Molecular Modeling Techniques Research Articles

Cryo-electron Microscopy and Molecular Modeling Methods to Characterize the Dynamics of Tau Bound to Microtubules.

The electron microscopy metainference integrative structural biology method enables the combination of cryo-electron microscopy electron density maps with molecular modeling techniques such as molecular dynamics to unveil the atomistic biomolecular structural ensemble and the error in the map data in an efficient manner. Here we illustrate the electron microscopy metainference protocol and analysis used to elucidate the atomistic structural ensemble of the microtubule-associated protein tau bound to microtubules by using state-of-the-art molecular mechanic force field and the electron density map.

Exploration of interaction existing between methyl chavicol and bovine serum albumin using spectroscopic and molecular modelling techniques

Exploration of interaction existing between methyl chavicol and bovine serum albumin using spectroscopic and molecular modelling techniques

Screening of Phytochemicals for Antisickling effects

Background: Sickle cell disease is an autosomal recessive disorder caused due to point mutation on a single gene on chromosome 11 resulting in substitution of valine in place of glutamic acid at sixth position of beta-chain of adult hemoglobin (HbA). The purpose of the current study is to evaluate the effectiveness of phytochemicals (Rutin and its derivatives) for antisickling effects. Methods: The structure of Rutin is obtained in Simplified Molecular Input Line Entry System (SMILES) format and is subjected to chemical similarity search using Extended-connectivity fingerprints (ECFP4) fingerprints. The Tanimoto coefficient is used as the similarity metric and top compounds obtained on chemical similarity search are further subjected to molecular docking based screening analysis. A local database of phytochemicals is created using the IMPAAT database. Molecular docking approach is implemented using iGEMDOCK for screening and ranking of Rutin and its derivatives. The docking is implemented with standard docking protocol with population size of 200 and number of generations= 70. Results: All the ligand molecules showed acceptable binding energies with the 2HBS receptor molecule ranging between -124.87 to-79.59kcal/mol. Koprutin showed the highest binding affinity against the 2HBS (2HBS-DEOXYHEMOGLOBIN S) and which can be further developed as an antisickling agent. Conclusion: The integration of insilico modelling techniques with the wet lab based experimental methods can significantly reduce the cost in terms of time and other resources. Further the intricacies of molecular interactions taking place between ligands and receptors can be better understood with the help of molecular modelling techniques.

Molecular toxicity of nitrobenzene derivatives to tetrahymena pyriformis based on SMILES descriptors using Monte Carlo, docking, and MD simulations

It is challenging to model the toxicity of nitroaromatic compounds due to limited experimental data. Nitrobenzene derivatives are commonly used in industry and can lead to environmental contamination. Extensive research, including several QSPR studies, has been conducted to understand their toxicity. Predictive QSPR models can help improve chemical safety, but their limitations must be considered, and the molecular factors affecting toxicity should be carefully investigated. The latest QSPR methods, molecular modeling techniques, machine learning algorithms, and computational chemistry tools are essential for developing accurate and robust models. In this work, we used these methods to study a series of fifty compounds derived from nitrobenzene. The Monte Carlo approach was used for QSPR modeling by applying the SMILES molecular structure representation and optimal molecular descriptors. The correlation ideality index (CII) and correlation contradiction index (CCI) were further introduced as validation parameters to estimate the developed models' predictive ability. The statistical quality of the CII models was better than those without CII. The best QSPR model with the following statistical parameters (Split-3): (R2 = 0.968, CCC = 0.984, IIC = 0.861, CII = 0.979, Q2 = 0.954, QF12 = 0.946, QF22 = 0.938, QF32 = 0.947, Rm2 = 0.878, RMSE = 0.187, MAE = 0.151, FTraining = 390, FInvisible = 218, FCalibration = 240, RTest2 = 0.905) was selected to generate the studied promoters with increasing and decreasing activity.

Molecular structure, spectroscopy, molecular docking, and molecular dynamic studies of tetrahydroneoprzewaquinone as potent cervical cancer agent

Abstract Cervical cancer is one of the most prevalent cancer-related diseases, causing accelerated morbidity and mortality rates in low-income countries and African states. This study explores the potential of (3R,3′R)-2,2′,3,3′-tetrahydroneoprzewaquinone (TDN) as a treatment for cervical cancer by investigating its structural and molecular properties using molecular modelling technique, which include; DFT, molecular docking, molecular dynamic simulation. The results are promising, with TDN demonstrating exceptional stability in the energy gap (E g) as well as through natural bond order analysis (NBO). π → σ* electronic transitions were found to contribute mainly to the molecule’s stability, with an outstanding total stabilization energy (E (2)). Docking exercises showed that TDN binds more favorably to the pro-apoptotic receptor 4s0o with a stronger H-bond compared to the conventional DOX drug, which interacted less effectively with TDN and more strongly with the anti-apoptotic protein, forming an outstanding strong H-bond. Molecular dynamics simulations also revealed that TDNʼs interaction with the pro-apoptotic protein (TDN_4S0o) was more stable than the standard DOX drug (DOX_4s0o). The H-bond plot indicated that TDN could effectively interact with both anti and pro-apoptotic receptors, forming approximately 1 to 4 hydrogen bonds between TDN_1g5M with respect to each picosecond (ps) ranging from 0 to 1000 ps. In contrast, the number of hydrogen bonds fluctuated when DOX interacted with the anti-apoptotic protein (1g5M), ranging from 1 to 5 H-bonds. Overall, these results suggest that TDN may be a promising drug candidate for cervical cancer treatment.

Exploring the antiviral activity of α-ketoamides compounds through electronic structure calculations: a structure-activity relationship study

In recent years, the scientific community has worked intensively in the search and development of new drugs to suppress viral infections, such as COVID-19. In fact, a number of active compounds have been tested; however, the absence of significant structure-activity relationships hinders the production of optimized drugs. In this study, molecular modeling techniques were employed to investigate the electronic, structural and chemical reactivity properties of a set α-ketoamides whose antiviral activities have been reported in the literature, aiming to propose new promising derivatives. The local reactivity of the compounds was evaluated via condensed-to-atoms Fukui indexes and molecular electrostatic potential. Multivariate data analysis and random forests machine learning techniques were employed to correlate the antiviral properties and electronic and structural descriptors and identify relevant variables. A series of new derivatives were then proposed and evaluated via density functional theory-based calculations, and docking/molecular dynamics with the target protein of the virus. The results suggest that active derivatives present reduced reactivity towards electrophilic agents on the central core of the molecules and high reactivity on R1 ligands. Derivatives with higher predicted antiviral activities were proposed based on simple electronic descriptors, and their efficacies are reinforced by docking and molecular dynamics simulations. Communicated by Ramaswamy H. Sarma

HIV-1 Vif protein sequence variations in South African people living with HIV and their influence on Vif-APOBEC3G interaction

PurposeDespite extensive research, HIV-1 remains a global epidemic with variations in pathogenesis across regions and subtypes. The Viral Infectivity Factor (Vif) protein, which neutralizes the host protein APOBEC3G, has been implicated in differences in clinical outcomes among people living with HIV (PLHIV). Most studies on Vif sequence diversity have focused on subtype B, leaving gaps in understanding Vif variations in HIV-1C regions like South Africa. This study aimed to identify and compare Vif sequence diversity in a cohort of 51 South African PLHIV and other HIV-1C prevalent regions.MethodsSanger sequencing was used for Vif analysis in the cohort, and additional sequences were obtained from the Los Alamos database. Molecular modeling and docking techniques were employed to study the influence of subtype-specific variants on Vif-APOBEC3G binding affinity.ResultsThe findings showed distinct genetic variations between Vif sequences from India and Uganda, while South African sequences had wider distribution and closer relatedness to both. Specific amino acid substitutions in Vif were associated with geographic groups. Molecular modeling and docking analyses consistently identified specific residues (ARGR19, LYS26, TYR30, TYR44, and TRP79) as primary contributors to intermolecular contacts between Vif and APOBEC3G, essential for their interaction. The Indian Vif variant exhibited the highest predicted binding affinity to APOBEC3G among the studied groups.ConclusionsThese results provide insights into Vif sequence diversity in HIV-1C prevalent regions and shed light on differential pathogenesis observed in different geographical areas. The identified Vif amino acid residues warrant further investigation for their diagnostic, prognostic, and therapeutic potential.

Phase stability and nucleation kinetics of salts in confinement

Of fundamental importance to energy storage, electrochemistry and separation processes, ionic liquids in confinement exhibit unexpected thermodynamic behaviors. Such complex behaviors highlight the need for multi-scale descriptions of the thermodynamics and dynamics of nanoconfined ionic liquids. Here, we probe the impacts of confinement on the freezing point and nucleation kinetics of a simple molten salt which shares important features with ionic liquids. Using molecular modeling techniques, the melting temperature of salt in confinement is found to be larger than its bulk counterpart. Relying on conventional thermodynamic models (Clapeyron and Gibbs-Thomson equations), we capture the crystal/liquid coexistence in bulk and confinement with simple parameters derived from molecular simulations. Then, by considering different nucleation mechanisms – bulk, surface, and confined nucleation, the metastability barrier upon salt formation is shown to decrease upon confinement, therefore providing theoretical insight into the larger nucleation rate constants observed in experiments on nanoconfined ionic liquids. We also discuss the effects of wettability on capillary freezing of ionic liquids at various confining surfaces through varying the contact angle. These findings provide a practical means to predict the melting point, wettability, and freezing kinetics of ionic liquids at various surface conditions from simple thermodynamic data.

Immunoinformatics-Driven Strategies for Advancing Epitope-Based Vaccine Design for West Nile Virus

The West Nile virus (WNV) is the causative agent of West Nile disease (WND), which poses a potential risk of meningitis or encephalitis. The aim of the study was to design an epitope-based vaccine for WNV by utilizing computational analyses. The epitope-based vaccine design process encompassed WNV sequence collection, phylogenetic tree construction, and sequence alignment. Computational models identified B-cell and T-cell epitopes, followed by immunological property analysis. Epitopes were then modeled and docked with B-cell receptors, MHC I, and MHC II. Molecular dynamics simulations further explored dynamic interactions between epitopes and receptors. The findings indicated that the B-cell epitope QINHHWHKSGSSIG, along with three T-cell epitopes (FLVHREWFM for MHC I, NPFVSVATANAKVLI for MHC II, and NAYYVMTVGTKTFLV for MHC II), successfully passed the immunological evaluations. These four epitopes were further subjected to docking and molecular dynamics simulation studies. Although each demonstrated favorable affinities with their respective receptors, only NAYYVMTVGTKTFLV displayed a stable interaction with MHC II during MDS analysis, hence emerging as a potential candidate for a WNV epitope-based vaccine. This study demonstrates a comprehensive approach to epitope vaccine design, combining computational analyses, molecular modeling, and simulation techniques to identify potential vaccine candidates for WNV.

Mechanisms of Fibroblast Growth Factor 21 Adsorption on Macroion Layers: Molecular Dynamics Modeling and Kinetic Measurements.

Molecular dynamic modeling and various experimental techniques, including multi-angle dynamic light scattering (MADLS), streaming potential, optical waveguide light spectroscopy (OWLS), quartz crystal microbalance with dissipation (QCM), and atomic force microscopy (AFM), were applied to determine the basic physicochemical parameters of fibroblast growth factor 21 in electrolyte solutions. The protein size and shape, cross-section area, dependence of the nominal charge on pH, and isoelectric point of 5.3 were acquired. These data enabled the interpretation of the adsorption kinetics of FGF 21 on bare and macrocation-covered silica investigated by OWLS and QCM. It was confirmed that the protein molecules irreversibly adsorbed on the latter substrate, forming layers with controlled coverage up to 0.8 mg m-2, while their adsorption on bare silica was much smaller. The viability of two cell lines, CHO-K1 and L-929, on both bare and macrocation/FGF 21-covered substrates was also determined. It is postulated that the acquired results can serve as useful reference systems for designing complexes that can extend the half-life of FGF 21 in its active state.

Discovery of new oxadiazolo pyridine derivatives as potent ghrelin O-acyltransferase inhibitors using molecular modeling techniques.

METTL3-mediated HOTAIRM1 promotes vasculogenic mimicry in glioma via regulating IGFBP2 expression. METTL3 expression is high in glioma cells and tissues that stabilize and enhance HOTAIRM1 expression. This HOTAIRM1 then interacts with IGFBP2 which in turn promotes glioma cell malignancy and vasculogenic mimicry (VM) formation, thus providing a new direction for glioma therapy. The online version contains supplementary material available at 10.1007/s40203-023-00167-z.

Synergistic inhibition of glioblastoma multiforme through an in-silico analysis of luteolin and ferulic acid derived from Angelica sinensis and Cannabis sativa: Advancements in computational therapeutics.

The primary objective of this study is to uncover novel therapeutic agents for the treatment of Glioblastoma Multiforme (GBM), a highly aggressive form of brain cancer, and Alzheimer's Disease (AD). Given the complexity and resistance associated with both conditions, the study underscores the imperative need for therapeutic alternatives that can traverse the biological intricacies inherent in both neuro-oncological and neurodegenerative disorders. To achieve this, a meticulous, target-based virtual screening was employed on an ensemble of 50 flavonoids and polyphenol derivatives primarily derived from plant sources. The screening focused predominantly on molecular targets pertinent to GBM but also evaluated the potential overlap with neural pathways involved in AD. The study utilized molecular docking and Molecular Dynamic (MD) simulation techniques to analyze the interaction of these compounds with a key biological target, protein tyrosine phosphatase receptor-type Z (PTPRZ). Out of the 50 compounds examined, 10 met our stringent criteria for binding affinity and specificity. Subsequently, the highest value of binding energy was observed for the synergistic binding of luteolin and ferulic acid with the value of -10.5 kcal/mol. Both compounds exhibited inherent neuroprotective properties and demonstrated significant potential as pathway inhibitors in GBM as well as molecular modulators in AD. Drawing upon advanced in-silico cytotoxicity predictions and sophisticated molecular modeling techniques, this study casts a spotlight on the therapeutic capabilities of polyphenols against GBM. Furthermore, our findings suggest that leveraging these compounds could catalyze a much-needed paradigm shift towards more integrative therapeutic approaches that span the breadth of both neuro-oncology and neurodegenerative diseases. The identification of cross-therapeutic potential in flavonoids and polyphenols could drastically broaden the scope of treatment modalities against both fatal diseases.

Search for Novel Antagonist/S of HIF-1α from Selected Synthetic Analgesics/ Bioactive Flavonoids: An In-Silico Approach

Computer aided drug designing as well as drug repurposing implies the usage of molecular modelling techniques like analysis of the structures of receptor and ligand, molecular docking, pharmacokinetics and toxicity prediction, to explain the bioactivity of the synthetic molecules or plant secondary metabolites to design more efficient drug candidates or to repurpose an old drug in new diseases. Numerous studies have demonstrated that the low oxygen environment inside the cell is a key factor in developing breast cancer metastasis. To gain insight into the spread of breast cancer, hypoxia-inducible factor 1 (HIF-1), one of the master regulators of the hypoxic response, has been intensively explored. Our current research focuses on the insilico analysis and comparative study to evaluate the effects of different cancer drugs, analgesics, and plant-derived flavonoid compounds on HIF-1α regulation of breast cancer metastasis. According to the study, Quercetin shows the maximum binding affinity, i.e., -8.2 kcal/ mol. followed by Letrozole (-7.3 kcal/mol.), Naringenin (-7.11 kcal/mol), Tamoxifen (-7.07 kcal/mol), Phenacetin (-6.16 kcal/mol), and Aspirin (-5.7 kcal/mol). The study highlighted that Quercetin has the strongest binding affinity whereas Aspirin has the least binding affinity with HIF-1α protein. Hence the least toxic compound Quercetin can be a good candidate to control breast cancer metastasis by modulating the HIF-1 pathway.

Assessment of intermolecular interaction of mefenamic acid (Form II) in acetone and dimethylformamide (DMF) solution using molecular modelling technique

Solvent plays an important role in the solute-solvent intermolecular interactions of crystal morphology to regulate the crystal shape. Therefore, this work aims to assess the role of functional groups of solvents on the preferential sites on mefenamic acid (Form II) crystal surfaces, namely {001}, {011}, and {010} using the computational molecular modelling interactions techniques. The crystal morphology was successfully predicted as a plate-like crystal morphology using the BFDH method, and the molecular interactions of solutesolvent were assessed using the surface-docking method via Biovia Materials Studio software. The solute and solvent interactions along with surfaces used in this study disclosed that the {001} surfaces had the most negative non-bonded energy, followed by the {010} and {011} surfaces, ranging from −2036 to −2994 kcal/mol. Meanwhile, the binding energy values of acetone on all facets of interest were stronger compared to the binding energy of DMF, which possessed the binding energy of only less than −10 kcal/mol. Nevertheless, the results showed that acetone as a small molecule interacted most strongly with all facets of mefenamic acid (Form II) crystals because it could form stronger hydrogen bonds due to its ketone functional group, hence inhibited the growth of the mefenamic acid (Form II) crystal facets.

Atypical homodimerization revealed by the structure of the (S)-enantioselective haloalkane dehalogenase DmmarA from Mycobacterium marinum.

Haloalkane dehalogenases (HLDs) are a family of α/β-hydrolase fold enzymes that employ SN2 nucleophilic substitution to cleave the carbon-halogen bond in diverse chemical structures, the biological role of which is still poorly understood. Atomic-level knowledge of both the inner organization and supramolecular complexation of HLDs is thus crucial to understand their catalytic and noncatalytic functions. Here, crystallographic structures of the (S)-enantioselective haloalkane dehalogenase DmmarA from the waterborne pathogenic microbe Mycobacterium marinum were determined at 1.6 and 1.85 Å resolution. The structures show a canonical αβα-sandwich HLD fold with several unusual structural features. Mechanistically, the atypical composition of the proton-relay catalytic triad (aspartate-histidine-aspartate) and uncommon active-site pocket reveal the molecular specificities of a catalytic apparatus that exhibits a rare (S)-enantiopreference. Additionally, the structures reveal a previously unobserved mode of symmetric homodimerization, which is predominantly mediated through unusual L5-to-L5 loop interactions. This homodimeric association in solution is confirmed experimentally by data obtained from small-angle X-ray scattering. Utilizing the newly determined structures of DmmarA, molecular modelling techniques were employed to elucidate the underlying mechanism behind its uncommon enantioselectivity. The (S)-preference can be attributed to the presence of a distinct binding pocket and variance in the activation barrier for nucleophilic substitution.

Molecular Modeling Techniques and In-Silico Drug Discovery.

Molecular modeling is the technique to determine the overall structure of an unknown molecule, be it a small one or a macromolecule. The technique encompasses the method of screening ligand libraries for the development of new candidate drug molecules. All these aspects have become an essential topic of research. This field is truly interdisciplinary and finds its applications in almost all fields of life science research. In this chapter, an overview of the protocol associated with molecular modeling techniques will be discussed.

Combined Deep Learning and Molecular Modeling Techniques on the Virtual Screening of New mTOR Inhibitors from the Thai Mushroom Database.

The mammalian target of rapamycin (mTOR) is a protein kinase of the PI3K/Akt signaling pathway that regulates cell growth and division and is an attractive target for cancer therapy. Many reports on finding alternative mTOR inhibitors available in a database contain a mixture of active compound data with different mechanisms, which results in an increased complexity for training the machine learning models based on the chemical features of active compounds. In this study, a deep learning model supported by principal component analysis (PCA) and structural methods was used to search for an alternative mTOR inhibitor from mushrooms. The mTORC1 active compound data set from the PubChem database was first filtered for only the compounds resided near the first-generation inhibitors (rapalogs) within the first two PCA coordinates of chemical features. A deep learning model trained by the filtered data set captured the main characteristics of rapalogs and displayed the importance of steroid cores. After that, another layer of virtual screening by molecular docking calculations was performed on ternary complexes of FKBP12-FRB domains and six compound candidates with high "active" probability scores predicted by the deep learning models. Finally, all-atom molecular dynamics simulations and MMPBSA binding energy analysis were performed on two selected candidates in comparison to rapamycin, which confirmed the importance of ring groups and steroid cores for interaction networks. Trihydroxysterol from Lentinus polychrous Lev. was predicted as an interesting candidate due to the small but effective interaction network that facilitated FKBP12-FRB interactions and further stabilized the ternary complex.

Exploration of phenolic acid derivatives as inhibitors of SARS-CoV-2 main protease and receptor binding domain: potential candidates for anti-SARS-CoV-2 therapy.

Severe acute respiratory Syndrome-Coronavirus-2 (SARS-CoV-2) is the etiological virus of Coronavirus Disease 2019 (COVID-19) which has been a public health concern due to its high morbidity and high mortality. Hence, the search for drugs that incapacitate the virus via inhibition of vital proteins in its life cycle is ongoing due to the paucity of drugs in clinical use against the virus. Consequently, this study was aimed at evaluating the potentials of natural phenolics against the Main protease (Mpro) and the receptor binding domain (RBD) using molecular modeling techniques including molecular docking, molecular dynamics (MD) simulation, and density functional theory (DFT) calculations. To this end, thirty-five naturally occurring phenolics were identified and subjected to molecular docking simulation against the proteins. The results showed the compounds including rosmarinic acid, cynarine, and chlorogenic acid among many others possessed high binding affinities for both proteins as evident from their docking scores, with some possessing lower docking scores compared to the standard compound (Remdesivir). Further subjection of the hit compounds to drug-likeness, pharmacokinetics, and toxicity profiling revealed chlorogenic acid, rosmarinic acid, and chicoric acid as the compounds with desirable profiles and toxicity properties, while the study of their electronic properties via density functional theory calculations revealed rosmarinic acid as the most reactive and least stable among the sets of lead compounds that were identified in the study. Molecular dynamics simulation of the complexes formed after docking revealed the stability of the complexes. Ultimately, further experimental procedures are needed to validate the findings of this study.

Talarolide A and Talaropeptides A-D: Potential Marine-Derived Therapeutic Peptides with Interesting Chemistry and Biological Activity Studied through Density Functional Theory (DFT) and Conceptual DFT.

Molecules sourced from marine environments hold immense promise for the development of novel therapeutic drugs, owing to their distinctive chemical compositions and valuable medicinal attributes. Notably, Talarolide A and Talaropeptides A-D have gained recent attention as potential candidates for pharmaceutical applications. This study aims to explore the chemical reactivity of Talarolide A and Talaropeptides A-D through the application of molecular modeling and computational chemistry techniques, specifically employing Conceptual Density Functional Theory (CDFT). By investigating their chemical behaviors, the study seeks to contribute to the understanding of the potential pharmacological uses of these marine-derived compounds. The molecular geometry optimizations and frequency calculations were conducted using the Density Functional Tight Binding (DFTBA) method. This was followed by a subsequent round of geometry optimization, frequency analysis, and computation of electronic properties and chemical reactivity descriptors. We employed the MN12SX/Def2TZVP/H2O model chemistry, utilizing the Gaussian 16 program and the SMD solvation model. The analysis of the global reactivity descriptors arising from CDFT was achieved as well as the graphical comparison of the dual descriptor DD revealing the areas of the molecules with more propensity to suffer a nucleophilic or electrophilic attack. Additionally, Molinspiration and SwissTargetPrediction were considered for the calculation of molecular characteristics and predicted biological targets. These include enzymes, nuclear receptors, kinase inhibitors, GPCR ligands, and ion channel modulators. The graphical results show that Talarolide A and the Talaropeptides A-D are likely to behave as protease inhibitors.

A generic binding pocket for small molecule IKs activators at the extracellular inter-subunit interface of KCNQ1 and KCNE1 channel complexes

The cardiac IKs ion channel comprises KCNQ1, calmodulin, and KCNE1 in a dodecameric complex which provides a repolarizing current reserve at higher heart rates and protects from arrhythmia syndromes that cause fainting and sudden death. Pharmacological activators of IKs are therefore of interest both scientifically and therapeutically for treatment of IKs loss-of-function disorders. One group of chemical activators are only active in the presence of the accessory KCNE1 subunit and here we investigate this phenomenon using molecular modeling techniques and mutagenesis scanning in mammalian cells. A generalized activator binding pocket is formed extracellularly by KCNE1, the domain-swapped S1 helices of one KCNQ1 subunit and the pore/turret region made up of two other KCNQ1 subunits. A few residues, including K41, A44 and Y46 in KCNE1, W323 in the KCNQ1 pore, and Y148 in the KCNQ1 S1 domain, appear critical for the binding of structurally diverse molecules, but in addition, molecular modeling studies suggest that induced fit by structurally different molecules underlies the generalized nature of the binding pocket. Activation of IKs is enhanced by stabilization of the KCNQ1-S1/KCNE1/pore complex, which ultimately slows deactivation of the current, and promotes outward current summation at higher pulse rates. Our results provide a mechanistic explanation of enhanced IKs currents by these activator compounds and provide a map for future design of more potent therapeutically useful molecules.