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

Analysis of raltegravir analogs to enhance inhibitory efficiency against HIV integrase

This article addresses the improvement of the efficacy of anti-integrase enzyme drugs for the AIDS virus, especially using the drug Raltegravir and its 21 analogs. In this research, Hartree-Fock and Density Functional Theory methods have been employed for the design and optimization of new drug candidates. These methods are used to enhance the accuracy and reactivity of the drugs. Additionally, docking is used to investigate the interactions between the drug and the target and evaluate binding energies. Molecular dynamics simulation is utilized to validate binding results. Computational results indicate that the designed analogs exhibit higher reactivity. In molecular docking calculations, RAL5 and RAL21 show the best binding energies of -10.10 and - 10.92 kcal/mol, respectively, indicating their superior efficiency. The analysis of inhibitor potentials against the HIV-1 integrase enzyme through molecular dynamics simulation reveals that RAL5 has strong inhibitory potential for treating viral diseases. These findings contribute to the promotion of therapeutic intervention methods in this field.

High-Throughput Ligand Dissociation Kinetics Predictions Using Site Identification by Ligand Competitive Saturation

The dissociation or off rate, koff, of a drug molecule has been shown to be more relevant to efficacy than affinity for selected systems, motivating the development of predictive computational methodologies. These are largely based on enhanced-sampling molecular dynamics (MD) simulations that come at a high computational cost limiting their utility for drug design where a large number of ligands need to be evaluated. To overcome this, presented is a combined physics- and machine learning (ML)-based approach that uses the physics-based site identification by ligand competitive saturation (SILCS) method to enumerate potential ligand dissociation pathways and calculate ligand dissociation free-energy profiles along those pathways. The calculated free-energy profiles along with molecular properties are used as features to train ML models, including tree and neural network approaches, to predict koff values. The protocol is developed and validated using 329 ligands for 13 proteins showing robustness of the ML workflow built upon the SILCS physics-based free-energy profiles. The resulting SILCS-Kinetics workflow offers a highly efficient method to study ligand dissociation kinetics, providing a powerful tool to facilitate drug design including the ability to generate quantitative estimates of atomic and functional groups contributions to ligand dissociation.

Corrosion Protection of Mild Steel in Acidic Environments by Cetrimonium Cinnamates in Solution and Added to a Low Volatile Organic Compound Coating

Protection of mild steel from acidic solutions used in the industry by environmentally friendly methods is an area of need. This work explores the anticorrosive properties of cetrimonium cinnamate compounds for mild steel in acid solutions as an additive in solution and as a pigment in a low-volatility organic compound (VOC) coating. Immersion tests show that protection is considerably enhanced after 24 h, at pH 1 i corr for the control being 330 μA/cm2 compared to 4.3 μA/cm2 for CTA-MeOcinn, suggesting synergy between the cetrimonium cation and cinnamate anion systems. NMR and cryo-transmission electron microscopy (cryo-TEM) suggested entrapment of the cinnamate within the cetrimonium micelles. This is further supported by molecular dynamics (MD) simulations, which also show that the carboxylate groups on the cinnamate protrude from the cetrimonium micelles, enhancing the attachment to the surface. The inhibitors are incorporated into waterborne polymeric coatings and tested in solutions at pH 1. Electrochemical impedance spectroscopy (EIS) data show that the inhibitors form a protective barrier, significantly increasing pore and charge transfer resistances for the coating, thus demonstrating the use of safe methods to protect mild steel in acidic conditions.

PyPE_RESP: A Tool to Facilitate and Standardize Derivation of RESP Charges

We introduce PyPE_RESP, a tool to facilitate and standardize partial atomic charge derivation using the Restrained Electrostatic Potential (RESP) approach. PyPE_RESP builds upon the open-source Python package RDKit for chemoinformatics and the AMBER suite for molecular simulations. PyPE_RESP provides an easy setup of multiconformer and multimolecule RESP fitting while allowing a comprehensive definition of charge constraint groups through multiple methods. As a command line tool, PyPE_RESP can be integrated into batch processes. The software enables the derivation of partial atomic charges for additive and polarizable force fields. It outputs constraint group and nonconstraint group charges to give an immediate overview of the fit result. PyPE_RESP will be distributed with AmberTools and compatible with most computing platforms.

Structure-based identification of herbacetin and caffeic acid phenethyl ester as inhibitors of S-adenosylmethionine-dependent viral methyltransferase

Chikungunya (CHIKV) and dengue (DENV) viruses pose a public health risk and lack antiviral treatments. Structure-based molecular docking of a natural MTase substrates library identified herbacetin (HC) and caffeic acid phenethyl ester (CAPE) as potential CHIKV nsP1 and DENV NS5 MTase inhibitors. Binding affinities and MTase inhibition were confirmed using purified proteins. The crystal structure of DENV 3 NS5 MTase and CAPE complex revealed CAPE binding at viral RNA capping sites. Interestingly, HC and CAPE depleted polyamines crucial for RNA virus replication and decreased viral titer with IC50 values of ~ 13.44 and ~ 0.57 μm against CHIKV, and ~ 7.24 and ~ 1.01 μm against DENV 3, respectively. Polyamine addition did not reverse the antiviral effects, suggesting a dual inhibition mechanism. Impact statement This study reveals the antiviral potential of natural small molecules, Herbacetin (HC) and Caffeic acid phenethyl ester (CAPE) against Dengue and Chikungunya viruses. The molecules deplete polyamine levels and directly inhibit viral methyltransferases. This study opens new avenues for developing antiviral strategies that target both host factors and viral components.

In Silico Approach to Design of New Multi-Targeted Inhibitors Based on Quinoline Ring with Potential Anticancer Properties

Searching for new anticancer drugs is a significant challenge for the medical community due to the current limitations of existing treatments. The primary objective of this study was to design and optimize multi-targeted drug candidates based on a quinoline scaffold. In this paper, we adopt various in silico techniques, including molecular docking, molecular dynamics simulations, and ADMET property modeling, to predict the binding affinity and interactions of 7-ethyl-10-hydroxycamptothecin derivatives with multiple biological targets. The interactions of these compounds with three potential molecular targets, topoisomerase I, bromodomain-containing protein 4, and ATP-binding cassette sub-family G member 2 proteins, were analyzed. It has been previously proved that the inhibition of these molecular targets may have beneficial effects on cancer treatment. The designed chemical compounds can effectively interact with selected proteins, thereby establishing their potential as drug candidates. Molecular docking revealed promising binding affinities, with topoisomerase I docking scores ranging from -9.0 to -10.3 kcal/mol, BRD4 scores from -6.6 to -8.0 kcal/mol, and ABCG2 scores from -8.0 to -10.0 kcal/mol. Furthermore, the ADMET property analysis indicates promising pharmacological profiles, protein binding affinity, selectivity, and bioavailability while minimizing toxicity. For example, satisfactory logP values have been demonstrated in the favorable range for bioavailability after oral administration. Additionally, several compounds exhibited predicted aqueous solubility values greater than -3, suggesting moderate-to-good solubility, which is crucial for oral drug delivery.

Revealing thermophysical and mechanical responses of graphene-reinforced polyvinyl alcohol nanocomposites using molecular dynamics simulations

The effects of graphene (G) nanofiller content on enhancing the mechanical and thermal resistance of the polyvinyl alcohol (PVA) matrix are disentangled by performing all-atom classical molecular dynamics (MD) simulations. The crux of the computational work is to assess several key performance-limiting factors of the functional hybrid material, including the strain rate, temperature, and the size and distribution of the graphene nanofiller. Adding graphene nanofiller to the polymer results in more compact polymer chains, with the most significant impact observed in the 2% graphene composite. Uniaxial compression MD simulations revealed that the yield strength of the material is impacted by the proportion of nanofiller present. Specifically, the calculated stress-strain responses at a strain rate of 1.5 × 108 s-1 show that incorporating 2% graphene nanofiller remarkably enhances the yield strength. Conversely, increasing the graphene content to 5-10% led to a reduction in yield stress, which is primarily attributed to the disruption of hydrogen bond networks and destabilization of non-covalent interactions. Further analysis shows that increasing the strain rate led to higher yield stress in the G-PVA composite, while elevated temperatures caused its yield stress to decrease. Additionally, the glass transition temperature of the PVA composite rises with the graphene content and strongly correlates with the polymer chain mobility. The proposed theoretical approach may serve as a quantitative framework for elucidating the crucial role of interfacial interaction between polymers and nanomaterials in modulating the conformational, thermodynamic, and macroscopic properties of the hybrid materials.

Machine learning assisted in Silico discovery and optimization of small molecule inhibitors targeting the Nipah virus glycoprotein

The Nipah virus (NiV), a lethal pathogen from the Paramyxoviridae family, presents a significant global health threat as a result of its high mortality rate and inter-human transmission. This investigation employed in silico methods that were assisted by machine learning to identify small-molecule inhibitors that target the NiV glycoprotein, a critical component of viral entry. Out of the 754 antiviral compounds that were screened using Lipinski's Rule of Five and DeepPurpose, 333 are identified. Five best hits were identified through molecular docking, each of which exhibited superior binding scores in comparison to the control. This was further refined to three compounds through density functional theory (DFT) analysis, with compound 138,567,123 exhibiting the highest electronic stability (DFT energy: -1976.74 Hartree; HOMO-LUMO gap: 0.83 eV). Its stability was verified by molecular dynamics (MD) simulations, which demonstrated consistent hydrogen bonding and minimal RMSD. Additionally, it possessed the highest docking score (-9.7 kcal/mol) and binding free energy (-24.04 kcal/mol, MM/GBSA). The results underscore ligand 138,567,123 as a promising antiviral candidate for NiV and illustrate the efficacy of machine learning-based in silico drug discovery.

Lipid-GPCR interactions in an asymmetric plasma membrane model

We report simulations and analysis of the A2A adenosine receptor in its fully active state, in two different membrane environments. The first is a model in which the lipids are distributed asymmetrically according to recent lipidomics, simulations, and biophysical measurements, which together establish the distribution of lipids and cholesterol between the two leaflets. The second is the symmetrized version, which captures the membrane state following loss of lipid asymmetry. By comparing lipid-protein interactions between these two cases we show that solvation by phosphatidyl serine (PS) is insensitive to the loss of asymmetry-an abundance of positively charged sidechains around the cytoplasmic side of the receptor enriches solvation by PS in both membrane states. Cholesterol interactions are sensitive to the loss of asymmetry, with the abundance of cholesterol in the exoplasmic leaflet driving long-lived cholesterol interactions in the asymmetric state. However, one cholesterol interaction site on helix 6 is observed in both cases, and was also observed in earlier work with different membrane models, supporting its identification as a bona fide cholesterol binding site.

Assessing the Effectiveness of Neural Networks and Molecular Dynamics Simulations in Predicting Viscosity of Small Organic Molecules

Viscosity is a crucial material property that influences a wide range of applications, including three-dimensional (3D) printing, lubricants, and solvents. However, experimental approaches to measuring viscosity face challenges such as handling multiple samples, high costs, and limited compound availability. To address these limitations, we have developed computational models for viscosity prediction of small organic molecules, utilizing machine learning (ML) and nonequilibrium molecular dynamics (NEMD) simulations. Our ML framework, which includes feed-forward neural networks (FNN) and physics-informed neural networks (PINN), is based on the largest data set of small molecule viscosities compiled from the literature. The PINN model, in particular, incorporates temperature dependence through a four-parameter model, allowing for the direct prediction of continuous temperature-dependent viscosity curves. The ML models demonstrate exceptional prediction accuracy for the viscosity of various organic compounds across a wide range of temperatures. External validation of our models further confirms that the ML prediction models outperform the NEMD approach in predicting viscosity across a diverse range of organic molecules and temperatures. This highlights the potential of ML models to overcome limitations in traditional MD simulations, which often struggle with accuracy for specific molecules or temperature ranges. Our further feature importance analysis revealed a strong correlation between molecular structure and viscosity. We emphasize the key role of substructures in determining viscosity, offering deeper molecular insights for material design with tailored viscosity.

Strong protection by bazedoxifene against chemically-induced ferroptotic neuronal death in vitro and in vivo

Ferroptosis, a form of regulated cell death associated with glutathione depletion and excess lipid peroxidation, can be induced in cultured cells by chemicals (e.g., erastin and RSL3). It has been shown that protein disulfide isomerase (PDI) is a mediator of chemically-induced ferroptosis and also a crucial target for ferroptosis protection. The present study reports that bazedoxifene (BAZ), a selective estrogen receptor modulator, is an inhibitor of PDI and can strongly rescue neuronal cells from chemically-induced oxidative ferroptosis. We find that BAZ can directly bind to PDI and inhibit its catalytic activity. Computational modeling analysis reveals that BAZ forms a hydrogen bond with PDI's His256 residue. Inhibition of PDI by BAZ markedly reduces iNOS and nNOS dimerization (i.e., catalytic activation) and NO accumulation, and these effects of BAZ are associated with reductions in cellular ROS and lipid-ROS and protection against chemically-induced ferroptosis. In addition, the direct antioxidant activity of BAZ may also partially contribute to its protection against chemically-induced ferroptosis. In vivo animal experiments show that mice treated with BAZ are strongly protected against kainic acid-induced oxidative hippocampal neuronal injury and memory deficits. Together, these results reveal that BAZ is a potent inhibitor of PDI and can strongly protect against chemically-induced ferroptosis in hippocampal neurons both in vitro and in vivo. This work provides evidence for an estrogen receptor-independent, PDI-mediated novel mechanism of neuroprotection by BAZ.

Computational structure-guided approach to simulate delamanid and pretomanid binding to mycobacterial F420 redox cycling proteins: identification of key determinants of resistance

The recently approved delamanid (DLM) and pretomanid (PTM) improved the existing options to treat multidrug-resistant tuberculosis (MDR-TB). However, the high spontaneous mutation rates in mycobacterial F420 genes ddn, fgd1, fbiA, fbiB, fbiC, and fbiD create a bottleneck to successful anti-TB treatments. Of known mutations, identifying the therapeutically relevant ones is a prerequisite for understanding the drug resistance mechanism. Here, we applied a multistep computational pipeline to rank the mutations in F420 genes associated with DLM/PTM resistance. The DLM-/PTM-resistant protein mutants were built and simulated their innate sensitivity towards the drugs. The molecular dynamics (MD) and molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) calculations quantified the effect of key mutations on drug union. The dynamic cross-correlated map (DCCM) and principal component analysis (PCA) showed a substantial link between the drug binding region and other sections in the mutants, hints to their potential role as an allosteric site. Also, the alterations induced conformationally unstable proteins with decreased DLM/PTM affinity. These investigations highlighted the DLM-tolerant G53D and Y65S and PTM-resilient Y133M (Ddn), L308P (FbiA), and C562W (FbiC) as candidate loss-of-function mutants of progressive research. The present results and interpretations could supply vital clues for protein engineering and drug development.

Neutral Imidazole Lipid Analogues Exhibit Improved Properties for Artificial Model Biomembranes

In recent years, a variety of lipid-mimetic imidazolium salts have been developed and applied to investigate biological membranes and related processes. Despite their overall similar properties to natural lipids, there are potential drawbacks including cytotoxicity attributed to the cationic charge. Herein, we report the investigation of a novel class of electronically neutral imidazole-based lipids. In comparison to their positively charged congeners, they show improved biophysical properties and higher similarity to native lipids. By employing calorimetry, fluorescence spectroscopies, and fluorescence and atomic force microscopy, we examined changes in the thermotropic phase behavior, lipid order parameter, fluidity, and lateral membrane organization upon incorporation of the lipid mimetics. Depending on the characteristic of the lipid chains, charge of the headgroup, and substitution pattern, we observed changes in lipid order and fluidity, thus allowing modulation and fine-tuning of the physicochemical properties of the modified membrane. Notably, a newly synthesized imidazole-based cholesterol showed membrane properties very similar to natural cholesterol. Extensive computational studies indicate effective mimicking of cholesterol and reveal its capability to participate in raft formation. This new class of neutral imidazole lipid analogues is expected to lead to better molecular probes and tools.

Chemoinformatics exploration of synthetically accessible N-heterocycles: uncovering new antifungal lead candidates

Fungal infections caused by Candida albicans pose a significant global health challenge due to their high morbidity, mortality, and the growing prevalence of drug resistance. The failure of existing antifungal agents against resistant strains underscores the urgent need for novel therapeutic alternatives. In response to this challenge, we have created an in-house library of biologically relevant nitrogenous heterocycles to screen against the resistant C. albicans dihydrofolate reductase (DHFR), with the aim of identifying potential antifungal leads. Using computational tools such as molecular docking and dynamics simulations, we identified two promising leads based on isoquinoline scaffold. The stability of these leads was further assessed using quantum chemical descriptor calculations. Screening results indicate that these isoquinoline-based compounds could serve as potential antifungal candidates, offering a foundation for the development of new therapies to combat resistant C. albicans infections. Further experimental studies, including animal model testing, are necessary to validate and confirm our findings.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-025-00359-9.

A fast and efficient virtual screening and identification strategy for helix peptide binders based on finDr webserver: A case study of bovine serum albumin (BSA)

Peptides offer unique advantages, including strong specificity, rapid action, and low side effects, making them a prominent focus in the development of new drugs and functional materials. However, the rapid and efficient screening and identification of high-affinity peptides for specific targets remains a significant challenge. In this study, we successfully screened 12-helix candidate peptides using bovine serum albumin (BSA) as the target protein, employing the computer-aided peptide virtual screening webserver finDr. Among the top five candidate peptides, we identified E4-TP2 (GVATVVARLFLL) as the peptide capable of binding BSA with high affinity constant (KD = 39.4 nM), confirmed through an in vitro molecular interaction instrument. The interaction mode of the peptide-BSA complex was analyzed using Ligplot software, revealing that the primary interactions involved hydrophobic forces and hydrogen bonds. Additionally, molecular dynamics simulations further elucidated the molecular mechanisms underlying the high-affinity peptide interactions, the results demonstrated that the complex exhibited good conformational stability and strong binding free energy (MM/PBSA: -21.075 ± 5.471 kJ/mol). In conclusion, the finDr virtual screening strategy and the molecular interaction identification method employed in this study provide a robust technical approach for the rapid and efficient acquisition of high-affinity binding peptides for target proteins of interest.

Discovery of new dual butyrylcholinesterase (BuChE) inhibitors and 5-HT7 receptor antagonists as compounds used to treat Alzheimer's disease symptoms

Alzheimer's disease is a neurodegenerative condition with no effective cure, and current therapies, like donepezil, only alleviate symptoms. Research has explored cholinesterase inhibitors and strategies targeting tau protein, often combining inhibitors with 5-HT receptor antagonists, particularly 5-HT6. However, dual-action BuChE inhibitors and 5-HT7 antagonists have not been studied until now. This study evaluated such compounds in an animal model, focusing on two candidates: compound 18 (BuChE IC50 = 4.75 μM; 5-HT7Ki = 7 nM) and compound 50 (BuChE IC50 = 2.53 μM; 5-HT7Ki = 1 nM). Compound 50 showed robust cognitive improvements, enhancing memory consolidation and acquisition, particularly in reversing scopolamine-induced deficits. In contrast, compound 18 exhibited limited or dose-dependent efficacy, potentially limiting its applicability. These findings highlight the strong potential of compound 50 for cognitive enhancement therapies and suggest it warrants further investigation.

Identification of acetylcholinesterase inhibitors and stability analysis of THC@HP-β-CD inclusion complex: A comprehensive computational study

Complexing medications with cyclodextrins can enhance their solubility and stability. In this study, we investigated the host-guest complexation between Tetrahydrocurcumin (THC) and Hydroxypropyl-β-Cyclodextrin (HP-β-CD) using density functional theory (DFT) at the B3LYP-D3/TPZ level of theory in two possible orientations. To determine the reactive sites in both complexes for electrophilic and nucleophilic attacks, we calculated and interpreted the binding energy, HOMO and LUMO orbitals, global chemical reactivity descriptors, natural bond orbital (NBO) analysis, and Fukui indices. The results indicate that Orientation A is energetically more favorable than Orientation B. Non-covalent interactions (NCI) were analyzed using reduced density gradient (RDG) approaches, providing detailed insights into host-guest interactions, including hydrogen bonding and van der Waals forces. To further assess stability, we conducted 1000 ns molecular dynamics (MD) simulations and analyzed the root mean square deviations (RMSD) for systems containing 1, 2, and 10 complexes. The RMSD analysis confirmed the stability of the systems, with average RMSD values of 2.01, 3.21, and 4.29 Å, respectively. In the second part of this study, we examined the interaction between THC and the target protein Acetylcholinesterase (E.C. 3.1.1.7) with PDB ID 1QTI. Molecular docking was performed to identify the binding modes and interaction energies of the THC-protein complex. Subsequently, 1000 ns MD simulations were conducted to assess the stability and dynamic behavior of the THC-protein complex over an extended period. The analysis provided valuable insights into the binding interactions and stability of THC with the target protein, further confirming its potential as a therapeutic agent.

Photophysical and structural aspects of poly-L-tryptophan: π-π stacking interaction with an excited state intermolecular proton transfer probe 3-Hydroxynaphthoic acid revealed by experiments and molecular simulation

In biophysical studies involving proteins, the involvement of the intrinsic fluorophore Tryptophan and its energy transfer/binding interactions are already well-investigated areas. Theoretical studies have also been well corroborated with experimental findings. However, in polymeric Tryptophans (specifically homopolymers), several queries still need to be addressed - their structure, the environment of each Tryptophan and the binding preferences of the latter. This necessitated some detailed investigations on the poly-L-Tryptophan system both from experimental and theoretical standpoints. In this work, we have carried out both steady-state and time-resolved fluorescence studies along with low-temperature phosphorescence (LTP) of poly-L-tryptophan, and the nature of the emitting Tryptophan (Trp) residue in the latter has been characterized based on a comparison with the emission features of the parent monomer. The very large red-shift of the (0-0) band of phosphorescence in poly-L-Tryptophan has been explained through triplet-triplet energy transfer along with the structure of the latter which has been developed by theoretical modelling. The nature of the environment of the emitting Trp residue in poly-L-Trp has been compared with several multi-Tryptophan proteins where different Trp residues exhibit optically resolved (0-0) bands. The interaction of the excited state proton transfer (ESIPT) probe 3-hydroxynaphthoic acid (3-HNA) with poly-L-Trp has also been investigated in detail using fluorescence, LTP, and classical molecular dynamics simulations.

The coevolutionary landscape of drug resistance in epidermal growth factor receptor: A cancer perspective

Epidermal growth factor receptor (EGFR), the first receptor tyrosine kinase, plays a critical role in neoplastic metastasis, angiogenesis, tumor invasion, and apoptosis, making it a prime target for treating non-small cell lung cancer (NSCLC). Although tyrosine kinase inhibitors (TKIs) have shown high efficacy and promise for cancer patients, resistance to these drugs often develops within a year due to alterations. The present study investigates the compensatory alterations in EGFR to understand the evolutionary process behind drug resistance. Our findings reveal that coevolutionary alterations expand the drug-binding pocket; leading to reduced drug efficacy and suggested that such changes significantly influence the structural adaptation of the EGFR against these drugs. Analysis such as root mean square deviation (RMSD), root mean square fluctuation (RMSF), solvent accessible surface area (SASA), principal component analysis (PCA), and free energy landscape (FEL) demonstrated that structures of wild EGFR docked with gefitinib are more stable which suggests its susceptibility towards drug than coevolution dependent double mutant. The findings were supported by MM-GBSA binding affinity analysis. The insights from this study highlighted the evolution-induced structural changes which contributes to drug resistance in EGFR and may certainly aid in designing more effective drugs.

Alkyl Tail Variation on Chalcone-Based Quaternary Pyridinium Salts as Rule-of-Thumb for Antimicrobial Activity

Aiming at developing a new class of quaternary pyridinium salts, the lead compound 1, characterized by a pyridine-3-yl chalcone framework, was rationally modified by inserting alkyl functions varying from 6 to 18 carbon units. Among the set, some valuable lead compounds were identified. Derivatives 4-6 were primarily active against Staphylococcus aureus and Candida albicans, respectively (MIC = 1.56 and 3.125 μM). In comparison, analogs 4 and 5 showed significant activities against Escherichia coli (MIC = 6.25 μM). Interestingly, the antimicrobial property of compounds 4-6, as well as their antibiofilm activity, occurred at lower concentrations than their cyto- and erythrocyte toxicities, thus ensuring a favorable safety profile. Structure-activity relationship analysis highlighted the critical role of the alkyl tail length in the antimicrobial activity, and optimal results were observed for moieties ranging from 10 to 14 carbon units. Molecular dynamics studies performed on 2 and 5 by modeling them on Gram-positive and Gram-negative membranes showed that the derivatives, upon diffusing across periodic boundary conditions, were able to intercalate into the microbial membranes. The difference in diffusion rates provides useful information to support the diverse antimicrobial potencies of the newly designed quaternary pyridinium salt.

Transport of phenoxyacetic acid herbicides by PIN-FORMED auxin transporters

Auxins are a group of phytohormones that control plant growth and development. Their crucial role in plant physiology has inspired development of potent synthetic auxins that can be used as herbicides. Phenoxyacetic acid derivatives are a widely used group of auxin herbicides in agriculture and research. Despite their prevalence, the identity of the transporters required for distribution of these herbicides in plants is both poorly understood and the subject of controversial debate. Here we show that PIN-FORMED auxin transporters transport a range of phenoxyacetic acid herbicides across the membrane. We go on to characterize the molecular determinants of substrate specificity using a variety of different substrates as well as protein mutagenesis to probe the binding site. Finally, we present cryogenic electron microscopy structures of Arabidopsis thaliana PIN8 bound to either 2,4-dichlorophenoxyacetic acid or 4-chlorophenoxyacetic acid. These structures represent five key states from the transport cycle, allowing us to describe conformational changes associated with the transport cycle. Overall, our results reveal that phenoxyacetic acid herbicides use the same export machinery as endogenous auxins and exemplify how transporter binding sites undergo transformations that dictate substrate specificity. These results provide a foundation for future development of novel synthetic auxins and for precision breeding of herbicide-resistant crop plants.

Unveiling an indole derivative YM818 as a novel tyrosinase inhibitor with anti-melanogenic and anti-melanin transfer effects

Indole and its derivatives, heterocyclic compounds with broad therapeutic potential, have seen limited study in melanogenesis. Here, our virtual screening identified 15 indole derivatives that potentially interacted with tyrosinase (TYR), a key enzyme in melanogenesis. Nine of the 15 indole derivatives tested significantly decreased tyrosinase activity, and 3-hydroxy-5-bromo-(3-indolyl)-2‑carbonyl indole (designated as YM818) exhibited highest inhibitory rate at 74.28 % with IC50 of 0.372 mmol/L. Surface plasmon resonance and fluorescence quenching assays demonstrated the direct interaction between YM818 and TYR with KD value 94.84 ± 45.27 μmol/L. YM818 treatment reduced cellular melanin content to 35.8 %. Furthermore, YM818 treatment enhanced AKT protein phosphorylation, leading to the downregulation of melanogenesis-related proteins, including MITF, TYR and TRP1. In vivo zebrafish studies confirmed the inhibitory effects of YM818 on melanogenesis. Additionally, YM818 disrupted melanin transfer by suppressing the expression of protease-activated receptor-2 (PAR-2) gene, a G protein-coupled receptor that plays a crucial role in mediating cellular responses to serine proteases, including keratinocyte phagocytosis and melanin transfer. YM818 also exhibited robust antioxidant activity, with 2,2'-Azino-bis(3-ethylbenzothiazoline-6-sulfonic acid (ABTS) and 2,2-Diphenyl-1-picrylhydrazyl (DPPH) radical scavenging IC50 values comparable to vitamin C and significantly reducing intracellular ROS levels in a dose-dependent manner. Taken together, these findings highlight YM818 as a promising anti-melanogenic agent, offering valuable insights into the development of novel anti-melanin drugs and tyrosinase inhibitors.

Predicting the structures of cyclic peptides containing unnatural amino acids by HighFold2

Cyclic peptides containing unnatural amino acids possess many excellent properties and have become promising candidates in drug discovery. Therefore, accurately predicting the 3D structures of cyclic peptides containing unnatural residues will significantly advance the development of cyclic peptide-based therapeutics. Although deep learning-based structural prediction models have made tremendous progress, these models still cannot predict the structures of cyclic peptides containing unnatural amino acids. To address this gap, we introduce a novel model, HighFold2, built upon the AlphaFold-Multimer framework. HighFold2 first extends the pre-defined rigid groups and their initial atomic coordinates from natural amino acids to unnatural amino acids, thus enabling structural prediction for these residues. Then, it incorporates an additional neural network to characterize the atom-level features of peptides, allowing for multi-scale modeling of peptide molecules while enabling the distinction between various unnatural amino acids. Besides, HighFold2 constructs a relative position encoding matrix for cyclic peptides based on different cyclization constraints. Except for training using spatial structures with unnatural amino acids, HighFold2 also parameterizes the unnatural amino acids to relax the predicted structure by energy minimization for clash elimination. Extensive empirical experiments demonstrate that HighFold2 can accurately predict the 3D structures of cyclic peptide monomers containing unnatural amino acids and their complexes with proteins, with the median RMSD for Cα reaching 1.891 Å. All these results indicate the effectiveness of HighFold2, representing a significant advancement in cyclic peptide-based drug discovery.

Inhibitory potential of furanocoumarins against cyclin dependent kinase 4 using integrated docking, molecular dynamics and ONIOM methods

Cyclin Dependent Kinase 4 (CDK4) is vital in the process of cell-cycle and serves as a G1 phase checkpoint in cell division. Selective antagonists of CDK4 which are in use as clinical chemotherapeutics cause various side-effects in patients. Furanocoumarins induce anti-cancerous effects in a range of human tumours. Therefore, targeting these compounds against CDK4 is anticipated to enhance therapeutic effectiveness. This work intended to explore the CDK4 inhibitory potential of 50 furanocoumarin molecules, using a comprehensive approach that integrates the processes of docking, drug-likeness, pharmacokinetic analysis, molecular dynamics simulations and ONIOM (Our own N-layered Integrated molecular Orbital and Molecular mechanics) methods. The top five best docked compounds obtained from docking studies were screened for subsequent analysis. The molecules displayed good pharmacokinetic properties and no toxicity. Epoxybergamottin, dihydroxybergamottin and notopterol were found to inhabit the ATP-binding zone of CDK4 with substantial stability and negative binding free energy forming hydrogen bonds with key catalytic residues of the protein. Notopterol exhibiting the highest binding energy was subjected to ONIOM calculations wherein the hydrogen bonding interactions were retained with significant negative interaction energy. Hence, through these series of computerised methods, notopterol was screened as a potent CDK4 inhibitor and can act as a starting point in successive processes of drug design.

Molecular basis of neurosteroid and anticonvulsant regulation of TRPM3

Transient receptor potential channel subfamily M member 3 (TRPM3) is a Ca2+-permeable cation channel activated by the neurosteroid pregnenolone sulfate (PregS) or heat, serving as a nociceptor in the peripheral sensory system. Recent discoveries of autosomal dominant neurodevelopmental disorders caused by gain-of-function mutations in TRPM3 highlight its role in the central nervous system. Notably, the TRPM3 inhibitor primidone, an anticonvulsant, has proven effective in treating patients with TRPM3-linked neurological disorders and in mouse models of thermal nociception. However, our understanding of neurosteroids, inhibitors and disease mutations on TRPM3 is limited. Here we present cryogenic electron microscopy structures of the mouse TRPM3 in complex with cholesteryl hemisuccinate, primidone and PregS with the synthetic agonist CIM 0216. Our studies identify the binding sites for the neurosteroid, synthetic agonist and inhibitor and offer insights into their effects and disease mutations on TRPM3 gating, aiding future drug development.

In silico cooling rate dependent crystallization and glass transition in n-alkanes

n-Alkanes (CnH2n+2) are linear chain compounds spanning length-scales from small molecules to polymers. Intermediate length alkanes (say, n = 10-20) have attracted much interest as organic phase change materials (PCM) for storing energy as latent heat. The cooling rate (γ) determines both the latent heat and temperature of crystallization. While slow cooling of the liquid leads to the crystalline state, rapid cooling leads to a glassy state (glass transition temperature Tg). Albeit scant theoretical investigations concerning the vitrification processes, the role of molecular conformations therein remains completely unexplored. Our work presents an all-atom molecular dynamics study of (a) cooling intermediate length alkanes (n = 12 and 16) at seven different rates, and (b) rapidly cooling 14 n-alkanes (4 ≤ n ≤ 50) for determining Tg(n). We find that for linear molecules the end-to-end distance (Ree) is of special relevance: the crystal is composed solely of fully stretched molecules (maximal Ree). Hence one may define the "degree of crystallization" as the area under the maximal Ree peak in the Ree distribution. Other peaks in the distribution represent conformations that existed in the supercooled liquid just before vitrification. A peak for the shortest, hairpin rotamer appears only for n ≥ n0 = 18, and is also manifested in the minimum of Rg/Ree(n) for liquid n-alkanes. The dependence of Tg on n is represented as two intersecting Ueberreiter and Kanig equations, intersecting near n0 = 18. Extrapolation gives the asymptotic n → ∞ limit of Tg, T∞g = 250 K, which is probably its most accurate estimate obtained theoretically todate.

Multimodal evaluation of lipoxygenase-targeting NSAIDs using integrated in vitro, SAR, in silico, cytotoxicity towards MCF-7 cell line, DNA docking and MD simulation approaches

Lipoxygenase (LOX) and cyclooxygenase (COX) pathways generate biologically active mediators implicated in inflammatory disorders and several classes of cancer. Non-steroidal anti-inflammatory drugs (NSAIDs) inhibit the COX pathway by inhibiting the COX-1 and COX-2 enzymes. We reported earlier that several NSAIDs, including naproxen, aspirin and acetaminophen, inhibited lipoxygenase (LOX) enzyme at sub-micromolar concentrations. In continuation, the present work demonstrates the anti-LOX activity of nine more NSAIDs supported by in vitro, in silico, MD simulation and breast cancer cell line studies. All tested drugs displayed potent to excellent inhibitory profiles with IC50 values <24.93 ± 0.64 μM. Aceclofenac (IC50 0.85 ± 0.06 μM) was the most active drug, followed by indomethacin (IC50 1.13 ± 0.07 μM), meloxicam (IC50 1.94 ± 0.07 μM) and ketorolac (IC50 9.26 ± 0.82 μM). Celecoxib (IC50 15.81 ± 0.71 μM), lornoxicam (IC50 16.54 ± 0.28 μM) and nimesulide (IC50 19.87 ± 0.85 μM) showed excellent inhibitory profiles. Flurbiprofen (IC50 21.73 ± 0.93 μM) and etoricoxib (IC50 24.93 ± 0.64 μM) moderately inhibited the target enzyme. SAR studies revealed that active molecules decorated with the carboxylate group afforded strong binding interactions as observed by in vitro assays and structural features. Other drugs, including enol derivatives and celecoxib, also showcased enhanced binding interactions. However, the cytotoxic effects of NSAIDs against the MCF-7 breast cancer cell line did not disclose significant anticancer activity. Molecular docking studies against human 5-LOX offered the best binding affinities for aceclofenac (-13.54 kcal/mol), accompanied by conventional hydrogen bonding and hydrophobic interactions as supported by the in vitro results. Docking studies with DNA dodecamer established minor groove binding with their possible role in DNA replication and gene expression. Density functional theory (DFT) and ESP studies, MD simulations and MMPBSA free energy calculations further reiterated the stability of ligand-receptor complexes. Overall, these findings highlight the potential of targeted NSAIDs as dual COX/LOX inhibitors with broader therapeutic relevance in inflammatory disorders.

Structural insights into terminal arabinosylation of mycobacterial cell wall arabinan

The global challenge of tuberculosis, caused by Mycobacterium tuberculosis (Mtb), is compounded by the emergence of drug-resistant strains. A critical factor in Mtb's pathogenicity is its intricate cell envelope, which acts as a formidable barrier against immune defences and pharmacological interventions. Central to this envelope are arabinogalactan (AG) and lipoarabinomannan (LAM), two complex polysaccharides containing arabinan domains essential for maintaining cell wall structure and function. The arabinofuranosyltransferase AftB plays a pivotal role in the biosynthesis of these arabinan domains by catalyzing the addition of β-(1 → 2)-linked terminal arabinofuranose residues. Here, we present the cryo-EM structures of Mycobacterium chubuense AftB in both its apo form and bound to a donor substrate analog, resolved at 2.9 Å and 3.4 Å resolution, respectively. These structures reveal that AftB has a GT-C fold, with a transmembrane (TM) domain comprised of eleven TM helices and a periplasmic cap domain. AftB has a distinctive irregular, tube-shaped cavity that connects two proposed substrate binding sites. Through an integrated approach combining structural analysis, biochemical assays, and molecular dynamics simulations, we delineate the molecular basis of AftB's reaction mechanism and propose a model for its catalytic function.

Cholesterol allosteric modulation of the oxytocin receptor

G-protein coupled receptors are critical components in cellular signaling, mediating various physiological responses to external stimuli. Here, we investigate the intricate relationship between cholesterol and the oxytocin receptor (OXTR), focusing on the binding mechanisms and the allosteric cross talk of bound cholesterol to the orthosteric ligand binding pocket. Utilizing molecular docking and molecular dynamics simulations, we identify cholesterol binding sites both on the agonist-bound and antagonist-bound state, which show differing distributions and residence times of the cholesterol molecules. Importantly, both methods converge on several key sites, demonstrating strong predictive overlap. Notably, one such site, and several sites detected by our molecular dynamics approach, also coincide with electron density observed in an experimental cryo-EM map, providing orthogonal validation for computational predictions. Allosteric network analysis uncovers the distinct pathways through which cholesterol may affect ligand-mediated receptor signaling, highlighting the significance of one site on the extracellular leaflet between TM4 and TM5, and two sites on the intracellular leaflet between TM2, TM3, and TM4 and between TM4 and TM5 in transmitting allosteric signals to the orthosteric pocket. These findings provide insights into the impact of cholesterol on OXTR function, emphasizing specific binding sites and signaling paths for further experimental exploration.

Deciphering the Regulatory Potential of Antioxidant and Electron-Shuttling Bioactive Compounds in Oolong Tea

OT has gained attention for its high polyphenol content and therapeutic potential. To elucidate this further, this study investigated the electron-shuttling bioactive compounds of OT and evaluated their effect on dysregulated breast cancer (BC) genes. OT extracts were obtained via solvent extraction (SE) and supercritical fluid extraction (SFE), followed by in vitro assays. Phytochemical analysis revealed that ethanol-extracted OT (OTL-E) had the highest polyphenol, flavonoid, and tannin contents, correlating with strong antioxidant activity, while water-extracted OT (OTL-W) exhibited greater bioelectricity-stimulating properties in microbial fuel cells (MFC), confirmed by cyclic voltammetry (CV). Based on phytochemical analyses, SE displayed a better extraction technique for isolating OT bioactive compounds compared to SFE. In silico approaches through network pharmacology, molecular docking and dynamics simulations revealed that polyphenols with ortho- or para-dihydroxyl groups targeted dysregulated BC proteins involved in kinase signaling, apoptosis, and hormone receptor pathways. Luteolin exhibited the highest binding affinities to MAPK1 and PIK3CA with free energy (ΔG) of -9.1 and -8.4 kcal/mol, respectively. Trajectory-based analyses confirmed enthalpy-favored ligand-induced conformational changes to these oncoproteins, altering their function in BC development. These findings suggest the potential of OT as a bioelectricity-stimulating and chemopreventive agent, warranting further in vitro and in vivo validation.

Computational approach for the evaluation of sesquiterpene lactone as a modulator of cannabinoid receptor type 2 for neurodegenerative disease prophylactics

Neurodegenerative diseases represent a major global health challenge, with cannabinoid receptor type 2 (CB2) emerging as a promising therapeutic target for its role in inflammation modulation and neuroprotection. Sesquiterpene lactone is a class of natural compounds with diverse molecular structures and known biological activities. This study aimed to explore sesquiterpene lactones for their potential as CB2 modulators using computational approaches such as molecular docking, molecular dynamics simulations (MDS), and ADMET predictions, to identify the promising candidates for neurodegenerative disease prophylactics. Out of 85 sesquiterpene lactones evaluated, podachaenin (PubChem CID: 15,828,229) exhibited the highest binding affinity to CB2 (- 12.242 kcal/mol), outperforming that of the native ligand (- 12.168 kcal/mol) and reference drugs apomorphine (- 9.482 kcal/mol), dantrolene (- 8.861 kcal/mol), and galantamine (- 9.689 kcal/mol). Hydrogen bonds as well as alkyl, Pi-alkyl, and van der Waal's interactions were present in the CB2-podachaenin complex providing structural intactness. MDS of 500 ns evaluated the stability of the protein-ligand complex and receptor structure in apo form through geometrical parameters: root mean square deviation, root mean square fluctuation, radius of gyration, solvent accessible surface area, and hydrogen bond length. Additionally, the binding free energy change calculation supplemented the initial inferences in terms of thermodynamic stability with a value of - 40.92 ± 4.56 kcal/mol. ADMET profiling also indicated favorable pharmacokinetic and pharmacodynamic properties, similar to that of the reference drugs. The preliminary results identified podachaenin as a possible CB2 modulator for treating neurodegenerative diseases and could be a hit compound in neuro-drug design. Further in vivo and in vitro studies are suggested to validate it as a hit candidate.

Self-Assembled Multilayered Concentric Supraparticle Architecture

Supraparticles (SPs) with unique properties are emerging as versatile platforms for applications in catalysis, photonics, and medicine. However, the synthesis of novel SPs with complex internal structures remains a challenge. Self-Assembled Multilayered Supraparticles (SAMS) presented here are composed of concentric lamellar spherical structures made from metallic nanoparticles, formed from a synergistic three-way interaction phenomenon between gold nanoparticles, lipidoid, and gelatin, exhibiting interlayer spacing of 3.5  ± 0.2 nm within a self-limited 156.8  ± 56.6 nm diameter. The formation is critically influenced by both physical (including nanoparticle size, lipidoid chain length) and chemical factors (including elemental composition, nanoparticle cap, and organic material), which collectively modulate the surface chemistry and hydrophobicity, affecting interparticle interactions. SAMS can efficiently deliver labile payloads such as siRNA, achieving dose-dependent silencing in vivo, while also showing potential for complex payloads such as mRNA. This work not only advances the field of SP design by introducing a new structure and interaction phenomenon but also demonstrates its potential in nanomedicine.

Atomistic Modeling of Cross-Linking in Epoxy-Amine Resins: An Open-Source Protocol

Atomistic modeling has become an extensively used method for studying thermosetting polymers, particularly in the analysis and development of high-performance composite materials. Despite extensive research on the topic, a widely accepted, standardized, flexible, and open-source approach for simulating the cross-linking process from precursor molecules has yet to be established. This study proposes, tests, and validates a Molecular Dynamics (MD) protocol to simulate the cross-linking process of epoxy resins. We developed an in-house code based on Python and LAMMPS, enabling the generation of epoxy resin structures with high degrees of cross-linking. In our work, the epoxy network is dynamically formed within the MD simulations, modeling the chemical bonding process with constraints based on the distance between the reactive sites. To validate our model against experimental data from the literature, we then computed the density, thermal conductivity, and elastic response. The results show that the produced structures align well with experimental evidence, validating our method and confirming its feasibility for further analyses and in silico experiments. Beyond the case study presented in this work, focusing on bisphenol A diglycidyl ether (DGEBA) epoxy resin and diethylenetriamine (DETA) as curing agents in a 5:2 ratio, our approach can be easily adapted to investigate different epoxy resins.

The Binding of Brazilin from C. sappan to the Full-Length SARS-CoV-2 Spike Proteins

The emergence of coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has become a global issue since 2019. The prominent characteristic of SARS-CoV-2 is the presence of the spike (S) protein protruding from the virus particle envelope. The S protein is a major drug and vaccine target because it initiates the key step in infection. Medicinal herbs are a potential treatment option to enhance immunity to fight viral infections. Caesalpinia sappan L. has been reported to display promising anti-viral activities. Specifically, brazilin (BRA), a major bioactive compound in C. sappan, was reported to play a role in inhibiting viral infection. Thus, the ability of BRA as a COVID-19 treatment was tested. The S protein was used as the BRA target of this work. Understanding the binding mechanism of BRA to the S protein is crucial for future utilisation of C. sappan as a COVID-19 treatment or other coronavirus-caused pandemics. Here, we performed molecular docking of BRA onto the S protein receptor binding domain (RBD) and multimerisation (MM) pockets. Molecular dynamics (MD) simulations were conducted to study the stability of binding to glycosylated and non-glycosylated S protein constructs. BRA can bind to the Receptor-binding motif (RBM) on an RBD surface stably; however, it is too large to fit into the MM pocket, resulting in dissociation. Nonetheless, BRA is bound by residues near the S1/S2 interface. We found that glycosylation has no effect on BRA binding, as the proposed binding site is far from any glycans. Our results thus indicate that C. sappan may act as a promising preventive and therapeutic alternative for COVID-19 treatment.

Single-particle adsorption of ultra-small gold nanoparticles at the biomembrane phase boundary

Nanomaterials are revolutionizing biomedical research by enabling the development of novel therapies, with applications ranging from drug delivery and diagnostics to the modulation of specific biological processes. Current research focuses on tasks such as enhancing cellular uptake of materials while preserving their functionality. However, the mechanisms governing interactions between nanomaterials and biological systems-particularly cellular membranes-remain challenging to elucidate due to the complex, dynamic nature of the lipid bilayer environment. This complexity arises from factors such as coexisting lipid domains (conserved regions of lipids) or lipid rafts, as well as cellular behaviors that induce state changes. The heterogeneous membrane landscape may offer unique adsorption properties and other functional effects, making it crucial to understand these interactions for greater biological control in nanotherapeutics. In this work, we systematically expose a phase-separated phospholipid-supported lipid bilayer (SLB)-specifically, a fluid-gel DOPC:DPPC bilayer-to low concentrations of citrate-capped 5 nm gold nanoparticles (AuNPs) to observe the adsorption process of individual AuNPs at the molecular scale. Using atomic force microscopy (AFM), we experimentally detect the adsorption of some AuNPs at the phase boundary. Complementary molecular dynamics (MD) simulations further elucidate the mechanism of single AuNP adsorption at lipid phase boundaries. Our findings indicate that the AuNP preferentially incorporates into the fluid-phase DOPC lipids while maintaining partial association with the gel-phase DPPC lipids due to diffusion effects. During adsorption, the AuNP disrupts lipid organization by increasing lateral lipid mixing across the phase boundary. This disruption to lipid molecular ordering is further evident upon AuNP incorporation into the bilayer. The ability to modulate the spatial organization and structure of lipid molecules has significant implications for therapeutics that leverage lipid diffusion pathways for alternative drug delivery mechanisms or to induce specific lipid behaviors.

ART-SM: Boosting Fragment-Based Backmapping by Machine Learning

In sequential multiscale molecular dynamics simulations, which advantageously combine the increased sampling and dynamics at coarse-grained resolution with the higher accuracy of atomistic simulations, the resolution is altered over time. While coarse-graining is straightforward once the mapping between atomistic and coarse-grained resolution is defined, reintroducing the atomistic details is still a nontrivial process called backmapping. Here, we present ART-SM, a fragment-based backmapping framework that learns from atomistic simulation data to seamlessly switch from coarse-grained to atomistic resolution. ART-SM requires minimal user input and goes beyond state-of-the-art fragment-based approaches by selecting from multiple conformations per fragment via machine learning to simultaneously reflect the coarse-grained structure and the Boltzmann distribution. Additionally, we introduce a novel refinement step to connect individual fragments by optimizing specific bonds, angles, and dihedral angles in the backmapping process. We demonstrate that our algorithm accurately restores the atomistic bond length, angle, and dihedral angle distributions for various small and linear molecules from Martini coarse-grained beads and that the resulting high-resolution structures are representative of the input coarse-grained conformations. Moreover, the reconstruction of the TIP3P water model is fast and robust, and we demonstrate that ART-SM can be applied to larger linear molecules as well. To illustrate the efficiency of the local and autoregressive approach of ART-SM, we simulated a large realistic system containing the surfactants TAPB and SDS in solution using the Martini3 force field. The self-assembled micelles of various shapes were backmapped with ART-SM after training on only short atomistic simulations of a single water-solvated SDS or TAPB molecule. Together, these results indicate the potential for the method to be extended to more complex molecules such as lipids, proteins, macromolecules, and materials in the future.

Systematic Investigation of CYP3A4 Using Side-by-Side Comparisons of Apo, Active Site, and Allosteric-Bound States

Cytochrome P450 (CYP) 3A4 (CYP3A4) is a complex enzyme that metabolizes diverse substrates. It contains a large binding site accommodating diverse ligands, binding to active or allosteric sites. CYP3A4 does not always follow Michaelis-Menten kinetics. While Km reflects substrate affinity, it does not necessarily determine the enzyme's activity, though it is often considered indicative of substrate binding characteristics. The mechanism may be highly sophisticated and driven by multiple factors. This suggests that the ligand binding affinity alone may not explain the differential behavior of the enzyme conformational stability. Here, we analyzed sequence conserveness of 57 CYPs, followed by a detailed molecular dynamics simulation study (9 μs) on CYP3A4. We studied three CYP3A4 enzyme states (apo-state, active-site, and allosteric-site ligand-bound states) collected from the same experimental setup to reduce the systematic error. We found that the enzyme conformational stability followed a consistent trend of allosteric > active > apo states, which was inconsistent with the enzyme-ligand (active/allosteric) binding affinity and the ligand conformational stability. However, the heme group showed a significant protein affinity and stability pattern directly related to the enzyme stability, suggesting that the active/allosteric binding may work by influencing the heme-CYP3A4 binding affinity, and the allosteric ligand appeared to form the most stable enzyme state of the three studied states.

Bacopa monnieri phytochemicals as promising BACE1 inhibitors for Alzheimer's disease therapy

Alzheimer's disease (AD) remains a formidable challenge, necessitating the discovery of effective therapeutic agents targeting β-site amyloid precursor protein cleaving enzyme 1 (BACE1). This study investigates the inhibitory potential of phytochemicals derived from Bacopa monnieri, a plant renowned for its cognitive-enhancing properties, in comparison to established synthetic inhibitors such as Atabecestat, Lanabecestat, and Verubecestat. Utilizing molecular docking and advanced computational simulations, we demonstrate that Bacopaside I exhibits superior binding affinity and a unique interaction profile with BACE1, suggesting a more nuanced inhibitory mechanism. Our findings highlight the promising role of Bacopa monnieri phytochemicals as viable alternatives to synthetic drugs, emphasizing their potential to overcome limitations faced in clinical settings. Furthermore, the development of the SIMANA ( https://simana.streamlit.app/ ) platform enhances the visualization and analysis of protein-ligand interactions, facilitating a deeper understanding of the dynamics involved. This research not only underscores the therapeutic promise of natural compounds in AD treatment but also advocates for a paradigm shift towards integrating traditional medicinal knowledge into contemporary drug discovery efforts.

Small-Molecule FICD Inhibitors Suppress Endogenous and Pathologic FICD-Mediated Protein AMPylation

The AMP transferase, FICD, is an emerging drug target fine-tuning stress signaling in the endoplasmic reticulum (ER). FICD is a bifunctional enzyme, catalyzing both AMP addition (AMPylation) and removal (deAMPylation) from the ER-resident chaperone BiP/GRP78. Despite increasing evidence linking excessive BiP/GRP78 AMPylation to human diseases, small molecules that inhibit pathogenic FICD variants are lacking. Using an in vitro high-throughput screen, we identify two small-molecule FICD inhibitors, C22 and C73. Both molecules significantly inhibit FICD-mediated BiP/GRP78 AMPylation in intact cells while only weakly inhibiting BiP/GRP78 deAMPylation. C22 and C73 also inhibit pathogenic FICD variants and improve proinsulin processing in β cells. Our study identifies and validates FICD inhibitors, highlighting a novel therapeutic avenue against pathologic protein AMPylation.

Kinetic implications of IP6 anion binding on the molecular switch of HIV-1 capsid assembly

HIV-1 capsid (CA) proteins self-assemble into a fullerene-shaped CA, enabling cellular transport and nuclear entry of the viral genome. A structural switch comprising the Thr-Val-Gly- Gly (TVGG) motif either assumes a disordered coil or a 310 helix conformation to regulate hexamer or pentamer assembly, respectively. The cellular polyanion inositol hexakisphosphate (IP6) binds to a positively charged pore of CA capsomers rich in arginine and lysine residues mediated by electrostatic interactions. Both IP6 binding and TVGG coil-to-helix transition are essential for pentamer formation. However, the connection between IP6 binding and TVGG conformational switch remains unclear. Using extensive atomistic simulations, we show that IP6 imparts structural order at the central ring, which results in multiple kinetically controlled events leading to the coil-to-helix conformational change of the TVGG motif. IP6 facilitates the helix-to-coil transition by allowing the formation of intermediate conformations. Our results suggest a key kinetic role of IP6 in HIV-1 pentamer formation.

Structural dynamics of olfactory receptors: implications for odorant binding and activation mechanisms

Olfaction, an ancient and intricate process, profoundly shapes human innate responses yet remains relatively understudied compared to other sensory modalities. Olfactory receptors (ORs), members of the G protein-coupled receptor (GPCR) family, play a pivotal role in detecting and discriminating a vast array of odorants. This comprehensive study explores the functional roles of five diverse ORs: OR1A1, OR2W1, OR11A1, OR51E1 and OR51E2, through detailed investigations into the differences between their apo and odorant-bound forms. By examining key residues and mutations, the possible molecular mechanisms that underlie the modulation of binding landscapes and the consequent alterations in OR stability were elucidated. The findings revealed dynamic conformational changes in ORs upon odorant binding, characterized by hinging motions and tilting of transmembrane helices. Using residue interaction network analyses, critical residues involved in mediating interactions between ORs and odorants were uncovered, shedding light on the molecular determinants of olfactory perception. By examining changes in binding pocket volume and per-residue energy decomposition, the dynamic nature of OR activation and the influence of mutations on receptor stability and functionality was observed.

Discovery and Characterization of Novel Receptor-Interacting Protein Kinase 1 Inhibitors Using Deep Learning and Virtual Screening

Receptor-interacting protein kinase 1 (RIPK1) serves as a critical mediator of cell necroptosis and represents a promising therapeutic target for various human neurodegenerative diseases and inflammatory diseases. Nonetheless, the RIPK1 inhibitors currently reported are inadequate for clinical research due to suboptimal inhibitory activities or lack of selectivity. Consequently, there is a need for the discovery of novel RIPK1 kinase inhibitors. In this study, we integrated a deep learning model, specifically the fingerprint graph attention network (FP-GAT), with molecular docking-based virtual screening to identify potential RIPK1 inhibitors from a library comprising 13 million compounds. Out of 43 compounds procured, two compounds (designated as 24 and 41) demonstrated enzyme inhibition activity exceeding 50% at a concentration of 10 μM against RIPK1. The half-maximal inhibitory concentrations (IC50) for compounds 24 and 41 were determined to be 2.01 and 2.95 μM, respectively. Furthermore, these compounds exhibited protective effects in an HT-29 cell model of TSZ-induced necroptosis, with half-maximal effective concentrations (EC50) of 6.77 μM for compound 24 and 68.70 μM for compound 41. Finally, molecular dynamics simulations and binding free energy calculations were conducted to elucidate the molecular mechanism of compounds 24 and 41 binding to RIPK1. The results show that Met92, Met95, Ala155, and Asp156 are key residues for novel RIPK1 inhibitors. In summary, this work discovered two hit compounds targeting RIPK1, which can be further structurally modified to become promising lead compounds.

Impact of Ruxolitinib Interactions on JAK2 JH1 Domain Dynamics

Janus kinase 2 (JAK2) is an important intracellular mediator of cytokine signaling. Mutations in the JAK2 gene are associated with myeloproliferative neoplasms (MPNs) such as polycythemia vera (PV) and essential thrombocythemia (ET), while aberrant JAK2 activity is also associated with a number of immune diseases. The acquired somatic mutation JAK2 V617F (95% of cases of PV and in 55-60% of cases of ET), which constitutively activates the JAK2, is the most common molecular event in MPN. The development of specific JAK2 inhibitors is therefore of considerable clinical importance. Ruxolitinib is a JAK inhibitor recently approved by the FDA/EMA and effective in relieving symptoms in patients with MPN. Ruxolitinib binds to the JAK2 last domain, namely JH1; its action on the dynamics of the domain is still only partially known. Using Molecular Dynamics simulations, we have analyzed the JH1 domain in four different states as follows: (i) alone, (ii) with one phosphorylation, (iii) adding Ruxolitinib, and (iv) with five phosphorylations and Ruxolitinib. The ligand induces a dynamic behavior similar to the inactive form of JH1, with a less flexible state than the phosphorylated active form of JH1. This study highlights the inhibitory effect of Ruxolitinib on the JH1 domain, demonstrating the importance of dynamics in regulating JH1 activation.

Active Site Plasticity of the Bacterial Sliding Clamp

The rise of antibiotic resistance poses a severe global threat, specifically due to the emergence of multiresistant bacteria (ESKAPE pathogens), which are responsible for countless deaths globally. Consequently, the development of novel antibiotics is in dire need. Targeting proteins essential to DNA replication is a promising pathway, making the β-sliding clamp (β-SC) an attractive target. Currently, there are no antibiotics on the market that target the β-SC. However, numerous compounds are being investigated to create an antibiotic with high potency against a broad range of bacterial species. Interestingly, most proposed compounds do not bind to the entire active site, which may reduce their potential as high-potency inhibitors. This is due to the active site residue Met at position 362, adopting a "closed" conformation, preventing inhibitors access into Subsite II of the active site. This study explored the effect of key residues on the plasticity of the β-SC active site using molecular dynamics and metadynamics simulations under different physiological states. Our results show that the Met gate exhibits flexibility and both open and closed states are thermodynamically and kinetically accessible.

Effects of naphthoquinone scaffold-derived compounds on head and neck squamous cell carcinoma based on network pharmacology and molecular docking

OBJECTIVES

This study aimed to analyze the effects of naphthoquinone scaffold-derived compounds on head and neck squamous cell carcinoma (HNSCC) using network pharmacology and molecular docking.

METHODS

We screened candidate compounds from the ASINEX database and evaluated their drug likeness and toxicity. They identified 80 compounds with naphthalenone structures, focusing on 1,4-naphthoquinone and 1,2-naphthoquinone scaffolds. The possible targets of these compounds were predicted using databases like SwissTargetPrediction and Similarity Ensemble Approach Database (SEA).

RESULTS

The common targets between the compounds and HNSCC were identified, yielding 65 overlapping targets. A protein-protein interaction (PPI) network was constructed, and 20 hub genes were identified based on centrality metrics. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that these compounds' protective effects against HNSCC are associated with cancer-related pathways, such as those in cancer and proteoglycans in cancer. Molecular docking was performed to evaluate the binding affinity between the compounds and hub genes. The results showed that the compounds had strong binding affinities with key targets like MET and TYK2, with binding energies < -5 kcal/mol.

CONCLUSIONS

The study suggests that naphthoquinone derivatives could serve as novel chemotherapy agents for HNSCC, warranting further research for clinical application.

MGAT1-Guided complex N-Glycans on CD73 regulate immune evasion in triple-negative breast cancer

Despite the widespread application of immunotherapy, treating immune-cold tumors remains a significant challenge in cancer therapy. Using multiomic spatial analyses and experimental validation, we identify MGAT1, a glycosyltransferase, as a pivotal factor governing tumor immune response. Overexpression of MGAT1 leads to immune evasion due to aberrant elevation of CD73 membrane translocation, which suppresses CD8+ T cell function, especially in immune-cold triple-negative breast cancer (TNBC). Mechanistically, addition of N-acetylglucosamine to CD73 by MGAT1 enables the CD73 dimerization necessary for CD73 loading onto VAMP3, ensuring membrane fusion. We further show that THBS1 is an upstream etiological factor orchestrating the MGAT1-CD73-VAMP3-adenosine axis in suppressing CD8+ T cell antitumor activity. Spatial transcriptomic profiling reveals spatially resolved features of interacting malignant and immune cells pertaining to expression levels of MGAT1 and CD73. In preclinical models of TNBC, W-GTF01, an inhibitor specifically blocked the MGAT1-catalyzed CD73 glycosylation, sensitizing refractory tumors to anti-PD-L1 therapy via restoring capacity to elicit a CD8+ IFNγ-producing T cell response. Collectively, our findings uncover a strategy for targeting the immunosuppressive molecule CD73 by inhibiting MGAT1.

Protonation Effects in Protein-Ligand Complexes - A Case Study of Endothiapepsin and Pepstatin A with Computational and Experimental Methods

Protonation states serve as an essential molecular recognition motif for biological processes. Their correct consideration is key to successful drug design campaigns, since chemoinformatic tools usually deal with default protonation states of ligands and proteins and miss atypical protonation states. The protonation pattern for the Endothiapepsin/PepstatinA (EP/pepA) complex is investigated using different dry lab and wet lab techniques. ITC experiments revealed an uptake of more than one mole of protons upon pepA binding to EP. Since these experiments were performed at physiological conditions (and not at pH=4.6 at which a large variety of crystal structures is available), a novel crystal structure at pH=7.6 was determined. This crystal structure showed that only modest structural changes occur upon increasing the pH value. This lead to computational studies Poisson-Boltzmann calculations and constant pH MD simulation to reveal the exact location of the protonation event. Both computational studies could reveal a significant pKa shift resulting in non-default protonation state and that the catalytic dyad is responsible for the uptake of protons. This study shows that assessing protonation states for two separate systems (protein and ligand) might result in the incorrect assignment of protonation states and hence incorrect calculation of binding energy.

Understanding the structural basis of ALS mutations associated with resistance to sulfonylurea in wheat

Developing herbicide-tolerant wheat varieties is highly desirable for effective weed management and improved crop yield. The enzyme acetolactate synthase (ALS) is the target enzyme for the sulfonylurea class of herbicides. The structural analysis of mutable sites in ALS is crucial for the generation of herbicide-resistant crops. Previous studies indicated that mutant lines of Triticum aestivum ALS (TaALS) with amino acid substitutions at P174, G631, and G632 residues provided resistance to sulfonylurea herbicide, nicosulfuron. The present study aimed to provide structural insights into mutable residues causing sulfonylurea herbicide resistance to TaALS enzyme through in-silico molecular docking and simulation approaches. The molecular docking analysis suggested a single point mutation at TaALS-P174S, its double mutant conformations (TaALS-G632S/P174S and TaALS-G631D/G632S) and associated triple mutant conformation (TaALS-G631D/G632S/P174S) to have the lowest binding affinity with nicosulfuron than the wild-type conformation of TaALS. Furthermore, the molecular dynamic simulation study confirms the weakest and more stable binding of the triple mutant conformation with nicosulfuron. Our computational study identifies a triple mutant conformation (TaALS-G631D/G632S/P174S) to be more effective in developing sulfonylurea herbicide-resistant wheat crops.

Enhanced sampling simulations to explore himalayan phytochemicals as potential phosphodiesterase-1 inhibitor for neurological disorders

The rising incidence of neurological and neuropsychiatric disorders underscores the urgent need for innovative and evidence based treatment strategies. Phosphodiesterase-1 (PDE1) is a dual-substrate (cAMP/cGMP) phosphodiesterase expressed in the central nervous system and peripheral areas, which modulates cyclic nucleotide signaling cascades. Inhibiting PDE1 enhances cAMP/cGMP levels, promoting neuronal plasticity and neuroprotection, making it a promising therapeutic strategy for neurological disorders. The pursuit of targeting this enzyme for treating neurological and neuropsychiatric disorders has faced obstacles due to the absence of potent, selective, and brain-penetrating inhibitors. This study aimed to identify potent PDE1 inhibitors by leveraging a diverse collection of bioactive molecules derived from Himalayan flora through computational screening methods. The four most promising hit molecules were chosen for further investigation and subjected to Molecular Dynamics (MD) simulations, binding free energy calculations, along with standard molecules. It was found that the hit molecules stigmast-7, corilagin and emblicanin-A had formed the most stable complexes, and also, the least binding free energy was observed for stigmast-7 among the hit molecules. Additionally, the pulling simulations indicated that stigmast-7 and corilagin were the most robust binders, and required the highest force to dissociate from the binding cavity completely. The umbrella sampling simulations also revealed the lowest binding free energy for corilagin and stigmast-7. The insights gained from this study provide a foundation for future research into PDE1-targeted therapies, highlighting the potential of Himalayan bioactive compounds in developing novel therapeutic interventions.

Discovering cryptic pocket opening and binding of a stimulant derivative in a vestibular site of the 5-HT3A receptor

A diverse set of modulators, including stimulants and anesthetics, regulates ion channel function in our nervous system. However, structures of ligand-bound complexes can be difficult to capture by experimental methods, particularly when binding is dynamic. Here, we used computational methods and electrophysiology to identify a possible bound state of a modulatory stimulant derivative in a cryptic vestibular pocket of a mammalian serotonin-3 receptor. We first applied a molecular dynamics simulation-based goal-oriented adaptive sampling method to identify possible open-pocket conformations, followed by Boltzmann docking that combines traditional docking with Markov state modeling. Clustering and analysis of stability and accessibility of docked poses supported a preferred binding site; we further validated this site by mutagenesis and electrophysiology, suggesting a mechanism of potentiation by stabilizing intersubunit contacts. Given the pharmaceutical relevance of serotonin-3 receptors in emesis, psychiatric, and gastrointestinal diseases, characterizing relatively unexplored modulatory sites such as these could open valuable avenues to understanding conformational cycling and designing state-dependent drugs.