CHARMM general force field: A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields

PAFAH2 suppresses synchronized ferroptosis to ameliorate acute kidney injury

Synchronized ferroptosis contributes to nephron loss in acute kidney injury (AKI). However, the propagation signals and the underlying mechanisms of the synchronized ferroptosis for renal tubular injury remain unresolved. Here we report that platelet-activating factor (PAF) and PAF-like phospholipids (PAF-LPLs) mediated synchronized ferroptosis and contributed to AKI. The emergence of PAF and PAF-LPLs in ferroptosis caused the instability of biomembranes and signaled the cell death of neighboring cells. This cascade could be suppressed by PAF-acetylhydrolase (II) (PAFAH2) or by addition of antibodies against PAF. Genetic knockout or pharmacological inhibition of PAFAH2 increased PAF production, augmented synchronized ferroptosis and exacerbated ischemia/reperfusion (I/R)-induced AKI. Notably, intravenous administration of wild-type PAFAH2 protein, but not its enzymatically inactive mutants, prevented synchronized tubular cell death, nephron loss and AKI. Our findings offer an insight into the mechanisms of synchronized ferroptosis and suggest a possibility for the preventive intervention of AKI.

Detection of helical water flows in sub-nanometer channels

Nanoscale flows of liquids can be revealed in various biological processes and underlie a wide range of nanofluidic applications. Though the integral characteristics of these systems, such as permeability and effective diffusion coefficient, can be measured in experiments, the behaviour of the flows within nanochannels is still a matter of speculation. Herein, we used a combination of quadrupolar solid-state NMR spectroscopy, computer simulation, and dynamic vapour sorption measurements to analyse water diffusion inside peptide nanochannels. We detected a helical water flow coexisting with a conventional axial flow that are independent of each other, immiscible, and associated with diffusion coefficients that may differ up to 3 orders of magnitude. The trajectory of the helical flow is dictated by the screw-like distribution of ionic groups within the channel walls, while its flux is governed by external water vapour pressure. Similar flows may occur in other types of nanochannels containing helicoidally distributed ionic groups and be exploited in various nanofluidic lab-on-a-chip devices.

Broadening access to small-molecule parameterization with the force field toolkit

Access to accurate force-field parameters for small molecules is crucial for computational studies of their interactions with proteins. Although a number of general force fields for small molecules exist, e.g., CGenFF, GAFF, and OPLS, they do not cover all common chemical groups and their combinations. The Force Field Toolkit (ffTK) provides a comprehensive graphical interface that streamlines the development of classical parameters for small molecules directly from quantum mechanical (QM) calculations, allowing for force-field generation for almost any chemical group and validation of the fit relative to the target data. ffTK relies on supported external software for the QM calculations, but it can generate the necessary QM input files and parse and analyze the QM output. In previous ffTK versions, support for Gaussian and ORCA QM packages was implemented. Here, we add support for Psi4, an open-source QM package free for all users, thereby broadening user access to ffTK. We also compare the parameter sets obtained with the new ffTK version using Gaussian, ORCA, and Psi4 for three molecules: pyrrolidine, n-propylammonium cation, and chlorobenzene. Despite minor differences between the resulting parameter sets for each compound, most prominently in the dihedral and improper terms, we show that conformational distributions sampled in molecular dynamics simulations using these parameter sets are quite comparable.

Binding of steroid substrates reveals the key to the productive transition of the cytochrome P450 OleP

OleP is a bacterial cytochrome P450 involved in oleandomycin biosynthesis as it catalyzes regioselective epoxidation on macrolide intermediates. OleP has recently been reported to convert lithocholic acid (LCA) into murideoxycholic acid through a highly regioselective reaction and to unspecifically hydroxylate testosterone (TES). Since LCA and TES mainly differ by the substituent group at the C17, here we used X-ray crystallography, equilibrium binding assays, and molecular dynamics simulations to investigate the molecular basis of the diverse reactivity observed with the two steroids. We found that the differences in the structure of TES and LCA affect the capability of these molecules to directly form hydrogen bonds with N-terminal residues of OleP internal helix I. The establishment of these contacts, by promoting the bending of helix I, fosters an efficient trigger of the open-to-closed structural transition that occurs upon substrate binding to OleP and contributes to the selectivity of the subsequent monooxygenation reaction.

CHARMM-GUI Multicomponent Assembler for modeling and simulation of complex multicomponent systems

Atomic-scale molecular modeling and simulation are powerful tools for computational biology. However, constructing models with large, densely packed molecules, non-water solvents, or with combinations of multiple biomembranes, polymers, and nanomaterials remains challenging and requires significant time and expertise. Furthermore, existing tools do not support such assemblies under the periodic boundary conditions (PBC) necessary for molecular simulation. Here, we describe Multicomponent Assembler in CHARMM-GUI that automates complex molecular assembly and simulation input preparation under the PBC. In this work, we demonstrate its versatility by preparing 6 challenging systems with varying density of large components: (1) solvated proteins, (2) solvated proteins with a pre-equilibrated membrane, (3) solvated proteins with a sheet-like nanomaterial, (4) solvated proteins with a sheet-like polymer, (5) a mixed membrane-nanomaterial system, and (6) a sheet-like polymer with gaseous solvent. Multicomponent Assembler is expected to be a unique cyberinfrastructure to study complex interactions between small molecules, biomacromolecules, polymers, and nanomaterials.

Hydropathic AF-2 variants in the androgen receptor gene among androgen insensitivity patients

BACKGROUND

Androgen insensitivity syndrome (AIS) is a common condition among individuals with differences of sexual development (DSD) and results from germline allelic variants in the androgen receptor (AR) gene. Understanding the phenotypic consequences of AR allelic variants that disrupt the activation function 2 (AF2) region is essential to grasping its clinical significance.

OBJECTIVES

This study aims to provide insights into the phenotypic characteristics and clinical impact of AR mutations affecting the AF2 region in AIS patients. We achieve this by reviewing reported AR variants in the AF2 region among individuals with AIS, including identifying a new phenotype associated with the c.2138T>C variant (p.Leu713Pro) in the AR gene.

MATERIALS AND METHODS

We comprehensively reviewed AR variants within the AF2 region reported in AIS and applied molecular dynamics simulations to assess the impact of the p.Leu713Pro variant on protein dynamics.

RESULTS

Our review of reported AR variants in the AF2 region revealed a spectrum of phenotypic outcomes in AIS patients. Molecular dynamics simulations indicated that the p.Leu713Pro variant significantly alters the local dynamics of the AR protein and disrupts the correlation and covariance between variables.

DISCUSSION

The diverse phenotypic presentations observed among individuals with AR variants in the AF2 region highlight the complexity of AIS. The altered protein dynamics resulting from the p.Leu713Pro variant further emphasize the importance of the AF2 region in AR function.

CONCLUSION

Our study provides valuable insights into AR mutations' phenotypic characteristics and clinical impact on the AF2 region in AIS. Moreover, the disruption of protein dynamics underscores the significance of the AF2 region in AR function and its role in the pathogenesis of AIS.

CHARMM-GUI QM/MM Interfacer for a Quantum Mechanical and Molecular Mechanical (QM/MM) Simulation Setup: 1. Semiempirical Methods

Quantum mechanical (QM) treatments, when combined with molecular mechanical (MM) force fields, can effectively handle enzyme-catalyzed reactions without significantly increasing the computational cost. In this context, we present CHARMM-GUI QM/MM Interfacer, a web-based cyberinfrastructure designed to streamline the preparation of various QM/MM simulation inputs with ligand modification. The development of QM/MM Interfacer has been achieved through integration with existing CHARMM-GUI modules, such as PDB Reader and Manipulator, Solution Builder, and Membrane Builder. In addition, new functionalities have been developed to facilitate the one-stop preparation of QM/MM systems and enable interactive and intuitive ligand modifications and QM atom selections. QM/MM Interfacer offers support for a range of semiempirical QM methods, including AM1(+/d), PM3(+/PDDG), MNDO(+/d, +/PDDG), PM6, RM1, and SCC-DFTB, tailored for both AMBER and CHARMM. A nontrivial setup related to ligand modification, link-atom insertion, and charge distribution is automatized through intuitive user interfaces. To illustrate the robustness of QM/MM Interfacer, we conducted QM/MM simulations of three enzyme-substrate systems: dihydrofolate reductase, insulin receptor kinase, and oligosaccharyltransferase. In addition, we have created three tutorial videos about building these systems, which can be found at https://www.charmm-gui.org/demo/qmi. QM/MM Interfacer is expected to be a valuable and accessible web-based tool that simplifies and accelerates the setup process for hybrid QM/MM simulations.

Modular Fluorescent Cholesterol Naphthalimide Probes And Their Application For Cholesterol Trafficking Studies In Cells

Development of fluorescent cholesterol analogs to better understand subcellular cholesterol trafficking is of great interest for cell biology and medicine. Our approach utilizes a bifunctional 1,8-naphthalimide scaffold with a push-pull character, modified on one side with a head group and a linker on the other side connecting it to cholesterol via an ester bond. Through structure-function studies, we've explored how different substituents-linkers and head groups-affect the ability of these fluorescent cholesterol naphthalimide analogs (CNDs) to mimic natural cholesterol behavior at both molecular and cellular levels. We categorized the resulting analogs into three groups: neutral, charged, and those featuring a hydroxyl group. Each compound was assessed for its solvatochromic behavior in organic solvents and model membranes. Extensive all-atom molecular dynamics simulations helped us examine how these analogs perform in model membranes compared to cholesterol. Additionally, we investigated the partitioning of these fluorescent probes in phase-separated giant unilamellar vesicles. We evaluated the uptake and distribution of these probes within mouse fibroblast cells and astrocytes, for their subcellular distributions in lysosomes and compared that to BODIPY-cholesterol, a well-regarded fluorescent cholesterol analog. The internalization efficiency of the fluorescent probes varies in different cell types and is affected mainly by the head groups. Our results demonstrate that the modular design significantly simplifies the creation of fluorescent cholesterol probes bearing distinct spectral, biophysical, and cellular targeting features, which makes it a valuable toolkit for the investigation of subcellular distribution and trafficking of cholesterol and its derivatives.

Deprotonation of formic, acetic acids and bicarbonate ion in slit silica nanopores at infinite dilution and in the presence of electrolytes

Dielectric effects and the coupled electrostatics between the nanoconfined and the internal/external aqueous media contribute to the observed deviations of chemistry within the nanoconfined environment when compared with unconfined systems. A systematic understanding has remained elusive, especially with respect to background salt concentration and boundary condition effects like the nanopore surface chemistry and the reference state used to calculate free energies. We utilize molecular dynamics simulations along with thermodynamic integration to determine the free energy difference associated with acid-base chemistry in 2 nm and 4 nm slit pores open to a bulk-like reservoir. pKa increases are predicted when confining acetic acid, formic acid, and bicarbonate in the slits at infinite dilution conditions. We find that confinement weakens the acids, and the modulation of outer pore surface dipole magnitudes can tune the pKa shift values, suggesting that purely "intrinsic" electrostatic effect on confinement may not exist. At sufficiently high salt concentrations, the dielectric/electrostatic effects on pKa values diminish due to charge screening effects. These discoveries enable future modifications of nanopore chemistries to achieve desirable properties for industrial applications.

Conformational flexibility driving charge-selective substrate translocation across a bacterial transporter

Bacterial membrane porins facilitate the translocation of small molecules while restricting large molecules, and this mechanism remains elusive at the molecular level. Here, we investigate the selective uptake of large cyclic sugars across an unusual passive membrane transporter, CymA, comprising a charged zone and a constricting N terminus segment. Using a combination of electrical recordings, protein mutagenesis and molecular dynamics simulations, we establish substrate translocation across CymA governed by the electrostatic pore properties and conformational dynamics of the constriction segment. Notably, we show that the variation in pH of the environment resulted in reversible modulation of the substrate binding site in the pore, thereby regulating charge-selective transport of cationic, anionic and neutral cyclic sugars. The quantitative kinetics of cyclic sugar translocation across CymA obtained in electrical recordings at different pHs are comparable with molecular dynamics simulations that revealed the transport pathway, energetics and favorable affinity sites in the pore for substrate binding. We further define the molecular basis of cyclic sugar translocation and establish that the constriction segment is flexible and can reside inside or outside the pore, regulating substrate translocation distinct from the ligand-gated transport mechanism. Our study provides novel insights into energy-independent large molecular membrane transport for targeted drug design strategies.

Advanced Molecular Dynamics Model for Investigating Biological-Origin Microfibril Structures

Understanding the atomic-scale structure of wood microfibrils is essential for establishing fundamental properties in various wood-based research aspects, including moisture impact, wood modification, and pretreatment. In this study, we employed molecular dynamics simulations to investigate the arrangement of wood polymers, including cellulose, hemicellulose, and lignin, with a primary focus on the composition of softwood, specifically Norway Spruce wood. We assessed the accuracy of our molecular dynamics model by comparing it with available experimental data, such as density, Young's modulus, and glass transition temperature, which ensures the reliability of our approach. A key aspect of our study involved modeling the active sorption site for water interaction with wood polymers. Our findings revealed that the interaction between water and hemicellulose, particularly within the hemicellulose-cellulose interphase, was the most prominent binding site. This observation aligns with prior research in this field, further strengthening the validity of our results.

New Pyrimidinone Bearing Aminomethylenes and Schiff Bases as Potent Antioxidant, Antibacterial, SARS-CoV-2, and COVID-19 Main Protease MPro Inhibitors: Design, Synthesis, Bioactivities, and Computational Studies

New 2-thioxopyrimidinone derivatives (A1-A10) were synthesized in 87-96% yields via a simple three-component condensation reaction. These compounds were screened extensively through in vitro assays for antioxidant and antibacterial investigations. The DPPH assays resulted in the excellent potency of A6-A10 as antioxidants with IC50 values of 0.83 ± 0.125, 0.90 ± 0.77, 0.36 ± 0.063, 1.4 ± 0.07, and 1.18 ± 0.06 mg/mL, which were much better than 1.79 ± 0.045 mg/mL for the reference ascorbic acid. These compounds exhibited better antibacterial potency against Klebsiella with IC50 values of 2 ± 7, 1.32 ± 8.9, 1.19 ± 11, 1.1 ± 12, and 1.16 ± 11 mg/mL for A6-A10. High-throughput screenings (HTS) of these motifs were carried out including investigation of drug-like behaviors, physiochemical property evaluation, and structure-related studies involving DFT and metabolic transformation trends. The radical scavenging ability of the synthesized motifs was validated through molecular docking studies through ligand-protein binding against human inducible nitric oxide synthase (HINOS) PDB ID: 4NOS, and the results were promising. Furthermore, the antiviral capability of the compounds was examined by in silico studies using two viral proteins PDB ID: 6Y84 and PDB ID: 6LU7. Binding poses of ligands were discussed, and amino acids in the protein binding pockets were investigated, where the tested compounds showed much better binding affinities than the standard inhibitors, proving to be suitable leads for antiviral drug discovery. The stabilities of the molecular docked complexes in real systems were validated by molecular dynamics simulations.

Simulations predict preferred Mg2+ coordination in a nonenzymatic primer-extension reaction center

The mechanism by which genetic information was copied prior to the evolution of ribozymes is of great interest because of its importance to the origin of life. The most effective known process for the nonenzymatic copying of an RNA template is primer extension by a two-step pathway in which 2-aminoimidazole-activated nucleotides first react with each other to form an imidazolium-bridged intermediate that subsequently reacts with the primer. Reaction kinetics, structure-activity relationships, and X-ray crystallography have provided insight into the overall reaction mechanism, but many puzzles remain. In particular, high concentrations of Mg2+ are required for efficient primer extension, but the mechanism by which Mg2+ accelerates primer extension remains unknown. By analogy with the mechanism of DNA and RNA polymerases, a role for Mg2+ in facilitating the deprotonation of the primer 3'-hydroxyl is often assumed, but no catalytic metal ion is seen in crystal structures of the primer-extension complex. To explore the potential effects of Mg2+ binding in the reaction center, we performed atomistic molecular dynamics simulations of a series of modeled complexes in which a Mg2+ ion was placed in the reaction center with inner-sphere coordination with different sets of functional groups. Our simulations suggest that coordination of a Mg2+ ion with both O3' of the terminal primer nucleotide and the pro-Sp nonbridging oxygen of the reactive phosphate of an imidazolium-bridged dinucleotide would help to pre-organize the structure of the primer/template substrate complex to favor the primer-extension reaction. Our results suggest that the catalytic metal ion may play an important role in overcoming electrostatic repulsion between a deprotonated O3' and the reactive phosphate of the bridged dinucleotide and lead to testable predictions of the mode of Mg2+ binding that is most relevant to catalysis of primer extension.

Highly accurate carbohydrate-binding site prediction with DeepGlycanSite

As the most abundant organic substances in nature, carbohydrates are essential for life. Understanding how carbohydrates regulate proteins in the physiological and pathological processes presents opportunities to address crucial biological problems and develop new therapeutics. However, the diversity and complexity of carbohydrates pose a challenge in experimentally identifying the sites where carbohydrates bind to and act on proteins. Here, we introduce a deep learning model, DeepGlycanSite, capable of accurately predicting carbohydrate-binding sites on a given protein structure. Incorporating geometric and evolutionary features of proteins into a deep equivariant graph neural network with the transformer architecture, DeepGlycanSite remarkably outperforms previous state-of-the-art methods and effectively predicts binding sites for diverse carbohydrates. Integrating with a mutagenesis study, DeepGlycanSite reveals the guanosine-5'-diphosphate-sugar-recognition site of an important G-protein coupled receptor. These findings demonstrate DeepGlycanSite is invaluable for carbohydrate-binding site prediction and could provide insights into molecular mechanisms underlying carbohydrate-regulation of therapeutically important proteins.

Discovery of azaleatin as a potential allosteric inhibitor for dengue NS2B-NS3 protease using in vitro and in silico studies

The rise in dengue cases in tropical and sub-tropical areas has become a significant health concern. At present, there is no definitive cure for dengue fever, which underscores the importance of identifying potent inhibitors. Dengue NS2B-NS3 protease is the prime drug target due to its vital function for replication. Quercetin, a flavone, has anti-dengue virus properties but is limited by low bioavailability. Previous studies have shown that methoxy substitution in flavones improves bioavailability and metabolic stability. Azaleatin is a derivative of quercetin with a methoxy substitution at the C5 position, however its ability to inhibit dengue is unknown. In this study, azaleatin was investigated for its inhibition against dengue NS2B-NS3 protease using in vitro and in silico techniques. The fluorescence assay was used to determine the IC50 value and inhibition kinetics. The molecular interaction between azaleatin and NS2B-NS3 was studied using CB-Dock2 and AutoDock Vina. The complex's stability was then analysed using GROMACS. Besides, the ADMETlab 2.0 was utilized to predict pharmacokinetic of the azaleatin. Results showed that azaleatin inhibits dengue NS2B-NS3 protease non-competitively with a Ki of 26.82 µg/ml and an IC50 of 38 µg/ml. Molecular docking indicated binding of the azaleatin to the allosteric pocket of NS2B-NS3 with a docking score of -8.2 kcal/mol. Azaleatin was found stable in the pocket along 100 ns, supporting its inhibitory mode. The compound has favourable pharmacokinetic profiles and conformed to Lipinski's Rule of Five. Taken together, azaleatin inhibits NS2B-NS3 protease in a non-competitive mode, suggesting its potential as safer anti-dengue compound.Communicated by Ramaswamy H. Sarma.

The novel function of an orphan pheromone receptor reveals the sensory specializations of two potential distinct types of sex pheromones in noctuid moth

Sex pheromones play crucial role in mating behavior of moths, involving intricate recognition mechanisms. While insect chemical biology has extensively studied type I pheromones, type II pheromones remain largely unexplored. This study focused on Helicoverpa armigera, a representative species of noctuid moth, aiming to reassess its sex pheromone composition. Our research unveiled two previously unidentified candidate type II sex pheromones-3Z,6Z,9Z-21:H and 3Z,6Z,9Z-23:H-in H. armigera. Furthermore, we identified HarmOR11 as an orphan pheromone receptor of 3Z,6Z,9Z-21:H. Through AlphaFold2 structural prediction, molecular docking, and molecular dynamics simulations, we elucidated the structural basis and key residues governing the sensory nuances of both type I and type II pheromone receptors, particularly HarmOR11 and HarmOR13. This study not only reveals the presence and recognition of candidate type II pheromones in a noctuid moth, but also establishes a comprehensive structural framework for PRs, contributing to the understanding of connections between evolutionary adaptations and the emergence of new pheromone types.

Synthesis, molecular docking and molecular dynamics simulations, drug-likeness studies, ADMET prediction and biological evaluation of novel pyrazole-carboxamides bearing sulfonamide moiety as potent carbonic anhydrase inhibitors

Pyrazoles are unique bioactive molecules with a versatile biological profile and they have gained an important place on pharmaceutical chemistry. Pyrazole compounds containing sulfonamide nuclei also attract attention as carbonic anhydrase (CA) inhibitors. In this study, a library of pyrazole-carboxamides were synthesized and the structures of the synthesized molecules were characterized using FT-IR, 1H-NMR, 13C-NMR and HRMS. Then the inhibition effects of newly synthesized molecules on human erythrocyte hCA I and hCA II isoenzymes were investigated. Ki values of the compounds were in the range of 0.063-3.368 µM for hCA I and 0.007-4.235 µM for hCA II. Molecular docking studies were performed between the most active compounds 6a, 6b and the reference inhibitor, acetazolamide (AAZ) and the hCA I and hCA II receptors to investigate the binding mechanisms between the compounds and the receptors. These compounds showed better interactions than the AAZ. ADMET analyzes were performed for the compounds and it was seen that the compounds did not show AMES toxicity. The stability of the molecular docking results over time was analysed by 50 ns molecular dynamics simulations. Molecular dynamics simulations revealed that 6a and 6b exhibited good stability after docking to the binding sites of hCA I and hCA II receptors, with minor conformational changes and fluctuations.

Gaseous inhibition of the transsulfuration pathway by cystathionine β-synthase

The transsulfuration pathway plays a key role in mammals for maintaining the balance between cysteine and homocysteine, whose concentrations are critical in several biochemical processes. Human cystathionine β-synthase is a heme-containing, pyridoxal 5'-phosphate (PLP)-dependent enzyme found in this pathway. The heme group does not participate directly in catalysis, but has a regulatory function, whereby CO or NO binding inhibits the PLP-dependent reactions. In this study, we explore the detailed structural changes responsible for inhibition using quantum chemical calculations to validate the experimentally observed bonding patterns associated with heme CO and NO binding and molecular dynamics simulations to explore the medium-range structural changes triggered by gas binding and propagating to the PLP active site, which is more than 20 Å distant from the heme group. Our results support a previously proposed mechanical signaling model, whereby the cysteine decoordination associated with gas ligand binding leads to breaking of a hydrogen bond with an arginine residue on a neighbouring helix. In turn, this leads to a shift in position of the helix, and hence also of the PLP cofactor, ultimately disrupting a key hydrogen bond that stabilizes the PLP in its catalytically active form.

Mechanistic insights into G-protein coupling with an agonist-bound G-protein-coupled receptor

G-protein-coupled receptors (GPCRs) activate heterotrimeric G proteins by promoting guanine nucleotide exchange. Here, we investigate the coupling of G proteins with GPCRs and describe the events that ultimately lead to the ejection of GDP from its binding pocket in the Gα subunit, the rate-limiting step during G-protein activation. Using molecular dynamics simulations, we investigate the temporal progression of structural rearrangements of GDP-bound Gs protein (Gs·GDP; hereafter GsGDP) upon coupling to the β2-adrenergic receptor (β2AR) in atomic detail. The binding of GsGDP to the β2AR is followed by long-range allosteric effects that significantly reduce the energy needed for GDP release: the opening of α1-αF helices, the displacement of the αG helix and the opening of the α-helical domain. Signal propagation to the Gs occurs through an extended receptor interface, including a lysine-rich motif at the intracellular end of a kinked transmembrane helix 6, which was confirmed by site-directed mutagenesis and functional assays. From this β2AR-GsGDP intermediate, Gs undergoes an in-plane rotation along the receptor axis to approach the β2AR-Gsempty state. The simulations shed light on how the structural elements at the receptor-G-protein interface may interact to transmit the signal over 30 Å to the nucleotide-binding site. Our analysis extends the current limited view of nucleotide-free snapshots to include additional states and structural features responsible for signaling and G-protein coupling specificity.

Thermodynamic Driving Forces for the Self-Assembly of Diblock Polypeptoids

Peptoid polymers with sequence-defined side chains are observed to self-assemble into a variety of structures spanning nanometer and micron scales. We explored a diblock copolypeptoid, poly(N-decylglycine)10-block-poly(N-2-(2-(2-methoxyethoxy)ethoxy)-ethylglycine)10 (abbreviated as Ndc10-Nte10), which forms crystalline nanofibers and nanosheets as evidenced by recent cryo-transmission electron microscopy, atomic force microscopy, X-ray diffraction, and calorimetry. Using all-atom molecular dynamics simulations, we examined the thermodynamic forces driving such self-assembly and how nanoscale morphology is tailored through modification of the N-terminus or via the addition of small molecules (urea). We have found that the hydrophobic Ndc domain alignment is key to the formation of molecular stacks whose growth is limited by electrostatic repulsion between protonated N-termini. These stacks are the building blocks that assemble via cooperative van der Waals attraction between the tips of extended decyl side chains to form nanofibers or nanosheets with a well-converged intermolecular interaction energy. Assemblies are significantly more stable in urea solution due to its strong attraction to the peptoid-solvent interface. Isolated peptoids exhibit curved all-cis backbones, which straighten within molecular stacks to maximize contact and registry between neighboring molecules. We hypothesize that competition between this attractive interaction and a strain cost for straightening the backbone is what leads to finite stack widths that define crystalline nanofibers of protonated Ndc10-Nte10. Growth is proposed to proceed through backbone unfurling via trans defects, which is more prevalent in aqueous solution than in THF, indicating a possible pathway to self-assembly under experimentally defined synthesis conditions (viz., THF evaporation).

All-atom molecular dynamics simulations of polymer and polyelectrolyte brushes

Densely grafted polymer and polyelectrolyte (PE) brushes, owing to their significant abilities to functionalize surfaces for a plethora of applications in sensing, diagnostics, current rectification, surface wettability modification, drug delivery, and oil recovery, have attracted significant attention over the past several decades. Unfortunately, most of the attention has primarily focused on understanding the properties of the grafted polymer and the PE chains with little attention devoted to studying the behavior of the brush-supported ions (counterions needed to screen the PE chains) and water molecules. Over the past few years, our group has been at the forefront of addressing this gap: we have employed all-atom molecular dynamics (MD) simulations for studying a wide variety of polymer and PE brush systems with specific attention to unraveling the properties and behavior of the brush-supported water molecules and ions. Our findings have revealed some of the most fascinating properties of such brush-supported ions and water molecules, including the most remarkable control of nanofluidic transport afforded by the specific ion and water responses induced by the PE brushes grafted on the inner walls of the nanochannel. This feature article aims to summarize some of our key contributions associated with such atomistic simulations of polymer and PE brushes and brush-supported water molecules and counterions.

Pharmacophore Virtual Screening Identifies Riboflavin as an Inhibitor of the Schistosome Cathepsin B1 Protease with Antiparasitic Activity

Schistosomiasis is a neglected disease of poverty that affects over 200 million people worldwide and relies on a single drug for therapy. The cathepsin B1 cysteine protease (SmCB1) of Schistosoma mansoni has been investigated as a potential target. Here, a structure-based pharmacophore virtual screening (VS) approach was used on a data set of approved drugs to identify potential antischistosomal agents targeting SmCB1. Pharmacophore (PHP) models underwent validation through receiver operating characteristics curves achieving values >0.8. The data highlighted riboflavin (RBF) as a compound of particular interest. A 1 μs molecular dynamics simulation demonstrated that RBF altered the conformation of SmCB1, causing the protease's binding site to close around RBF while maintaining the protease's overall integrity. RBF inhibited the activity of SmCB1 at low micromolar values and killed the parasite in vitro. Finally, in a murine model of S. mansoni infection, oral administration of 100 mg/kg RBF for 7 days significantly decreased worm burdens by ∼20% and had a major impact on intestinal and fecal egg burdens, which were decreased by ∼80%.

Lambda-ABF: Simplified, Portable, Accurate, and Cost-Effective Alchemical Free-Energy Computation

We introduce the lambda-Adaptive Biasing Force (lambda-ABF) method for the computation of alchemical free-energy differences. We propose a software implementation and showcase it on biomolecular systems. The method arises from coupling multiple-walker adaptive biasing force with λ-dynamics. The sampling of the alchemical variable is continuous and converges toward a uniform distribution, making manual optimization of the λ schedule unnecessary. Contrary to most other approaches, alchemical free-energy estimates are obtained immediately without any postprocessing. Free diffusion of λ improves orthogonal relaxation compared to fixed-λ thermodynamic integration or free-energy perturbation. Furthermore, multiple walkers provide generic orthogonal space coverage with minimal user input and negligible computational overhead. We show that our high-performance implementations coupling the Colvars library with NAMD and Tinker-HP can address real-world cases including ligand-receptor binding with both fixed-charge and polarizable models, with a demonstrably richer sampling than fixed-λ methods. The implementation is fully open-source, publicly available, and readily usable by practitioners of current alchemical methods. Thanks to the portable Colvars library, lambda-ABF presents a unified user interface regardless of the back-end (NAMD, Tinker-HP, or any software to be interfaced in the future), sparing users the effort of learning multiple interfaces. Finally, the Colvars Dashboard extension of the visual molecular dynamics (VMD) software provides an interactive monitoring and diagnostic tool for lambda-ABF simulations.

Analyzing energetics and dynamics of hepatitis C virus polymerase interactions with marine bacterial compounds: a computational study

Hepatitis C Virus (HCV) is a significant health concern affecting a large portion of the global population and is a major cause of acute liver diseases, including cirrhosis. The variability in the HCV genome mainly results from the rapid replication facilitated by the NS5B polymerase, making it a prime target for anti-HCV drug development. This study explores potential compounds from marine bacteria that could inhibit the HCV NS5B polymerase by virtual screening, analyzing the energetics, and dynamic behavior of target-compound complexes. Virtual screening with the Lipinski filter was employed to select compounds from the marine bacteria database that demonstrated strong binding affinity to NS5B. The top four (CMNPD27216, CMNPD21066, CMNPD21065, and CMNPD27283) compounds, ranked by their re-docking scores, underwent additional evaluation. Molecular dynamics simulations for 200 ns were conducted to assess the dynamic stability of these complexes in a solvent environment. Furthermore, methods such as MM-GBSA, PCA, and free energy landscape analysis were used to analyze the system's energetics and identify stable conformations by locating transition states. The findings suggest that these compounds exhibit promising binding capabilities to HCV polymerase and could be considered for future experimental validation.

Learning QM/MM potential using equivariant multiscale model

The machine learning (ML) method emerges as an efficient and precise surrogate model for high-level electronic structure theory. Its application has been limited to closed chemical systems without considering external potentials from the surrounding environment. To address this limitation and incorporate the influence of external potentials, polarization effects, and long-range interactions between a chemical system and its environment, the first two terms of the Taylor expansion of an electrostatic operator have been used as extra input to the existing ML model to represent the electrostatic environments. However, high-order electrostatic interaction is often essential to account for external potentials from the environment. The existing models based only on invariant features cannot capture significant distribution patterns of the external potentials. Here, we propose a novel ML model that includes high-order terms of the Taylor expansion of an electrostatic operator and uses an equivariant model, which can generate a high-order tensor covariant with rotations as a base model. Therefore, we can use the multipole-expansion equation to derive a useful representation by accounting for polarization and intermolecular interaction. Moreover, to deal with long-range interactions, we follow the same strategy adopted to derive long-range interactions between a target system and its environment media. Our model achieves higher prediction accuracy and transferability among various environment media with these modifications.

Triplet-Triplet Annihilation Upconverting Liposomes: Mechanistic Insights into the Role of Membranes in Two-Dimensional TTA-UC

Triplet-triplet annihilation upconversion (TTA-UC) implemented in nanoparticle assemblies is of emerging interest in biomedical applications, including in drug delivery and imaging. As it is a bimolecular process, ensuring sufficient mobility of the sensitizer and annihilator to facilitate effective collision in the nanoparticle is key. Liposomes can provide the benefits of two-dimensional confinement and condensed concentration of the sensitizer and annihilator along with superior fluidity compared to other nanoparticle assemblies. They are also biocompatible and widely applied across drug delivery modalities. However, there are relatively few liposomal TTA-UC systems reported to date, so systematic studies of the influence of the liposomal environment on TTA-UC are currently lacking. Here, we report the first example of a BODIPY-based sensitizer TTA-UC system within liposomes and use this system to study TTA-UC generation and compare the relative intensity of the anti-Stokes signal for this system as a function of liposome composition and membrane fluidity. We report for the first time on time-resolved spectroscopic studies of TTA-UC in membranes. Nanosecond transient absorption data reveal the BODIPY-perylene dyad sensitizer has a long triplet lifetime in liposome with contributions from three triplet excited states, whose lifetimes are reduced upon coinclusion of the annihilator due to triplet-triplet energy transfer, to a greater extent than in solution. This indicates triplet energy transfer between the sensitizer and the annihilator is enhanced in the membrane system. Molecular dynamics simulations of the sensitizer and annihilator TTA collision complex are modeled in the membrane and confirm the co-orientation of the pair within the membrane structure and that the persistence time of the bound complex exceeds the TTA kinetics. Modeling also reliably predicted the diffusion coefficient for the sensitizer which matches closely with the experimental values from fluorescence correlation spectroscopy. The relative intensity of the TTA-UC output across nine liposomal systems of different lipid compositions was explored to examine the influence of membrane viscosity on upconversion (UC). UC showed the highest relative intensity for the most fluidic membranes and the weakest intensity for highly viscous membrane compositions, including a phase separation membrane. Overall, our study reveals that the co-orientation of the UC pair within the membrane is crucial for effective TTA-UC within a biomembrane and that the intensity of the TTA-UC output can be tuned in liposomal nanoparticles by modifying the phase and fluidity of the liposome. These new insights will aid in the design of liposomal TTA-UC systems for biomedical applications.

AI-based prediction of protein-ligand binding affinity and discovery of potential natural product inhibitors against ERK2

Determination of protein-ligand binding affinity (PLA) is a key technological tool in hit discovery and lead optimization, which is critical to the drug development process. PLA can be determined directly by experimental methods, but it is time-consuming and costly. In recent years, deep learning has been widely applied to PLA prediction, the key of which lies in the comprehensive and accurate representation of proteins and ligands. In this study, we proposed a multi-modal deep learning model based on the early fusion strategy, called DeepLIP, to improve PLA prediction by integrating multi-level information, and further used it for virtual screening of extracellular signal-regulated protein kinase 2 (ERK2), an ideal target for cancer treatment. Experimental results from model evaluation showed that DeepLIP achieved superior performance compared to state-of-the-art methods on the widely used benchmark dataset. In addition, by combining previously developed machine learning models and molecular dynamics simulation, we screened three novel hits from a drug-like natural product library. These compounds not only had favorable physicochemical properties, but also bound stably to the target protein. We believe they have the potential to serve as starting molecules for the development of ERK2 inhibitors.

Exploring ligands that target von Willebrand factor selectively under oxidizing conditions through docking and molecular dynamics simulations

The blood protein von Willebrand factor (VWF) is a large multimeric protein that, when activated, binds to blood platelets, tethering them to the site of vascular injury and initiating blood coagulation. This process is critical for the normal hemostatic response, but especially under inflammatory conditions, it is thought to be a major player in pathological thrombus formation. For this reason, VWF has been the target for the development of anti-thrombotic therapeutics. However, it is challenging to prevent pathological thrombus formation while still allowing normal physiological blood coagulation, as currently available anti-thrombotic therapeutics are known to cause unwanted bleeding, in particular intracranial hemorrhage. This work explores the possibility of inhibiting VWF selectively under the inflammatory conditions present during pathological thrombus formation. In particular, the A2 domain of VWF is known to inhibit the neighboring A1 domain from binding to the platelet surface receptor GpIbα, and this auto-inhibitory mechanism has been shown to be removed by oxidizing agents released during inflammation. Hence, finding drug molecules that bind at the interface between A1 and A2 only under oxidizing conditions could restore such an auto-inhibitory mechanism. Here, by using a combination of computational docking, molecular dynamics simulations, and free energy perturbation calculations, a ligand from the ZINC15 database was identified that binds at the A1A2 interface, with the interaction being stronger under oxidizing conditions. The results provide a framework for the discovery of drug molecules that bind to a protein selectively in the presence of inflammatory conditions.

Targeted removal of the FA2 site on human albumin prevents fatty acid-mediated inhibition of Zn2+ binding

Zinc is required for virtually all biological processes. In plasma, Zn2+ is predominantly transported by human serum albumin (HSA), which possesses two Zn2+-binding sites of differing affinities (sites A and B). Fatty acids (FAs) are also transported by HSA, with seven structurally characterized FA-binding sites (named FA1-FA7) known. FA binding inhibits Zn2+-HSA interactions, in a manner that can impact upon hemostasis and cellular zinc uptake, but the degree to which binding at specific FA sites contributes to this inhibition is unclear. Wild-type HSA and H9A, H67A, H247A, and Y150F/R257A/S287A (FA2-KO) mutant albumins were expressed in Pichia pastoris. Isothermal titration calorimetry studies revealed that the Zn2+-binding capacity at the high-affinity Zn2+ site (site A) was reduced in H67A and H247A mutants, with site B less affected. The H9A mutation decreased Zn2+ binding at the lower-affinity site, establishing His9 as a site B ligand. Zn2+ binding to HSA and H9A was compromised by palmitate, consistent with FA binding affecting site A. 13C-NMR experiments confirmed that the FA2-KO mutations prohibited FA binding at site FA2. Zn2+ binding to the FA2-KO mutant was unaffected by myristate, suggesting binding at FA2 is solely responsible for inhibition. Molecular dynamics studies identified the steric obstruction exerted by bound FA in site FA2, which impedes the conformational change from open (FA-loaded) to closed (FA-free) states, required for Zn2+ to bind at site A. The successful targeting of the FA2 site will aid functional studies exploring the interplay between circulating FA levels and plasma Zn2+ speciation in health and disease.

Unleashing the power of generative AI in drug discovery

Artificial intelligence (AI) is revolutionizing drug discovery by enhancing precision, reducing timelines and costs, and enabling AI-driven computer-aided drug design. This review focuses on recent advancements in deep generative models (DGMs) for de novo drug design, exploring diverse algorithms and their profound impact. It critically analyses the challenges that are intricately interwoven into these technologies, proposing strategies to unlock their full potential. It features case studies of both successes and failures in advancing drugs to clinical trials with AI assistance. Last, it outlines a forward-looking plan for optimizing DGMs in de novo drug design, thereby fostering faster and more cost-effective drug development.

Network pharmacology, molecular docking, and molecular dynamics simulation to elucidate the mechanism of anti-aging action of Tinospora cordifolia

Scientific research has demonstrated that Tinospora cordifolia acts as an anti-aging agent in several experimental models, generating global interest in its underlying molecular mechanisms of this activity. The aim of the study was to identify the possible phytochemical compounds of T. cordifolia that might combat age-related illness through integrating network pharmacology, molecular docking techniques, and molecular dynamics (MD) study to explore their potential mechanisms of action. To carry out this study, several databases were used, including PubChem, KNApSAcK family database, PubMed, SwissADME, Molsoft, SwissTargetPrediction, GeneCards, and OMIM database. For network development and GO enrichment analysis KEGG, ShinyGo 0.77, and the STRING database were used. For better analysis, the networks were also constructed using Cytoscape 3.9.1. The Cytoscape network analyzer tool was used for data analysis, and molecular docking was done via Vina-GPU-2.0. The best compounds and AKT1 were finally subjected to MD simulation for 100 ns. The CytoHubba plugin of Cytoscape identified ten key targets, commonly called hub genes, including AKT1, GAPDH, and TP53, and so on. GO and KEGG pathway enrichment analysis revealed the relevant biological processes, cellular components, and molecular functions involved in treating aging-related disorders. KEGG pathway analysis involved neuroactive ligand-receptor interactions, lipid and atherosclerosis, and cAMP signaling. The docking of 100 T. cordifolia compounds with AKT1 demonstrated good binding affinity, particularly for Amritoside, Sitagliptin, Berberine, and Piperine. Finally, the relative stability of four-hit phytochemicals was validated by MD simulation, which may be the most crucial compound for anti-aging activity. In conclusion, this study used network pharmacology, molecular docking, and MD simulation to identify the compounds in T. cordifolia and proposed a potential mechanism for anti-aging activity. These results suggest future directions for the prevention and treatment of age-related diseases.

Identification of the potential TLR7 antagonists by virtual screening and experimental validation

Toll-like receptor 7 (TLR7) is highly expressed in dendritic cells (DCs) and B cells, and its aberrant activation can promote disease progression in systemic lupus erythematosus (SLE). We utilized structure-based virtual screening and experimental validation to screen natural products from TargetMol for potential TLR7 antagonists. Our results of molecular docking and molecular dynamics simulation showed that Mogroside V (MV) strongly interacted with TLR7, with stable open-TLR7-MV and close-TLR7-MV complexes. Furthermore, in vitro experiments demonstrated that MV significantly inhibited B cell differentiation in a concentration-dependent manner. In addition to TLR7, we also revealed a strong interaction of MV with all TLRs, including TLR4. The above results suggested that MV might be a potential TLR7 antagonist deserving of further study.

Coumarin linked thiazole derivatives as potential α-glucosidase inhibitors to treat diabetes mellitus

Diabetes is a leading cause of kidney failure, blindness, heart attacks and lower limb amputation. Prevalence of diabetes is rising globally. α-glucosidase is validated target for controlling hyperglycemia because of its role in catalysing hydrolysis of carbohydrates to glucose in GIT. In an attempt to find novel safe and effective α-glucosidase inhibitors, coumarin linked thiazole was identified as potential scaffold on the basis of its interactions with the active site of α-glucosidase studied in silico. A series of coumarin linked thiazole derivatives were synthesized and analyzed for α-glucosidase inhibitory potential in an in-vitro assay. The synthesized molecules showed potent inhibition of α-glucosidase with IC50 values ranging from 0.14 to 9.38 μM. The most potent compound 2-[(4-bromophenyl) amino)-N-(4- (2-oxo-2H-chromen-3-yl) thiazol-2-yl] acetamide was further docked with α-glucosidase and molecular dynamics studies were carried out for 100 ns which suggested the stability of protein and ligand in the protein active site over the simulation period and role of hydrophobic interactions slightly more than the electrostatic/polar interactions in ligand- receptor stability. In summary, our results demonstrate efficacy of coumarin-linked thiazole as potential leads for further optimization and development.

CHARMM GUI Membrane Builder for oxidized phospholipid membrane modeling and simulation

Oxidative stress leads to the production of oxidized phospholipids (oxPLs) that modulate the biophysical properties of phospholipid monolayers and bilayers. As many immune cells are responsible for surveilling cells and tissues for the presence of oxPLs, oxPL-dependent mechanisms have been suggested as targets for treating chronic kidney disease, atherosclerosis, diabetes, and cancer metastasis. This review details recent experimental and computational studies that characterize oxPLs' behaviors in various monolayers and bilayers. These studies investigate how the tail length and polar functional groups of OxPLs impact membrane properties, how oxidized membranes can be stabilized, and how membrane integrity is generally affected by oxidized lipids. In addition, for oxPL-containing membrane modeling and simulation, CHARMM-GUI Membrane Builder has been extended to support a variety of oxPLs, accelerating the simulation system building process for these biologically relevant lipid bilayers.

Identification of a 1, 8-naphthyridine-containing compound endowed with the inhibition of p53-MDM2/X interaction signaling: a computational perspective

Various studies have established that molecules specific for MDMX inhibition or optimized for dual inhibition of p53-MDM2/MDMX interaction signaling are more suitable for activating the Tp53 gene in tumor cells. Nevertheless, there are sparse numbers of approved molecules to treat the health consequences brought by the lost p53 functions in tumor cells. Consequently, this study explored the potential of a small molecule ligand containing 1, 8-naphthyridine scaffold to act as a dual inhibitor of p53-MDM2/X interactions using computational methods. The results obtained from quantum mechanical calculations revealed our studied compound entitled CPO is more stable but less reactive compared to standard dual inhibitor RO2443. Like RO2443, CPO also exhibited good non-linear optical properties. The results of molecular docking studies predicted that CPO has a higher potential to inhibit MDM2/MDMX than RO2443. Furthermore, CPO was stable over 50 ns molecular dynamics (MD) simulation in complex with MDM2 and MDMX respectively. On the whole, CPO also exhibited good drug-likeness and pharmacokinetics properties compared to RO2443 and was found with more anti-cancer activity than RO2443 in bioactivity prediction. CPO is anticipated to elevate effectiveness and alleviate drug resistance in cancer therapy. Ultimately, our results provide an insight into the mechanism that underlay the inhibition of p53-MDM2/X interactions by a molecule containing 1, 8-naphthyridine scaffold in its molecular structure.

Insights into short chain polyethylene penetration of phospholipid bilayers via atomistic molecular dynamics simulations

The escalation of global plastic production, reaching an annual output of 400 million tons, has significantly intensified concerns regarding plastic waste management. This has been exacerbated by improper recycling and disposal practices, contributing to the impending crisis of plastic pollution. Predictions indicate that by 2025, the environment will bear the burden of over ten billion metric tons of accumulated plastic waste. This situation has led to the concerning release of microplastics and nanoplastics (NPs) into the environment as plastic materials degrade, thereby posing risks to both ecosystems and human health. Nanoparticle interactions with living organisms have garnered significant attention due to their potential to disrupt vital biological processes. Of particular interest are lipid membranes, acting as crucial gatekeepers, underscoring the importance of comprehending the intricate process of NP penetration. Molecular dynamics (MD) simulations serve as a robust tool, offering molecular-level insights into these intricate interactions. In this study, we leverage all-atom MD simulations to delve into the interactions between lipid bilayers and polyethylene (PETH) chains of varying lengths. The investigation spans diverse lipid bilayer compositions-ranging from pure POPC to POPC:DPPC mixtures-revealing how PETH accommodates itself, adopts extended conformations, and influences membrane structure and ordering. Significantly, while longer PETH chains demonstrate limited passive diffusion, their potential to penetrate bilayers over extended timescales emerges as a significant revelation. Overall, this research significantly advances our comprehension of NP-membrane interactions, shedding light on the potential environmental and health implications that lie ahead.

Mechanistic insights into the conformational changes and alterations in residual communications due to the mutations in the pncA Gene of Mycobacterium tuberculosis: A computational perspective for effective therapeutic solutions

Due to its emerging resistance to first-line anti-TB medications, tuberculosis (TB) is one of the most contagious illness in the world. According to reports, the effectiveness of treating TB is severely impacted by drug resistance, notably resistance caused by mutations in the pncA gene-encoded pyrazinamidase (PZase) to the front-line drug pyrazinamide (PZA). The present study focused on investigating the resistance mechanism caused by the mutations D12N, T47A, and H137R to better understand the structural and molecular events responsible for the resistance acquired by the pncA gene of Mycobacterium tuberculosis (MTB) at the structural level. Bioinformatics analysis predicted that all three mutations were deleterious and located near the active centre of the pncA, affecting its functional activity. Furthermore, molecular dynamics simulation (MDS) results established that mutations significantly reduced the structural stability and caused the rearrangement of FE2+ in the active centre of pncA. Moreover, essential dynamics analysis, including principal component analysis (PCA) and free energy landscape (FEL), concluded variations in the protein motion and decreased conformational space in the mutants. Additionally, the mutations potentially impacted the network topologies and altered the residual communications in the network. The complex simulation study results established the significant movement of the flap region from the active centre of mutant complexes, further supporting the flap region's significance in developing resistance to the PZA drug. This study advances our knowledge of the primary cause of the mechanism of PZA resistance and the structural dynamics of pncA mutants, which will help us to design new and potent chemical scaffolds to treat drug-resistant TB (DR-TB).

Bayesian network models identify cooperative GPCR:G protein interactions that contribute to G protein coupling

Cooperative interactions in protein-protein interfaces demonstrate the interdependency or the linked network-like behavior and their effect on the coupling of proteins. Cooperative interactions also could cause ripple or allosteric effects at a distance in protein-protein interfaces. Although they are critically important in protein-protein interfaces, it is challenging to determine which amino acid pair interactions are cooperative. In this work, we have used Bayesian network modeling, an interpretable machine learning method, combined with molecular dynamics trajectories to identify the residue pairs that show high cooperativity and their allosteric effect in the interface of G protein-coupled receptor (GPCR) complexes with Gα subunits. Our results reveal six GPCR:Gα contacts that are common to the different Gα subtypes and show strong cooperativity in the formation of interface. Both the C terminus helix5 and the core of the G protein are codependent entities and play an important role in GPCR coupling. We show that a promiscuous GPCR coupling to different Gα subtypes, makes all the GPCR:Gα contacts that are specific to each Gα subtype (Gαs, Gαi, and Gαq). This work underscores the potential of data-driven Bayesian network modeling in elucidating the intricate dependencies and selectivity determinants in GPCR:G protein complexes, offering valuable insights into the dynamic nature of these essential cellular signaling components.

Computational identification of new TKI as potential noncovalent reversible EGFRL858R/T790M inhibitors: VHTS, molecular docking, DFT study and molecular dynamic simulation

The mutations concerned with non-small cell lung cancer involving epidermal growth factor receptor of tyrosine kinase family have primarily targeted. In this study, we employed a scalable high-throughput virtual screening (HTVS) framework and a targeted compound library of over 50.000 Erlotinib-derived compounds as noncovalent reversible EGFRL858R/T790M inhibitors. Our HTVS work flow leverages include HTVS, SP (Standard Precision) and XP (Extra Precision) docking protocol along with its relative binding free energy calculation, cluster analysis study and ADMET properties. Then we used multiple ns-time scale molecular dynamics (MD) simulations and density functional theory (DFT) precise calculation techniques to elucidate how the bound ligand interact with the complexes conformational states involving motions both proximal and distal to the binding site. Based on glide score and protein-ligand interactions, the highest scoring molecule was selected for molecular dynamic simulation providing a complete insight into the conformational stability. A hyperfine analysis of DFT based refinement strategy highly supported their stability by strong intermolecular interactions. Together, our results demonstrate that the virtually screened top retained molecules present the best moieties introduced to Erlotinib. They exhibit interesting pharmacokinetic properties that can act as potent antitumor drug candidates than the lead compound drug and in some extent tackling the drug resistance problem which offer a springboard for further therapeutic experiments and applications.Communicated by Ramaswamy H. Sarma.

Discovery of New Dual-Target Agents Against PPAR-γ and α-Glucosidase Enzymes with Molecular Modeling Methods: Molecular Docking, Molecular Dynamic Simulations, and MM/PBSA Analysis

Type 2 diabetes mellitus (T2DM) has become a serious public health problem both in our country and worldwide, being the most prevalent type of diabetes. The combined use of drugs in the treatment of T2DM leads to serious side effects, including gastrointestinal problems, liver toxicity, hypoglycemia, and treatment costs. Hence, there has been a growing emphasis on drugs that demonstrate dual interactions. Several studies have suggested that dual-target agents for peroxisome proliferator-activated receptor-γ (PPAR-γ) and alpha-glucosidase (α-glucosidase) could be a potent approach for treating patients with diabetes. We aim to develop new antidiabetic agents that target PPAR-γ and α-glucosidase enzymes using molecular modeling techniques. These compounds show dual interactions, are more effective, and have fewer side effects. The molecular docking method was employed to investigate the enzyme-ligand interaction mechanisms of 159 newly designed compounds with target enzymes. Additionally, we evaluated the ADME properties and pharmacokinetic suitability of these compounds based on Lipinski and Veber's rules. Compound 70, which exhibited favorable ADME properties, demonstrated more effective binding energy with both PPAR-γ and α-glucosidase enzymes (-12,16 kcal/mol, -10.07 kcal/mol) compared to the reference compounds of Acetohexamide (-9.31 kcal/mol, -7.48 kcal/mol) and Glibenclamide (-11.12 kcal/mol, -8.66 kcal/mol). Further, analyses of MM/PBSA binding free energy and molecular dynamics (MD) simulations were conducted for target enzymes with compound 70, which exhibited the most favorable binding affinities with both enzymes. Based on this information, our study aims to contribute to the development of new dual-target antidiabetic agents with improved efficacy, reduced side effects, and enhanced reliability for diabetes treatment.

Current perspectives and trend of computer-aided drug design: a review and bibliometric analysis

AIM

Computer-aided drug design (CADD) is a drug design technique for computing ligand-receptor interactions and is involved in various stages of drug development. To better grasp the frontiers and hotspots of CADD, we conducted a review analysis through bibliometrics.

METHODS

A systematic review of studies published between 2000 and 20 July 2023 was conducted following the PRISMA guidelines. Literature on CADD was selected from the Web of Science Core Collection. General information, publications, output trends, countries/regions, institutions, journals, keywords, and influential authors were visually analyzed using software such as Excel, VOSviewer, RStudio, and CiteSpace.

RESULTS

A total of 2031 publications were included. These publications primarily originated from 99 countries or regions led by the U.S. and China. Among the contributors, MacKerell AD had the highest number of articles and the greatest influence. The Journal of Medicinal Chemistry was the most cited journal, whereas the Journal of Chemical Information and Modeling had the highest number of publications.

CONCLUSIONS

Influential authors in the field were identified. Current research shows active collaboration between countries, institutions, and companies. CADD technologies such as homology modeling, pharmacophore modeling, quantitative conformational relationships, molecular docking, molecular dynamics simulation, binding free energy prediction, and high-throughput virtual screening can effectively improve the efficiency of new drug discovery. Artificial intelligence-assisted drug design and screening based on CADD represent key topics that will influence future development. Furthermore, this paper will be helpful in better understanding the frontiers and hotspots of CADD.

Identification of Mulberrofuran as a potent inhibitor of hepatitis A virus 3Cpro and RdRP enzymes through structure-based virtual screening, dynamics simulation, and DFT studies

Hepatitis is a medical condition characterized by inflammation of the liver. It is commonly caused by the hepatitis viruses A, B, C, D, and E. Hepatitis A virus (HAV) is highly contagious and can spread from infected individuals, through contaminated food, blood, or can also be water-borne. As per the statistics of World Health Organization (WHO), HAV infects about 1.4 million individuals each year globally. In this research work, we have focused on identifying natural product-based potential inhibitors for the two major enzymes of HAV namely 3C proteinase (3Cpro) and RNA-directed RNA polymerase (RdRP). The enzyme 3Cpro plays an important role in proteolytic activity that promotes viral maturation and infectivity. RNA-directed RNA polymerase facilitate viral replication and transcription. Structure-based virtual screening was carried out using NPACT database that contains a collection of 1574 curated plant-derived natural compounds that are validated by experiments. The screening procedure identified the phytochemical Mulberrofuran W, which could bind to both the targets 3Cpro and RdRP. The phytochemical Mulberrofuran W also had better binding affinity compared to the control compounds atropine and pyridinyl ester, which are previously identified inhibitors of HAV 3Cpro and RdRP, respectively. The Mulberrofuran W bound 3Cpro and RdRP complexes were subjected to 200 ns molecular dynamics simulations and were found to be stable and interacting with the active site of the enzymes throughout the course of complex MD simulations. In addition to DFT, MMGBSA studies were also performed to validate the identified potential inhibitor further. The identified phytochemical Mulberrofuran W can be considered as a new potential drug candidate and could be taken up for experimental evaluation against HAV infection.

In silico designing of novel epitope-based peptide vaccines against HIV-1

The HIV-1 virus has been regarded as a catastrophe for human well-being. The global incidence of HIV-1-infected individuals is increasing. Hence, development of effective immunostimulatory molecules has recently attracted an increasing attention in the field of vaccine design against HIV-1 infection. In this study, we explored the impacts of CD40L and IFN-γ as immunostimulatory adjuvants for our candidate HIV-1 Nef vaccine in human and mouse using immunoinformatics analyses. Overall, 18 IFN-γ-based vaccine constructs (9 constructs in human and 9 constructs in mouse), and 18 CD40L-based vaccine constructs (9 constructs in human and 9 constructs in mouse) were designed. To find immunogenic epitopes, important characteristics of each component (e.g., MHC-I and MHC-II binding, and peptide-MHC-I/MHC-II molecular docking) were determined. Then, the selected epitopes were applied to create multiepitope constructs. Finally, the physicochemical properties, linear and discontinuous B cell epitopes, and molecular interaction between the 3D structure of each construct and CD40, IFN-γ receptor or toll-like receptors (TLRs) were predicted. Our data showed that the full-length CD40L and IFN-γ linked to the N-terminal region of Nef were capable of inducing more effective immune response than multiepitope vaccine constructs. Moreover, molecular docking of the non-allergenic full-length- and epitope-based CD40L and IFN-γ constructs to their cognate receptors, CD40 and IFN-γ receptors, and TLRs 4 and 5 in mouse were more potent than in human. Generally, these findings suggest that the full forms of these adjuvants could be more efficient for improvement of HIV-1 Nef vaccine candidate compared to the designed multiepitope-based constructs.

Therapeutic peptides for coronary artery diseases: in silico methods and current perspectives

Many drug formulations containing small active molecules are used for the treatment of coronary artery disease, which affects a significant part of the world's population. However, the inadequate profile of these molecules in terms of therapeutic efficacy has led to the therapeutic use of protein and peptide-based biomolecules with superior properties, such as target-specific affinity and low immunogenicity, in critical diseases. Protein‒protein interactions, as a consequence of advances in molecular techniques with strategies involving the combined use of in silico methods, have enabled the design of therapeutic peptides to reach an advanced dimension. In particular, with the advantages provided by protein/peptide structural modeling, molecular docking for the study of their interactions, molecular dynamics simulations for their interactions under physiological conditions and machine learning techniques that can work in combination with all these, significant progress has been made in approaches to developing therapeutic peptides that can modulate the development and progression of coronary artery diseases. In this scope, this review discusses in silico methods for the development of peptide therapeutics for the treatment of coronary artery disease and strategies for identifying the molecular mechanisms that can be modulated by these designs and provides a comprehensive perspective for future studies.

Pulsed electric field induces exocytosis and overexpression of MAGE antigens in melanoma

Nanosecond pulsed electric field (nsPEF) has emerged as a promising approach for inducing cell death in melanoma, either as a standalone treatment or in combination with chemotherapeutics. However, to date, there has been a shortage of studies exploring the impact of nsPEF on the expression of cancer-specific molecules. In this investigation, we sought to assess the effects of nsPEF on melanoma-specific MAGE (Melanoma Antigen Gene Protein Family) expression. To achieve this, melanoma cells were exposed to nsPEF with parameters set at 8 kV/cm, 200 ns duration, 100 pulses, and a frequency of 10 kHz. We also aimed to comprehensively describe the consequences of this electric field on melanoma cells' invasion and proliferation potential. Our findings reveal that following exposure to nsPEF, melanoma cells release microvesicles containing MAGE antigens, leading to a simultaneous increase in the expression and mRNA content of membrane-associated antigens such as MAGE-A1. Notably, we observed an unexpected increase in the expression of PD-1 as well. While we did not observe significant differences in the cells' proliferation or invasion potential, a remarkable alteration in the cells' metabolomic and lipidomic profiles towards a less aggressive phenotype was evident. Furthermore, we validated these results using ex vivo tissue cultures and 3D melanoma culture models. Our study demonstrates that nsPEF can elevate the expression of membrane-associated proteins, including melanoma-specific antigens. The mechanism underlying the overexpression of MAGE antigens involves the initial release of microvesicles containing MAGE antigens, followed by a gradual increase in mRNA levels, ultimately resulting in elevated expression of MAGE antigens post-experiment. These findings shed light on a novel method for modulating cancer cells to overexpress cancer-specific molecules, thereby potentially enhancing their sensitivity to targeted anticancer therapy.

Actuation mechanism of a nanoscale drilling rig based on nested carbon nanotubes

With the increasing emphasis on health and the continuous improvement of medical standards, more and more micro/nano devices are being used in the medical field. However, the existing micro/nano devices cannot effectively solve various problems encountered in medical processes and achieve specific therapeutic effects. Based on this, this article designs a new type of nanoscale drilling rig. The nanoscale drilling rig is composed of double-layer nested carbon nanotubes with multiple electrodes, and is powered by an external power source, making it easy to perform long-term surgery in the human body. Through coding strategies, we can adjust the surface charge density and distribution of the nanoscale drilling rig, thereby controlling its periodical rotation and achieving precise medical treatment. In addition, in order to control the length of the nanoscale drill bit, meet the treatment needs of different parts of the human body, and reduce damage to the human body, we have designed a structure of ion electric double layers so that the drill bit can be fixed in different positions, reducing the risk of treatment to a certain extent. This drilling rig enriches the functions of micro/nano devices, which is beneficial for the development of the medical industry.

Identifying and Assessing Putative Allosteric Sites and Modulators for CXCR4 Predicted through Network Modeling and Site Identification by Ligand Competitive Saturation

The chemokine receptor CXCR4 is a critical target for the treatment of several cancer types and HIV-1 infections. While orthosteric and allosteric modulators have been developed targeting its extracellular or transmembrane regions, the intramembrane region of CXCR4 may also include allosteric binding sites suitable for the development of allosteric drugs. To investigate this, we apply the Gaussian Network Model (GNM) to the monomeric and dimeric forms of CXCR4 to identify residues essential for its local and global motions located in the hinge regions of the protein. Residue interaction network (RIN) analysis suggests hub residues that participate in allosteric communication throughout the receptor. Mutual residues from the network models reside in regions with a high capacity to alter receptor dynamics upon ligand binding. We then investigate the druggability of these potential allosteric regions using the site identification by ligand competitive saturation (SILCS) approach, revealing two putative allosteric sites on the monomer and three on the homodimer. Two screening campaigns with Glide and SILCS-Monte Carlo docking using FDA-approved drugs suggest 20 putative hit compounds including antifungal drugs, anticancer agents, HIV protease inhibitors, and antimalarial drugs. In vitro assays considering mAB 12G5 and CXCL12 demonstrate both positive and negative allosteric activities of these compounds, supporting our computational approach. However, in vivo functional assays based on the recruitment of β-arrestin to CXCR4 do not show significant agonism and antagonism at a single compound concentration. The present computational pipeline brings a new perspective to computer-aided drug design by combining conformational dynamics based on network analysis and cosolvent analysis based on the SILCS technology to identify putative allosteric binding sites using CXCR4 as a showcase.

Insights into Transfer of Supramolecular Doxorubicin/Congo Red Aggregates through Phospholipid Membranes

Doxorubicin (DOX) is a commonly used chemotherapeutic drug, from the anthracycline class, which is genotoxic to neoplastic cells via a DNA intercalation mechanism. It is effective and universal; however, it also causes numerous side effects. The most serious of them are cardiotoxicity and a decrease in the number of myeloid cells. For this reason, targeted DOX delivery systems are desirable, since they would allow lowering the drug dose and therefore limiting systemic side effects. Recently, synthetic dyes, in particular Congo red (CR), have been proposed as possible DOX carriers. CR is a planar molecule, built of a central biphenyl moiety and two substituted naphthalene rings, connected with diazo bonds. In water, it forms elongated ribbon-shaped supramolecular structures, which are able to selectively interact with immune complexes. In our previous studies, we have shown that CR aggregates can intercalate DOX molecules. In this way, they preclude DOX precipitation in water solutions and increase its uptake by MCF7 breast cancer cells. In the present work, we further explore the interactions between DOX, CR, and their aggregates (CR/DOX) with phospholipid membranes. In addition to neutral molecules, the protonated doxorubicin form, DXP, is also studied. Molecular dynamics simulations are employed to study the transfer of CR, DOX, DXP, and their aggregates through POPC bilayers. Interactions of CR, DOX, and CR/DOX with model monolayers are studied with Langmuir trough measurements. This study shows that CR may support the transfer of doxorubicin molecules into the bilayer. Both electrostatic and van der Waals interactions with lipids are important in this respect. The former promote the initial stages of the insertion process, the latter keep guest molecules inside the bilayer.

Drug screening in human physiologic medium identifies uric acid as an inhibitor of rigosertib efficacy

The nonphysiological nutrient levels found in traditional culture media have been shown to affect numerous aspects of cancer cell physiology, including how cells respond to certain therapeutic agents. Here, we comprehensively evaluated how physiological nutrient levels affect therapeutic response by performing drug screening in human plasma-like medium. We observed dramatic nutrient-dependent changes in sensitivity to a variety of FDA-approved and clinically trialed compounds, including rigosertib, an experimental cancer therapeutic that recently failed in phase III clinical trials. Mechanistically, we found that the ability of rigosertib to destabilize microtubules is strongly inhibited by the purine metabolism end product uric acid, which is uniquely abundant in humans relative to traditional in vitro and in vivo cancer models. These results demonstrate the broad and dramatic effects nutrient levels can have on drug response and how incorporation of human-specific physiological nutrient medium might help identify compounds whose efficacy could be influenced in humans.

Comparative Retention Analysis of Intercalated Cations Inside the Interlayer Gallery of Lamellar and Nonlamellar Graphene Oxide Membranes in Reverse Osmosis Process: A Molecular Dynamics Study

Over the past decade, multilayered graphene oxide (GO) membranes have emerged as promising candidates for desalination applications. Despite their potential, a comprehensive understanding of separation mechanisms remains elusive due to the intricate morphology and structural arrangement of interlayer galleries. Moreover, a critical concern of multilayered GO membranes is their susceptibility to swelling within aqueous environments, which hinders their practical implementation. Therefore, this study introduces cation intercalation within GO laminates to elucidate the underlying factors governing swelling behavior and subsequently mitigate it. Moreover, this study performed nonequilibrium molecular dynamics simulations on the cation (Mg2+ or K+)-intercalated lamellar and nonlamellar GO membranes to understand the effect of the arrangement of GO sheets on the retention time of intercalated cations within GO layers, water permeance, and salt rejection mechanism in the reverse osmosis process using cation-intercalated GO membranes. Our results highlight that lamellar GO membranes exhibit higher water permeance, attributed to their well-defined interlayer gallery structure. On the other hand, nonlamellar GO membranes display superior salt rejection due to their complex interlayer gallery structure that impedes salt permeation. Moreover, the structural complexity of nonlamellar GO membranes contributes to greater stability by retention of the more intercalated cations for a longer time within the layers. Furthermore, it is observed that a higher percentage of Mg2+ cations remained inside the GO laminates as compared to K+ cations, hence resulting in the greater stability of the Mg2+-intercalated GO membrane in the aqueous environment.