Epithelial Sodium Channel Inhibition by Amiloride Addressed with THz Spectroscopy and Molecular Modeling.

THz spectroscopy is important for the study of ion channels because it directly addresses the low frequency collective motions relevant for their function. Here we used THz spectroscopy to investigate the inhibition of the epithelial sodium channel (ENaC) by its specific blocker, amiloride. Experiments were performed on A6 cells' suspensions, which are cells overexpressing ENaC derived from Xenopus laevis kidney. THz spectra were investigated with or without amiloride. When ENaC was inhibited by amiloride, a substantial increase in THz absorption was noticed. Molecular modeling methods were used to explain the observed spectroscopic differences. THz spectra were simulated using the structural models of ENaC and ENaC-amiloride complexes built here. The agreement between the experiment and the simulations allowed us to validate the structural models and to describe the amiloride dynamics inside the channel pore. The amiloride binding site validated using THz spectroscopy agrees with previous mutagenesis studies. Altogether, our results show that THz spectroscopy can be successfully used to discriminate between native and inhibited ENaC channels and to characterize the dynamics of channels in the presence of their specific antagonist.

Effects of Cd2+ on the epithelial Na+ channel (ENaC) investigated by experimental and modeling studies.

The function of the epithelial Na+ channel from the apical membrane of many Na+ transporting epithelia is modulated by various chemical compounds from the extracellular space, such as heavy metals, protons or chloride ions. We have studied the effect of extracellular Cd2+ on the function of the epithelial Na+ channel (ENaC) in heterologously expressed Xenopus laevis oocytes and Na+-transporting epithelia. We assayed channel function as the amiloride-sensitive sodium current (I(Na)). Cd2+ rapidly and voltage-independently inhibited INa in oocytes expressing αßγ Xenopus ENaC (xENaC). The extracellular Cd2+ inhibited Na+ transport and showed no influence on ENaC trafficking, as revealed by concomitant measurements of the transepithelial current, conductance and capacitance in Na+-transporting epithelia. Instead, amiloride inhibition was noticeably diminished in the presence of Cd2+ on the apical membrane. Using molecular modeling approaches, we describe the amiloride binding sites in rat and xENaC structures, and we present four putative binding sites for Cd2+. These results indicate that ENaC functions as a sensor for external Cd2+.

Chemical Structure-Biological Activity Models for Pharmacophores' 3D-Interactions.

Within medicinal chemistry nowadays, the so-called pharmaco-dynamics seeks for qualitative (for understanding) and quantitative (for predicting) mechanisms/models by which given chemical structure or series of congeners actively act on biological sites either by focused interaction/therapy or by diffuse/hazardous influence. To this aim, the present review exposes three of the fertile directions in approaching the biological activity by chemical structural causes: the special computing trace of the algebraic structure-activity relationship (SPECTRAL-SAR) offering the full analytical counterpart for multi-variate computational regression, the minimal topological difference (MTD) as the revived precursor for comparative molecular field analyses (CoMFA) and comparative molecular similarity indices analysis (CoMSIA); all of these methods and algorithms were presented, discussed and exemplified on relevant chemical medicinal systems as proton pump inhibitors belonging to the 4-indolyl,2-guanidinothiazole class of derivatives blocking the acid secretion from parietal cells in the stomach, the 1-[(2-hydroxyethoxy)-methyl]-6-(phenylthio)thymine congeners' (HEPT ligands) antiviral activity against Human Immunodeficiency Virus of first type (HIV-1) and new pharmacophores in treating severe genetic disorders (like depression and psychosis), respectively, all involving 3D pharmacophore interactions.

Structure-biological function relationship extended to mitotic arrest-deficient 2-like protein Mad2 native and mutants-new opportunity for genetic disorder control.

Overexpression of mitotic arrest-deficient proteins Mad1 and Mad2, two components of spindle assembly checkpoint, is a risk factor for chromosomal instability (CIN) and a trigger of many genetic disorders. Mad2 transition from inactive open (O-Mad2) to active closed (C-Mad2) conformations or Mad2 binding to specific partners (cell-division cycle protein 20 (Cdc20) or Mad1) were targets of previous pharmacogenomics studies. Here, Mad2 binding to Cdc20 and the interconversion rate from open to closed Mad2 were predicted and the molecular features with a critical contribution to these processes were determined by extending the quantitative structure-activity relationship (QSAR) method to large-size proteins such as Mad2. QSAR models were built based on available published data on 23 Mad2 mutants inducing CIN-related functional changes. The most relevant descriptors identified for predicting Mad2 native and mutants action mechanism and their involvement in genetic disorders are the steric (van der Waals area and solvent accessible area and their subdivided) and energetic van der Waals energy descriptors. The reliability of our QSAR models is indicated by significant values of statistical coefficients: Cross-validated correlation q2 (0.53-0.65) and fitted correlation r2 (0.82-0.90). Moreover, based on established QSAR equations, we rationally design and analyze nine de novo Mad2 mutants as possible promoters of CIN.

Pure Epigallocatechin-3-gallate-Assisted Green Synthesis of Highly Stable Titanium Dioxide Nanoparticles.

Nanoparticles (NPs) are conventionally produced by using physical and chemical methods that are no longer in alignment with current society's demand for a low environmental impact. Accordingly, green synthesis approaches are considered a potential alternative due to the plant extracts that substitute some of the hazardous reagents. The general mechanism is based on the reducing power of natural products that allows the formation of NPs from a precursor solution. In this context, our study proposes a simple, innovative, and reproducible green approach for the synthesis of titanium dioxide (TiO2 NPs) that uses, for the first time, the major component of green tea (Camellia sinensis)-epigallocatechin-3-gallate (EGCG), a non-toxic, dietary, accessible, and bioactive molecule. The influence of EGCG on the formation of TiO2 NPs was analyzed by comparing the physicochemical characteristics of green synthesized NPs with the chemically obtained ones. The synthesis of bare TiO2 NPs was performed by hydrolysis of titanium isopropoxide in distilled water, and green TiO2 NPs were obtained in the same conditions, but in the presence of a 1 mM EGCG aqueous solution. The formation of TiO2 NPs was confirmed by UV-VIS and FTIR spectroscopy. SEM micrographs showed spherical particles with relatively low diameters. Our findings also revealed that green synthesized NPs were more stable in colloids than the chemically synthesized ones. However, the phytocompound negatively influenced the formation of a crystalline structure in the green synthesized TiO2 NPs. Furthermore, the synthesis of EGCG-TiO2 NPs could become a versatile choice for applications extending beyond photocatalysis, including promising prospects in the biomedical field.

Unlocking the potential of biocompatible chitosan-hyaluronic acid nanogels labeled with fluorochromes: A promising step toward enhanced FRET bioimaging.

Chitosan is a natural polysaccharide widely used in medical formulations as nanoparticles due to their special properties. Our work aimed to assess the biocompatibility of chitosan-hyaluronic acid nanogels labeled with fluorochromes for use in biomedical applications, based on the FRET effect. The preparation method included the ionic gelation, grafting rhodamine or fluorescein isothiocyanate molecules onto the chitosan backbone. To assess the potential applications as fluorescence imaging tools of chitosan-fluorophores conjugates in diagnostics and therapies, SVEC4-10 cells (simian virus 40-transformed mouse microvascular endothelial cell line) and RAW264.7 murine macrophages were used within this study. Good biocompatibility was observed after 6 and 24 h of incubation with nanogels, with no increase in cell death or membrane damage for concentrations up to 120 µg/mL. Both types of fluorescent nanogels presented the tendency to agglomerate on the cell membrane's surface, and few cells were internalized, especially at the periphery of cells. Molecular dynamics simulations showed that distances between fluorophores fitted at values close to those calculated based on FRET experiments. These formulations can further incorporate gadolinium for better nanomedicine tools.

Transient Receptor Potential Ankyrin 1 (TRPA1) Modulation by 4-Hydroxynonenal (4-HNE) in Pancreatic Adenocarcinoma Cell Lines: Putative Roles for Therapies.

BACKGROUND: Transient receptor potential channels (TRP) are overexpressed in some pancreatic adenocarcinoma (PDAC) patients and cell lines, settling them as putative therapeutic targets in this disease. Reactive oxygen species (ROS), with levels increased in PDAC, modulate some members of the TRP family renamed "redox channels". Here, we investigate the direct effects of 4-hydroxinonenal (4-HNE) on TRPA1, natively expressed in PDAC cell lines and in association with cell migration and cell cycle progression. METHODS: We performed microfluorimetry experiments, while the activation of resident membrane channels was investigated using confocal microscopy. We applied a prospective molecular docking of 4-HNE using Autodock and AutoDock Tools4. Also, we simulated the diffusion of 4-HNE through the membrane from the extracellular space with the Permeability of Molecules across Membranes (PerMM) web server. The analysis of cell migration was performed using the wound healing assay, and cell cycle progression was acquired using a Beckman Coulter CytoFlex flow cytometer. RESULTS: Our results show, for the first time in PDAC, that 4-HNE diffuses through the cell membrane and rapidly activates Ca2+ uptake in PDAC cells. This process depends on TRPA1 activation, as 4-HNE forms a covalent binding with a pocket-like region within the intracellular N-terminal of the channel, shaped by the cysteine residues 621, 641, and 665. The activation of TRPA1 by 4-HNE inhibits cell migration and induces cell cycle arrest in the G2/M phase. CONCLUSIONS: Our study brings new insights into the effects of 4-HNE, highlighting the activation of the TRPA1 channel, a druggable, putative target for PDAC-expressing tumors.

Half-Curcuminoids Encapsulated in Alginate-Glucosamine Hydrogel Matrices as Bioactive Delivery Systems.

The therapeutic effects of curcumin and its derivatives, based on research in recent years, are limited by their low bioavailability. To improve bioavailability and develop the medical field of application, different delivery systems have been developed that are adapted to certain environments or the proposed target type. This study presents some half-curcuminoids prepared by the condensation of acetylacetone with 4-hydroxybenzaldehyde (C1), 4-hydroxy-3-methoxybenzaldehyde (C2), 4-acetamidobenzaldehyde (C3), or 4-diethylaminobenzaldehyde (C4), at microwaves as a simple, solvent-free, and eco-friendly method. The four compounds obtained were characterized in terms of morphostructural and photophysical properties. Following the predictions of theoretical studies on the biological activities related to the molecular structure, in vitro tests were performed for compounds C1-C3 to evaluate the antitumor properties and for C4's possible applications in the treatment of neurological diseases. The four compounds were encapsulated in two types of hydrogel matrices. First, the alginate-glucosamine network was generated and then the curcumin analogs were loaded (G1, G3, G5-G7, and G9). The second type of hydrogels was obtained by loading the active compound together with the generation of the hydrogel carrier matrices, by simply dissolving (G4 and G10) or by chemically binding half-curcuminoid derivatives to glucosamine (G2 and G8). Thus, two types of curcumin analog delivery systems were obtained, which could be applied in various types of medical treatments.

The Impact of ZnO and Fe2O3 Nanoparticles on Sunflower Seed Germination, Phenolic Content and Antiglycation Potential.

The enhancement of seed germination by using nanoparticles (NPs) holds the potential to elicit the synthesis of more desired compounds with important biomedical applications, such as preventing protein glycation, which occurs in diabetes. Here, we used 7 nm and 100 nm ZnO and 4.5 nm and 16.7 nm Fe2O3 NPs to treat sunflower seeds. We evaluated the effects on germination, total phenolic content, and the anti-glycation potential of extracted polyphenols. Sunflower seeds were allowed to germinate in vitro after soaking in NP solutions of different concentrations. Polyphenols were extracted, dosed, and used in serum albumin glycation experiments. The germination speed of seeds was significantly increased by the 100 nm ZnO NPs and significantly decreased by the 4.5 nm Fe2O3 NPs. The total phenolic content (TPC) of seeds was influenced by the type of NP, as ZnO NPs enhanced TPC, and the size of the NPs, as smaller NPs led to improved parameters. The polyphenols extracted from seeds inhibited protein glycation, especially those extracted from seeds treated with 7 nm ZnO. The usage of NPs impacted the germination speed and total polyphenol content of sunflower seeds, highlighting the importance of NP type and size in the germination process.

Biomedical Promise of Sustainable Microwave-Engineered Symmetric Curcumin Derivatives.

Curcumin is a polyphenol of the Curcuma longa plant, which can be used for various medicinal purposes, such as inflammation and cancer treatment. In this context, two symmetric curcumin derivatives (D1-(1E,6E)-1,7-bis(4-acetamidophenyl)hepta-1,6-diene-3,5-dione and D2-p,p-dihydroxy di-cinnamoyl methane) were obtained by the microwave-based method and evaluated for their antitumoral effect on human cervix cancer in comparison with toxicity on non-tumoral cells, taking into account that they were predicted to act as apoptosis agonists or anti-inflammatory agents. The HeLa cell line was incubated for 24 and 72 h with a concentration of 50 µg/mL of derivatives that killed almost half of the cells compared to the control. In contrast, these compounds did not alter the viability of MRC-5 non-tumoral lung fibroblasts until 72 h of incubation. The nitric oxide level released by HeLa cells was higher compared to MRC-5 fibroblasts after the incubation with 100 µg/mL. Both derivatives induced the decrease of catalase activity and glutathione levels in cancer cells without targeting the same effect in non-tumoral cells. Furthermore, the Western blot showed an increased protein expression of HSP70 and a decreased expression of HSP60 and MCM2 in cells incubated with D2 compared to control cells. We noticed differences regarding the intensity of cell death between the tested derivatives, suggesting that the modified structure after synthesis can modulate their function, the most prominent effect being observed for sample D2. In conclusion, the outcomes of our in vitro study revealed that these microwave-engineered curcumin derivatives targeted tumor cells, much more specifically, inducing their death.

Pharmacological descriptors related to the binding of Gp120 to CD4 corresponding to 60 representative HIV-1 strains.

The standardization of HIV-1 strains for vaccine tests include the use of viral reference panels. We determined a series of pharmacological descriptors (molecular surfaces, volumes, electrostatic energies, solvation energies, number of atoms, number of hydrogen donors or acceptors and number of rigid bonds) for the gp120 CD4-binding sites structures in the unliganded state from a reference panel of 60 diverse strains of HIV-1. We identified the descriptors that varied significantly between the strains, the outliner strains for each descriptor set and the possible correlations between the descriptors. Our results improve the knowledge about gp120, its molecular and possible neutralization properties.

Harnessing Code Interpreters for Enhanced Predictive Modeling: A Case Study on High-Density Lipoprotein Level Estimation in Romanian Diabetic Patients.

Diabetes is a condition accompanied by the alteration of body parameters, including those related to lipids like triglyceride (TG), low-density lipoproteins (LDLs), and high-density lipoproteins (HDLs). The latter are grouped under the term dyslipidemia and are considered a risk factor for cardiovascular events. In the present work, we analyzed the complex relationships between twelve parameters (disease status, age, sex, body mass index, systolic blood pressure, diastolic blood pressure, TG, HDL, LDL, glucose, HbA1c levels, and disease onset) of patients with diabetes from Romania. An initial prospective analysis showed that HDL is inversely correlated with most of the parameters; therefore, we further analyzed the dependence of HDLs on the other factors. The analysis was conducted with the Code Interpreter plugin of ChatGPT, which was used to build several models from which Random Forest performed best. The principal predictors of HDLs were TG, LDL, and HbA1c levels. Random Forest models were used to model all parameters, showing that blood pressure and HbA1c can be predicted based on the other parameters with the least error, while the less predictable parameters were TG and LDL levels. By conducting the present study using the ChatGPT Code Interpreter, we show that elaborate analysis methods are at hand and easy to apply by researchers with limited computational resources. The insight that can be gained from such an approach, such as what we obtained on HDL level predictors in diabetes, could be relevant for deriving novel management strategies and therapeutic approaches.

Ideonella sakaiensis Can Metabolize Bisphenol A as a Carbon Source.

Bisphenol A and its analogues represent a significant environmental and public health hazard, particularly affecting the endocrine systems of children and newborns. Due to the growing need for non-pathogenic biodegradation microbial agents as environmentally friendly and cost-effective solutions to eliminate endocrine disruptors, this study aimed to investigate the degradation of bisphenol A by Ideonella sakaiensis, based on its currently understood unique enzymatic machinery that is already well known for degrading polyethylene terephthalate. The present study provides novel insights into the metabolic competence and growth particularities of I. sakaiensis. The growth of I. sakaiensis exposed to bisphenol A exceeded that in the control conditions, starting with 72 h in a 70% nutrient-rich medium and starting with 48 h in a 100% nutrient-rich medium. Computational modeling showed that bisphenol A, as well as its analogue bisphenol S, are possible substrates of PETase and MHETase. The use of bisphenol A as a carbon and energy source through a pure I. sakaiensis culture expands the known substrate spectra and the species' potential as a new candidate for bisphenol A bioremediation processes.

Structural Bioinformatics Used to Predict the Protein Targets of Remdesivir and Flavones in SARS-CoV-2 Infection.

BACKGROUND: During the current SARS-CoV-2 pandemic, the identification of effective antiviral drugs is crucial. Unfortunately, no specific treatment or vaccine is available to date. OBJECTIVE: Here, we aimed to predict the interactions with SARS-CoV-2 proteins and protein targets from the human body for some flavone molecules (kaempferol, morin, pectolinarin, myricitrin, and herbacetin) in comparison to synthetic compounds (hydroxychloroquine, remdesivir, ribavirin, ritonavir, AMD-070, favipiravir). METHODS: Using MOE software and advanced bioinformatics and cheminformatics portals, we conducted an extensive analysis based on various structural and functional features of compounds, such as their amphiphilic field, flexibility, and steric features. The structural similarity analysis of natural and synthetic compounds was performed using Tanimoto coefficients. The interactions of some compounds with SARS-CoV-2 3CLprotease or RNA-dependent RNA polymerase were described using 2D protein-ligand interaction diagrams based on known crystal structures. The potential targets of considered compounds were identified using the SwissTargetPrediction web tool. RESULTS: Our results showed that remdesivir, pectolinarin, and ritonavir present a strong structural similarity which may be correlated to their similar biological activity. As common molecular targets of compounds in the human body, ritonavir, kaempferol, morin, and herbacetin can activate multidrug resistance-associated proteins, while remdesivir, ribavirin, and pectolinarin appear as ligands for adenosine receptors. CONCLUSION: Our evaluation recommends remdesivir, pectolinarin, and ritonavir as promising anti- SARS-CoV-2 agents.

Deep Learning in the Quest for Compound Nomination for Fighting COVID-19.

The current COVID-19 pandemic initiated an unprecedented response from clinicians and the scientific community in all relevant biomedical fields. It created an incredible multidimensional data-rich framework in which deep learning proved instrumental to make sense of the data and build models used in prediction-validation workflows that in a matter of months have already produced results in assessing the spread of the outbreak, its taxonomy, population susceptibility, diagnostics or drug discovery and repurposing. More is expected to come in the near future by using such advanced machine learning techniques to combat this pandemic. This review aims to unravel just a small fraction of the large global endeavors by focusing on the research performed on the main COVID-19 targets, on the computational weaponry used in identifying drugs to combat the disease, and on some of the most important directions found to contain COVID-19 or alleviating its symptoms in the absence of specific medication.

3D-ALMOND-QSAR Models to Predict the Antidepressant Effect of Some Natural Compounds.

The current treatment of depression involves antidepressant synthetic drugs that have a variety of side effects. In searching for alternatives, natural compounds could represent a solution, as many studies reported that such compounds modulate the nervous system and exhibit antidepressant effects. We used bioinformatics methods to predict the antidepressant effect of ten natural compounds with neuroleptic activity, reported in the literature. For all compounds we computed their drug-likeness, absorption, distribution, metabolism, excretion (ADME), and toxicity profiles. Their antidepressant and neuroleptic activities were predicted by 3D-ALMOND-QSAR models built by considering three important targets, namely serotonin transporter (SERT), 5-hydroxytryptamine receptor 1A (5-HT1A), and dopamine D2 receptor. For our QSAR models we have used the following molecular descriptors: hydrophobicity, electrostatic, and hydrogen bond donor/acceptor. Our results showed that all compounds present drug-likeness features as well as promising ADME features and no toxicity. Most compounds appear to modulate SERT, and fewer appear as ligands for 5-HT1A and D2 receptors. From our prediction, linalyl acetate appears as the only ligand for all three targets, neryl acetate appears as a ligand for SERT and D2 receptors, while 1,8-cineole appears as a ligand for 5-HT1A and D2 receptors.

Advanced Bioinformatics Tools in the Pharmacokinetic Profiles of Natural and Synthetic Compounds with Anti-Diabetic Activity.

Diabetes represents a major health problem, involving a severe imbalance of blood sugar levels, which can disturb the nerves, eyes, kidneys, and other organs. Diabes management involves several synthetic drugs focused on improving insulin sensitivity, increasing insulin production, and decreasing blood glucose levels, but with unclear molecular mechanisms and severe side effects. Natural chemicals extracted from several plants such as Gymnema sylvestre, Momordica charantia or Ophiopogon planiscapus Niger have aroused great interest for their anti-diabetes activity, but also their hypolipidemic and anti-obesity activity. Here, we focused on the anti-diabetic activity of a few natural and synthetic compounds, in correlation with their pharmacokinetic/pharmacodynamic profiles, especially with their blood-brain barrier (BBB) permeability. We reviewed studies that used bioinformatics methods such as predicted BBB, molecular docking, molecular dynamics and quantitative structure-activity relationship (QSAR) to elucidate the proper action mechanisms of antidiabetic compounds. Currently, it is evident that BBB damage plays a significant role in diabetes disorders, but the molecular mechanisms are not clear. Here, we presented the efficacy of natural (gymnemic acids, quercetin, resveratrol) and synthetic (TAK-242, propofol, or APX3330) compounds in reducing diabetes symptoms and improving BBB dysfunctions. Bioinformatics tools can be helpful in the quest for chemical compounds with effective anti-diabetic activity that can enhance the druggability of molecular targets and provide a deeper understanding of diabetes mechanisms.

Potential Therapeutic Approaches to Alzheimer's Disease By Bioinformatics, Cheminformatics And Predicted Adme-Tox Tools.

BACKGROUND: Alzheimer's disease (AD) is considered a severe, irreversible and progressive neurodegenerative disorder. Currently, the pharmacological management of AD is based on a few clinically approved acethylcholinesterase (AChE) and N-methyl-D-aspartate (NMDA) receptor ligands, with unclear molecular mechanisms and severe side effects. METHODS: Here, we reviewed the most recent bioinformatics, cheminformatics (SAR, drug design, molecular docking, friendly databases, ADME-Tox) and experimental data on relevant structurebiological activity relationships and molecular mechanisms of some natural and synthetic compounds with possible anti-AD effects (inhibitors of AChE, NMDA receptors, beta-secretase, amyloid beta (Aß), redox metals) or acting on multiple AD targets at once. We considered: (i) in silico supported by experimental studies regarding the pharmacological potential of natural compounds as resveratrol, natural alkaloids, flavonoids isolated from various plants and donepezil, galantamine, rivastagmine and memantine derivatives, (ii) the most important pharmacokinetic descriptors of natural compounds in comparison with donepezil, memantine and galantamine. RESULTS: In silico and experimental methods applied to synthetic compounds led to the identification of new AChE inhibitors, NMDA antagonists, multipotent hybrids targeting different AD processes and metal-organic compounds acting as Aß inhibitors. Natural compounds appear as multipotent agents, acting on several AD pathways: cholinesterases, NMDA receptors, secretases or Aß, but their efficiency in vivo and their correct dosage should be determined. CONCLUSION: Bioinformatics, cheminformatics and ADME-Tox methods can be very helpful in the quest for an effective anti-AD treatment, allowing the identification of novel drugs, enhancing the druggability of molecular targets and providing a deeper understanding of AD pathological mechanisms.

N-glycosylation state of TRPM8 protein revealed by terahertz spectroscopy and molecular modelling.

TRPM8 member of the TRP superfamily of membrane proteins participates to various cellular processes ranging from Ca2+ uptake and cold sensation to cellular proliferation and migration. TRPM8 is a large tetrameric protein with more than 70% of its residues located in the cytoplasm. TRPM8 is N-glycosylated, with a single site per subunit. This work focuses on the N-glycosylation of TRPM8 channel that was previously studied by our group in relation to proliferation and migration of tumoral cells. Here, experimental data performed with deglycosylating agents assess that the sole glycosylation site contains complex glycans with a molecular weight of 2.5 kDa. The glycosylation state of TRPM8 in cells untreated and treated with a deglycosylating agent was addressed with Terahertz (THz) spectroscopy. Results show a clear difference between cells comprising glycosylated and deglycosylated TRPM8, the first presenting an increased THz absorption. Human TRPM8 was modelled using as templates the available TRPM8 and other TRPM channels structures. Glycosylations were modelled by considering two glycan structures with molecular weight close to the experiment: shorter and branched at the first sugar unit (glc1) and longer and unbranched (glc2). Simulation of THz spectra based on the molecular dynamics of unglycosylated and the two glycosylated TRPM8 models in lipid membrane and solvation box showed that glycan structure strongly influences the THz spectrum of the channel and of other components from the simulation system. Only spectra of TRPM8 with glc1 glycans were in agreement with the experiment, leading to the validation of glc1 glycan structure.

Natural Compounds Therapeutic Features in Brain Disorders by Experimental, Bioinformatics and Cheminformatics Methods.

BACKGROUND: Synthetic compounds with pharmaceutical applications in brain disorders are daily designed and synthesized, with well first effects but also seldom severe side effects. This imposes the search for alternative therapies based on the pharmaceutical potentials of natural compounds. The natural compounds isolated from various plants and arthropods venom are well known for their antimicrobial (antibacterial, antiviral) and antiinflammatory activities, but more studies are needed for a better understanding of their structural and pharmacological features with new therapeutic applications. OBJECTIVES: Here we present some structural and pharmaceutical features of natural compounds isolated from plants and arthropods venom relevant for their efficiency and potency in brain disorders. We present the polytherapeutic effects of natural compounds belonging to terpenes (limonene), monoterpenoids (1,8-cineole) and stilbenes (resveratrol), as well as natural peptides (apamin, mastoparan and melittin). METHODS: Various experimental and in silico methods are presented with special attention on bioinformatics (natural compounds database, artificial neural network) and cheminformatics (QSAR, drug design, computational mutagenesis, molecular docking). RESULTS: In the present paper we reviewed: (i) recent studies regarding the pharmacological potential of natural compounds in the brain; (ii) the most useful databases containing molecular and functional features of natural compounds; and (iii) the most important molecular descriptors of natural compounds in comparison with a few synthetic compounds. CONCLUSION: Our paper indicates that natural compounds are a real alternative for nervous system therapy and represents a helpful tool for the future papers focused on the study of the natural compounds.