel-phase DPPC bilayer, while the electrostatic interaction between positively charged fluoxetine and negatively charged DPPG bilayer retains the drug within the lipid headgroup domain. When the temperature is increased to 45 °C, both neutral and charged forms of fluoxetine can partition into the DPPC and DPPG bilayers spontaneously. Analysis of the biophysical and structural changes in both DPPC and DPPG bilayers in the presence of fluoxetine revealed a phase-dependent effect. The binding of fluoxetine to the lipid bilayers limits the movement and orientation of the drug. These findings shed light on the interactions between a commonly prescribed antidepressant and lipid membranes, and such information can be beneficial to the development of potential therapeutic agents.

Ion pairing in aqueous tetramethylammonium-acetate solutions by neutron scattering and molecular dynamics simulations

Tetramethylammonium (TMA) is a ubiquitous cationic motif in biochemistry, found in the charged choline headgroup of membrane phospholipids and in tri-methylated lysine residues, which modulates histone-DNA interactions and impacts epigenetic mechanisms. TMA interactions with anionic species, particularly carboxylate groups of amino acid residues and extracellular sugars, are of substantial biological relevance, as these interactions mediate a wide range of cellular processes. This study investigates the molecular interactions between TMA and acetate, representing carboxylate-containing groups, using neutron scattering experiments complemented by force fields and ab initio molecular dynamics (MD) simulations. Neutron diffraction with isotopic substitution reveals specific ion pairing signatures between TMA and acetate, with simulations providing a detailed interpretation of the ion pairing structures. Force fields, notably CHARMM36 with the electronic continuum correction (ECC) (by a factor of 0.85) and AMBER99SB, capture essential pairing characteristics, but only revPBE-based ab initio MD simulations accurately model specific experimental features such as the low Q peak intensity in reciprocal space. Our study delivers a refined molecular model of TMA-carboxylate interactions, guiding the selection of force fields for complex biological systems where such interactions are of significant importance.

Multi-pronged molecular insights into flavonoid-mediated inhibition of squalene epoxidase: a pathway to novel therapeutics

Squalene epoxidase (SQLE) is a crucial enzyme in the sterol biosynthesis pathway and a promising target for therapeutic intervention in hypercholesterolemia and fungal infections. This study evaluates the inhibitory potential of six flavonoids namely silibinin, baicalin, naringenin, chrysin, apigenin-7-O-glucoside, and isorhamnetin against SQLE using an integrative approach combining in silico and experimental methods. Molecular docking revealed that apigenin-7-O-glucoside, silibinin, and baicalin displayed the highest binding affinities (-10.7, -10.2, and -10.0 kcal mol-1, respectively) and robust interactions with the SQLE binding site. These findings were corroborated by 200 ns molecular dynamics (MD) simulations, which demonstrated stable binding trajectories, minimal structural fluctuations, a thermodynamically favored potential energy landscape (PEL) and favorable MM/PBSA binding free energies for three flavonoids. Experimental validation via in vitro inhibition assays confirmed the computational predictions, with apigenin-7-O-glucoside emerging as the most potent inhibitor (IC50 = 1.74 ± 0.05 μM), followed by silibinin (IC50 = 1.88 ± 0.28 μM) and baicalin (IC50 = 2.50 ± 0.46 μM). Enzyme kinetics studies revealed distinct mechanisms of action: apigenin-7-O-glucoside exhibited competitive inhibition, while silibinin and baicalin showed mixed inhibition. Furthermore, in silico ADMET analysis indicated favorable pharmacokinetic and pharmacodynamic profiles for these flavonoids, with silibinin demonstrating particularly high bioavailability and lipophilicity. This study highlights apigenin-7-O-glucoside, silibinin, and baicalin as potent SQLE inhibitors with promising therapeutic potential. The congruence between in silico predictions and experimental results underscores the reliability of computational approaches in drug discovery, paving the way for future preclinical development of these compounds as novel SQLE-targeted therapeutics.

Quantitative Prediction of Protein-Polyelectrolyte Binding Thermodynamics: Adsorption of Heparin-Analog Polysulfates to the SARS-CoV-2 Spike Protein RBD

Interactions of polyelectrolytes (PEs) with proteins play a crucial role in numerous biological processes, such as the internalization of virus particles into host cells. Although docking, machine learning methods, and molecular dynamics (MD) simulations are utilized to estimate binding poses and binding free energies of small-molecule drugs to proteins, quantitative prediction of the binding thermodynamics of PE-based drugs presents a significant obstacle in computer-aided drug design. This is due to the sluggish dynamics of PEs caused by their size and strong charge-charge correlations. In this paper, we introduce advanced sampling methods based on a force-spectroscopy setup and theoretical modeling to overcome this barrier. We exemplify our method with explicit solvent all-atom MD simulations of the interactions between anionic PEs that show antiviral properties, namely heparin and linear polyglycerol sulfate (LPGS), and the SARS-CoV-2 spike protein receptor binding domain (RBD). Our prediction for the binding free-energy of LPGS to the wild-type RBD matches experimentally measured dissociation constants within thermal energy, k B T, and correctly reproduces the experimental PE-length dependence. We find that LPGS binds to the Delta-variant RBD with an additional free-energy gain of 2.4 k B T, compared to the wild-type RBD, due to the additional presence of two mutated cationic residues contributing to the electrostatic energy gain. We show that the LPGS-RBD binding is solvent dominated and enthalpy driven, though with a large entropy-enthalpy compensation. Our method is applicable to general polymer adsorption phenomena and predicts precise binding free energies and reconfigurational friction as needed for drug and drug-delivery design.

Influences of pulsed electric field parameters on cell electroporation and electrofusion events: Comprehensive understanding by experiments and molecular dynamics simulations

Electroporation and electrofusion are efficient methods, which have been widely used in different areas of biotechnology and medicine. Pulse strength and width, as an external condition, play an important role in the process of these methods. However, comparatively little work has been done to explore the effects of pulsed electric field parameters on electroporation and electrofusion. Herein, influences of pulse strength and width on the electroporation and electrofusion of phospholipid bilayers were systematically investigated by using experiments combined with molecular dynamics simulations. Experimental results and machine learning-based regression analysis showed that the number of pores is mainly determined by pulse strength, while the sizes of pores were enlarged by increasing the pulse widths. In addition, the formation of large-size pores is the most crucial factor that affects the fusion rate of myeloma cells. The same trend has taken place on coarse-grained and all-atom MD simulations. The result suggested that electroporation events occur only in an electric field exceeding the strength of threshold, and the unbalanced degree of electric potential between two membranes leads to pores formation during the process of electroporation. Generally, this work provides a comprehensive understanding of how pulse strength and width govern the poration event of bilayer lipid membranes, as well as guidance on the experimental design of electrofusion.

Identification of promising SARS-CoV-2 main protease inhibitor through molecular docking, dynamics simulation, and ADMET analysis

The COVID-19 pandemic caused by SARS-CoV-2 continues to pose a major challenge to global health. Targeting the main protease of the virus (Mpro), which is essential for viral replication and transcription, offers a promising approach for therapeutic intervention. In this study, advanced computational techniques such as molecular docking and molecular dynamics simulations were used to screen a series of antiviral compounds for their potential inhibitory effect on the SARS-CoV-2 Mpro. A comprehensive analysis of compounds from the ChemDiv and PubChem databases was performed. The physicochemical properties, pharmacokinetics, and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profiles were evaluated to determine drug similarity and safety. Compound 4896 - 4038 proved to be the most promising candidate. It exhibited a favorable balance between molecular weight (491.06) and lipophilicity (logP 3.957), high intestinal absorption (92.119%), and broad tissue distribution (VDss of 0.529), indicating good oral bioavailability and therapeutic potential. Molecular docking studies showed that 4896 - 4038 has a strong binding affinity to the active site of Mpro and forms key interactions, such as hydrogen bonds, carbon-hydrogen bonds, pi-sulfur, and multiple van der Waals and pi-pi stacked bonds. The binding energy was comparable to that of the reference drug X77, indicating potential efficacy. Molecular dynamics simulations over 300 ns confirmed the stability of the Mpro/4896 - 4038 complex of protein-ligand. Free energy landscape mapping and MM/PBSA calculations further substantiated the favorable binding and stability of the complex. Importantly, 4896 - 4038 exhibited a comparatively favorable safety profile. In summary, compound 4896 - 4038 shows significant potential as a potent SARS-CoV-2 Mpro inhibitor, combining potent inhibitory activity with favorable pharmacokinetic and safety profiles. These results support the further development of 4896 - 4038 as a promising therapeutic agent in the fight against COVID-19 that warrants experimental validation and clinical investigation.

The evolution of the Amber additive protein force field: History, current status, and future

Molecular dynamics simulations are pivotal in elucidating the intricate properties of biological molecules. Nonetheless, the reliability of their outcomes hinges on the precision of the molecular force field utilized. In this perspective, we present a comprehensive review of the developmental trajectory of the Amber additive protein force field, delving into researchers' persistent quest for higher precision force fields and the prevailing challenges. We detail the parameterization process of the Amber protein force fields, emphasizing the specific improvements and retained features in each version compared to their predecessors. Furthermore, we discuss the challenges that current force fields encounter in balancing the interactions of protein-protein, protein-water, and water-water in molecular dynamics simulations, as well as potential solutions to overcome these issues.

Motif-driven dynamics and intermediates during unfolding of multi-domain BphC enzyme

Understanding the folding mechanisms of multi-domain proteins is crucial for gaining insights into protein folding dynamics. The BphC enzyme, a key player in the degradation of polychlorinated biphenyls consists of eight identical subunits, each containing two domains, with each domain comprising two "βαβββ" motifs. In this study, we employed high-temperature molecular dynamics simulations to systematically analyze the unfolding dynamics of a BphC subunit. Our results reveal that the unfolding process of BphC is a complex, multi-intermediate, and multi-phased event. Notably, we identified a thermodynamically stable partially unfolded intermediate. The unfolding sequences, pathways, and rates of the motifs differ significantly. Motif D unfolds first and most rapidly, while Motif C initiates unfolding before Motifs A and B but completes it slightly later. The unfolding behavior of the motifs strongly influences the domain unfolding, leading to the early initiation of Domain 2 unfolding compared to Domain 1, although at a slower rate. The motifs and domains exhibit both independence and cooperativity during the unfolding process, which we interpret through proposed cascading effects. We hypothesize that the folding mechanism of BphC begins with local folding, which propagates through cooperative interactions across structural hierarchies to achieve the folded state. These findings provide new insights into the folding and unfolding mechanisms of multi-domain proteins.

Lipidic folding pathway of α-Synuclein via a toxic oligomer

Aggregation intermediates play a pivotal role in the assembly of amyloid fibrils, which are central to the pathogenesis of neurodegenerative diseases. The structures of filamentous intermediates and mature fibrils are now efficiently determined by single-particle cryo-electron microscopy. By contrast, smaller pre-fibrillar α-Synuclein (αS) oligomers, crucial for initiating amyloidogenesis, remain largely uncharacterized. We report an atomic-resolution structural characterization of a toxic pre-fibrillar aggregation intermediate (I1) on pathway to the formation of lipidic fibrils, which incorporate lipid molecules on protofilament surfaces during fibril growth on membranes. Super-resolution microscopy reveals a tetrameric state, providing insights into the early oligomeric assembly. Time resolved nuclear magnetic resonance (NMR) measurements uncover a structural reorganization essential for the transition of I1 to mature lipidic L2 fibrils. The reorganization involves the transformation of anti-parallel β-strands during the pre-fibrillar I1 state into a β-arc characteristic of amyloid fibrils. This structural reconfiguration occurs in a conserved structural kernel shared by a vast number of αS-fibril polymorphs including extracted fibrils from Parkinson's and Lewy Body Dementia patients. Consistent with reports of anti-parallel β-strands being a defining feature of toxic αS pre-fibrillar intermediates, I1 impacts viability of neuroblasts and disrupts cell membranes, resulting in an increased calcium influx. Our results integrate the occurrence of anti-parallel β-strands as salient features of toxic oligomers with their significant role in the amyloid fibril assembly pathway. These structural insights have implications for the development of therapies and biomarkers.

Gypsum heterogenous nucleation pathways regulated by surface functional groups and hydrophobicity

Gypsum (CaSO4·2H2O) plays a critical role in numerous natural and industrial processes. Nevertheless, the underlying mechanisms governing the formation of gypsum crystals on surfaces with diverse chemical properties remain poorly understood due to a lack of sufficient temporal-spatial resolution. Herein, we use in situ microscopy to investigate the real-time gypsum nucleation on self-assembled monolayers (SAMs) terminated with -CH3, -hybrid (a combination of NH2 and COOH), -COOH, -SO3, -NH3, and -OH functional groups. We report that the rate of gypsum formation is regulated by the surface functional groups and hydrophobicity, in the order of -CH3 > -hybrid > -COOH > -SO3 ≈ - NH3 > - OH. Results based on classical nucleation theory and molecular dynamics simulations reveal that nucleation pathways for hydrophilic surfaces involve surface-induced nucleation, with ion adsorption sites (i.e., functional groups) serving as anchors to facilitate the growth of vertically oriented clusters. Conversely, hydrophobic surfaces involve bulk nucleation with ions near the surface that coalesce into larger horizontal clusters. These findings provide new insights into the spatial and temporal characteristics of gypsum formation on various surfaces and highlight the significance of surface functional groups and hydrophobicity in governing gypsum formation mechanisms, while also acknowledging the possibility of alternative nucleation pathways due to the limitations of experimental techniques.

Phospholipid Transport Across the Bacterial Periplasm Through the Envelope-spanning Bridge YhdP

The outer membrane of Gram-negative bacteria provides a formidable barrier, essential for both pathogenesis and antimicrobial resistance. Biogenesis of this complex structure necessitates the transport of phospholipids across the cell envelope. Recently, YhdP was implicated as a major protagonist in the trafficking of inner membrane phospholipids to the outer membrane; however the molecular mechanism of YhdP mediated transport remains elusive. Here, utilising AlphaFold, we observe YhdP to form an elongated assembly of 60 β-strands that curve to form a continuous hydrophobic groove. This architecture is consistent with our negative stain electron microscopy data which reveals YhdP to be approximately 250 Å in length and thus sufficient to span the bacterial cell envelope. Furthermore, molecular dynamics simulations and bacterial growth assays indicate essential helical regions at the N- and C-termini of YhdP, that may embed into the inner and outer membranes respectively, reinforcing its envelope spanning nature. Our in vivo crosslinking data reveal phosphate-containing substrates captured along the length of the YhdP groove, providing direct evidence that YhdP interacts with a phosphate-containing substrate, which we propose to be phospholipids. This finding is congruent with our molecular dynamics simulations which demonstrate the propensity for inner membrane lipids to spontaneously enter the groove of YhdP. Collectively, our results support a model in which YhdP bridges the cell envelope, providing a hydrophobic environment for the transport of phospholipids to the outer membrane.

From Antipsychotic to Neuroprotective: Computational Repurposing of Fluspirilene as a Potential PDE5 Inhibitor for Alzheimer's Disease

Phosphodiesterase 5 (PDE5) inhibitors have shown great potential in treating Alzheimer's disease by improving memory and cognitive function. In this study, we evaluated fluspirilene, a drug commonly used to treat schizophrenia, as a potential PDE5 inhibitor using computational methods. Molecular docking revealed that fluspirilene binds strongly to PDE5, supported by hydrophobic and aromatic interactions. Molecular dynamics simulations confirmed that the fluspirilene-PDE5 complex is stable and maintains its structural integrity over time. Binding energy calculations further highlighted favorable interactions, indicating that the drug forms a strong and stable bond with PDE5. Additional analyses, including studies of protein dynamics and energy landscape mapping, revealed how the drug interacts dynamically with PDE5, adapting to different conformations and maintaining stability. These findings suggest that fluspirilene may modulate PDE5 activity, potentially offering therapeutic benefits for Alzheimer's disease. This study provides strong evidence for repurposing fluspirilene as a treatment for Alzheimer's and lays the foundation for further experimental and clinical investigations.

Validation of a Coarse-Grained Martini 3 Model for Molecular Oxygen

Molecular oxygen (O2) is essential for life, and continuous effort has been made to understand its pathways in cellular respiration with all-atom (AA) molecular dynamics (MD) simulations of, e.g., membrane permeation or binding to proteins. To reach larger length scales with models, such as curved membranes in mitochondria or caveolae, coarse-grained (CG) simulations could be used at much lower computational cost than AA simulations. Yet a CG model for O2 is lacking. In this work, a CG model for O2 is therefore carefully selected from the Martini 3 force field based on criteria including size, zero charge, nonpolarity, solubility in nonpolar organic solvents, and partitioning in a phospholipid membrane. This chosen CG model for O2 (TC3 bead) is then further evaluated through the calculation of its diffusion constant in water and hexadecane, its permeability rate across pure phospholipid- and cholesterol-containing membranes, and its binding to the T4 lysozyme L99A protein. Our CG model shows semiquantitative agreement between CG diffusivity and permeation rates with the corresponding AA values and available experimental data. Additionally, it captures the binding to hydrophobic cavities of the protein, aligning well with the AA simulation of the same system. Thus, the results show that our O2 model approximates the behavior observed in the AA simulations. The CG O2 model is compatible with the widely used multifunctional Martini 3 force field for biological simulations, which will allow for the simulation of large biomolecular systems involved in O2's transport in the body.

A General Nonbonded Force Field Based on Accurate Quantum Mechanics Calculations for Elements H-La and Hf-Rn

Noncovalent interactions (NCI) play a central role in numerous physical, chemical, and biological phenomena. An accurate description of NCI is the key to success for any theoretical study in such areas. Although quantum mechanics (QM) methods such as dispersion-corrected density functional theory are sufficiently accurate, their applications are practical only for <300 atoms and <100 ps of simulation time. Thus, empirical force fields (FF) have generally been the only choice for systems with thousands to millions of atoms and for nanoseconds and longer. We want to develop a FF that can be applied to applications of thousands to millions of atoms with an accuracy comparable to QM methods. As the first step, we develop here a new general nonbonded potential (GNB) based on a novel functional form with four adjustable parameters for each element. We report here parameters for elements H-La, Hf-Rn (excluding lanthanides and actinides) by fitting the interaction energy of molecular complexes to QM calculations using the accurate Head-Gordon ωB97M-V density functional. We performed extensive testing of GNB for organic molecules, organometallic molecules, and metal organic-framework (MOF) systems. The mean absolute errors of GNB are 0.37 kcal/mol for the dispersion and mixed groups of the S66 × 8 benchmark set, 0.35 kcal/mol for CO2 adsorption on MOF materials, and 4.53 kcal/mol for the XTMC43 benchmark. GNB outperforms existing FF and in many cases has accuracy comparable to that of QM methods such as PBE-D3. GNB can potentially replace the nonbonded part of existing FFs in a wide range of applications.

Molecular Dynamics Simulation on the Conformational Change of a pH-Switchable Lipid

pH-sensitive lipids are important components of lipid nanoparticles, which enable the targeted delivery and controlled release of drugs. Understanding the mechanism of pH-triggered drug release at the molecular level is important for the rational design of ionizable lipids. Based on a recently reported pH-switchable lipid, named SL2, molecular dynamics (MD) simulations were employed to explore the microscopic mechanism behind the membrane destabilization induced by the conformational change of pH-switchable lipids. The simulated results showed that, at neutral pH, the neutral SL2 lipids assembled with other components (helper lipids and cholesterol) to form a structurally ordered bilayer structure. At this moment, the two hydrocarbon chains of SL2 were closely aligned and inserted in an orderly manner inside of the membrane. With a decrease in pH, the protonation of the pyridinium ring caused a large degree of molecular structural change. The pyridinium ring preferred to form intramolecular H-bonds with the methoxy groups and intermolecular H-bonds with water, resulting in the flip of the pyridinium ring. Meanwhile, due to the structural flip, the two alkane chains showed a more open state, which perturbed the arrangement of molecules within the membrane. The perturbations caused local collapse of the membrane and the formation of water molecule channels, which contributed to the pH-induced drug release. Our results verified the experimentally proposed mechanism at the molecular level and provided more complementary information, which are expected to have deeper insights into the pH-triggered drug release.