The Binding Mechanism Between Cyclodextrins and Anticancer Drug Noscapine: A Spectroscopic and Molecular Docking Study.

In this paper the binding of noscapine (NOS) as an anticancer drug with poor bioavailability and low solubility with beta and methyl-beta cyclodextrins (ß-CD and M-ß-CD) as the biocompatible drug carriers were discussed using ultraviolet-visible, fluorescence and nuclear magnetic resonance spectroscopy, as well as molecular docking. The absorption of NOS changed when it was bound to both cyclodextrins, resulting in a hyperchromic shift. It formed a 1:1 stoichiometry inclusion complex with both cyclodextrins according to the Benesi-Hildebrand equation. The binding affinity was larger in NOS-M-ß-CD (5.9 (± 0.66) × 103 M- 1) than NOS-ß-CD (3.7 (± 0.22) × 103 M- 1) complex. The fluorescence emission band of NOS at 408 nm was quenched when NOS was complexed with ß-CD, and enhanced in the presence of M-ß-CD, while the shoulder at 350 nm was enhanced selectively when NOS was complexed with M-ß-CD. The fluorescence quenching of NOS with ß-CD showed a negative deviation from the Stern-Volmer. The thermodynamic parameters have been estimated with the help of the Van't Hoff equation in different temperatures, and a dynamic mechanism was proposed for quenching. Also, both ΔH and ΔS have positive values thus the main interactions result in hydrophobic forces. Moreover, the negative value of ΔG indicates that the bonding process is spontaneous. 1H NMR chemical shift changes were observable for NOS and both CDs protons due to the chemical environment changes of some nuclei upon complexation. The molecular docking results revealed that the 1:1 inclusion complex possesses a good molecular shape complementarity score for their most probable structures, and indicated that the M-ß-CD inclusion system gave the higher complexation efficiency. The binding energy values for ß-CD and M-ß-CD were determined to be -6.7 and - 9.5 kcal/mol, respectively. These findings suggest the same as the result of experimental tests that the NOS-M-ß-CD complex is more stable than the NOS-ß-CD complex.

Development of a sensitive ds-DNA/Au NPs/ PGE biosensor for determination of Buprenorphine using electrochemical and molecular dynamic simulation investigation.

A sensitive electrochemical DNA biosensor has been developed for the detection of Buprenorphine (Bu), a narcotic pain reliever. To achieve this, double-stranded DNA (ds-DNA) was immobilized on a pencil graphite electrode that was modified with gold nanoparticles (Au NPs/PGE). The gold nanoparticles enhanced the performance of the DNA biosensor. The constructed ds-DNA/Au NPs/ PGE exhibited a linear detection range spanning from 0.05 to 100 µM with an impressive detection limit of 20 nM for Bu detection. Additionally, the DNA biosensor demonstrated good response in real samples evaluations. Finally, the interaction between carbon and gold atoms with DNA was confirmed through molecular dynamics simulation, while the interaction between DNA and the Bu drug was confirmed through molecular docking method. In conclusion, the electrochemical DNA biosensor presented in this study demonstrates exceptional sensitivity and reliability in the detection of buprenorphine. The incorporation of gold nanoparticles, as well as the use of molecular dynamics simulations and docking methods, contributes to a comprehensive understanding of the interactions involved in this detection process.

Dual-template imprinted polymer electrochemical sensor for simultaneous determination of malathion and carbendazim using graphene quantum dots.

Malathion (MAL) and carbendazim (CBZ) are organophosphate pesticides and fungicides, respectively. They are often used simultaneously in agriculture, and both have been shown to have harmful effects on humans and animals. Therefore, it is important to be able to measure both of these toxins simultaneously in order to assess their potential risks. This study aims to design a dual template electrochemical sensor using a cost-effective graphite-epoxy composite electrode (GECE) modified with molecularly imprinted polymers (MIPs) coated on graphene quantum dots (GQDs) for simultaneous detection of MAL and CBZ in real samples. GQDs were synthesized initially, and their surface was coated with MIPs that were formed using MAL and CBZ as the template molecules, ethylene glycol dimethyl acrylate as the cross-linker, and methacrylic acid as the functional monomer. The GQDs@MIP were characterized using Fourier transform infrared spectroscopy, field-emission scanning electron microscopy, and X-ray scattering spectroscopy. Parameters affecting the sensor response, such as the percentage of GQDs@MIP in the fabricated electrode, the pH of the rebinding solution and analysis solution, and the incubation time, were optimized. The optimum pH values of the rebinding solution were verified using density functional theory (DFT) calculations. Under the optimized conditions, differential pulse voltammetry (DPV) response calibration curves of MAL and CBZ were generated, and the results showed that the sensor had a linear response to MAL in the range of 0.02-55.00 µM with a limit of detection (LOD) of 2 nM (S/N = 3) and to CBZ in the range of 0.02-45.00 µM with a low LOD of 1 nM (S/N = 3). The results also demonstrated the proposed sensor's long-term stability and anti-interference capability. The practical applicability of the fabricated electrode was evaluated for real sample analysis, and good recovery values were obtained.

Research and study of 2-((4,6 dimethyl pyrimidine-2-yle) thio)-N-phenyl acetamide derivatives as inhibitors of sirtuin 2 protein for the treatment of cancer using QSAR, molecular docking and molecular dynamic simulation.

Phenyl acetamide derivatives have a wide range of biological activities, so their research and development can be useful and effective for the design production of new drugs. In this project, quantitative structure-activity relationship (QSAR) was performed. For modeling two methods of multiple linear regression (MLR) and nonlinear regression of support vector machine (SVR) were used. In the MLR stage, the best model with the values of R2train = 0.913 and R2test = 0.881 was selected by stepwise method. In this model, 4 descriptors of BELV2, GATS8p, GATS6e and RDF080m were included, which were used as input for the nonlinear support vector regression method. In the SVR model, the best results were obtained using the radial Gaussian kernel function (RBF) with R2train = 0.978 and R2test = 0.990. In the next step, using molecular docking and molecular dynamic simulation methods, the interaction between phenyl acetamide derivatives and the sirtuin 2 protein was investigated. Examining the results of molecular docking, it was observed that these derivatives formed complexes by forming hydrogen and hydrophobic bonds with the sirtuin 2 protein. Also, the results of molecular dynamic simulation show that phenyl acetamide compounds form stable complex with the sirtuin 2 protein, and it was found that the compounds with more activity have formed a number of hydrogen bonds with the protein.

A new electrochemical DNA biosensor for determination of anti-cancer drug chlorambucil based on a polypyrrole/flower-like platinum/NiCo2O4/pencil graphite electrode.

In the current study, DNA immobilization was performed on pencil graphite (PG) modified with a polypyrrole (PPy) and flower-like Pt/NiCo2O4 (FL-Pt/NiCo2O4) nanocomposite, as a new sensitive electrode to detect chlorambucil (CHB). Energy dispersive X-ray (EDX) analysis, X-ray diffraction (XRD) and scanning electron microscopy (SEM) techniques were employed to characterize the synthesized FL-Pt/NiCo2O4 and PPy/FL-Pt/NiCo2O4 nanocomposites. Moreover, differential pulse voltammetry (DPV) was selected to assess the guanine and adenine electrochemical responses on the DNA sensor. The CHB determination was performed using the maximum currents towards adenine and guanine in the acetate buffer solution (ABS). According to the results, ds-DNA/PPy/FL-Pt/NiCo2O4/PGE was able to detect the different concentrations of CHB in the range between 0.018 and 200 µM, with a detection limit of (LOD) of 4.0 nM. The new biosensor was also exploited for CHB determination in real samples (serum, urine and drug), the results of which revealed excellent recoveries (97.5% to 103.8%). Furthermore, the interaction between ds-DNA and CHB was studied using electrochemistry, spectrophotometry and docking whose outputs confirmed their effective interaction.

Fabrication of platinum-doped NiCo2O4 nanograss modified electrode for determination of carbendazim.

In this study, a platinum-doped nickel cobaltite nanograss (Pt-doped NiCo2O4 NG) with its own unique structural features was initially synthesized, utilizing a simple hydrothermal method and then applied as a novel platform for the detection of carbendazim (C9H9N3O2; CBZ). To this end, the CBZ electrochemical signals were evaluated by means of differential pulse voltammetry (DPV), demonstrating the acceptable catalytic effect of the Pt-doped NiCo2O4 NG/screen-printed electrode (SPE) on the CBZ oxidation signal. Under the optimized conditions, CBZ was subsequently quantified by the Pt-doped NiCo2O4 NG/SPE with a wide linear range (0.03-140 µM) and a low limit of detection (LOD) value (0.005 µM). The proposed sensor was thus characterized by good anti-interference ability, selectivity, and stability. The analysis of the real samples, viz. tomato and lettuce, also confirmed that the given sensor had good recoveries and relative standard deviation (RSD). Ultimately, a comparison between liquid chromatography-mass spectrometry (LC-MS) and this method established no significant difference in the results.

Development of a new electrochemical DNA biosensor based on Eu3+-doped NiO for determination of amsacrine as an anti-cancer drug: Electrochemical, spectroscopic and docking studies.

The present research reported a new electrochemical biosensor based on ds-DNA/Eu3+ doped NiO/CPE to detect amsacrine. Therefore, UV-Vis spectrophotometry, docking, and differential pulse voltammetry (DPV) have been used to study the interactions between amsacrine and dsDNA. Then, experimental parameters affected DNA immobilization and interactions between amsacrine and ds-DNA have been optimized. Afterwards, guanine oxidation peak current of ds-DNA has been chosen as a signal to analyze amsacrine in a concentration ranging between 0.1 and 100.0 µM and finally, limit of detection (LOD) of 0.05 µM has been calculated at optimal condition. Ultimately, it was found that the suggested biosensor is able to determine amsacrine in human serum and urine samples successfully.

Investigation of the mechanism of enantioseparation of some drug compounds by considering the mobile phase in HPLC by molecular dynamics simulation.

The separation mechanism of chiral drugs in high-pressure liquid chromatography is yet ambiguous. Computational chemistry helps to gain insights about chiral drug separations. The interaction between the 13 drug enantiomers and cellulose tris (3, 5-dimethyl phenyl carbamate) (chiral cel OD) as chiral stationary phase in 3 mobile phases was assayed by AutoDock and LAMMPS simulations. It is found that not only the structure of 2 enantiomers but also the mobile phase has an important role in enantioseparations and sometimes may invert the elution order. The molecular dynamics simulation is a comprehensive method that can be used to investigate the chiral drug enantioseparation mechanism in HPLC.

In silico study combining QSAR, docking and molecular dynamics simulation on 2,4-disubstituted pyridopyrimidine derivatives.

2,4-Disubstituted pyridopyrimidine derivatives were studied against ABCG2 enzyme. The modeling of pyridopyrimidine derivatives were done using two methods of multiple linear regression and support vector regression and four molecular descriptors of BIC4, log p, VRA2, and binding energy were selected for modeling. The statistical results were satisfactory. The interactions of ABCG2 enzyme with pyridopyrimidine derivatives were investigated using molecular docking method. Based on the results, increasing of binding energy and hydrophobicity of the compounds increase their inhibitory activity. Protein stability in complex with pharmaceutical derivatives was discussed using molecular dynamics simulation method.

Molecular docking, molecular dynamics simulations and QSAR studies on some of 2-arylethenylquinoline derivatives for inhibition of Alzheimer's amyloid-beta aggregation: Insight into mechanism of interactions and parameters for design of new inhibitors.

Alzheimer's disease is characterized using amyloid-beta (Aß) aggregation. The present work was carried out to extend and design a novel quantitative structure-activity relationship (QSAR) model on inhibition efficiency of some of new 2-arylethenylquinoline derivatives against the Aß1-42 peptide aggregation. The QSAR study, molecular docking and molecular dynamics (MD) simulations were performed to explore the influence of the structural features and investigate the molecular mechanism of ligands interactions with the Aß1-42 peptide. Using molecular docking was understood that electron donating groups with small size help to create interactions between the ligands and peptide residues to stabilize the conformation of ligands at the binding pocket. QSAR model was developed using the most stable conformations and parameters that obtained from the molecular docking. It is shown that, a combination of docking parameters and structural descriptors of inhibitor compounds can describe the inhibition efficiency on Aß1-42 peptide. The model exhibited statistically significant results so that the coefficient of determination R2train, Q2LOO, R2ext and GH (goodness of hit) are 0.912, 0.915, 0.836 and 0.804, respectively. The stability and binding modes of the compounds 1 and 13 with the most inhibition efficiency and compounds 12 and 36 with the lowest inhibition efficiency were determined by molecular dynamics simulations in GROMACS package. It is showed that interactions of compounds 1 and 13 are stable after 25ns of trajectories. Based on obtained results, 10 new drug compounds have been designed that provide better inhibition efficiency with the Aß1-42 peptide than the reference compounds.

3D protein structure prediction using Imperialist Competitive algorithm and half sphere exposure prediction.

Predicting the native structure of proteins based on half-sphere exposure and contact numbers has been studied deeply within recent years. Online predictors of these vectors and secondary structures of amino acids sequences have made it possible to design a function for the folding process. By choosing variant structures and directs for each secondary structure, a random conformation can be generated, and a potential function can then be assigned. Minimizing the potential function utilizing meta-heuristic algorithms is the final step of finding the native structure of a given amino acid sequence. In this work, Imperialist Competitive algorithm was used in order to accelerate the process of minimization. Moreover, we applied an adaptive procedure to apply revolutionary changes. Finally, we considered a more accurate tool for prediction of secondary structure. The results of the computational experiments on standard benchmark show the superiority of the new algorithm over the previous methods with similar potential function.

Application of multivariate image analysis in modeling (13) C-NMR chemical shifts of mono substituted pyridines.

Multivariate image analysis (MIA) descriptors have been applied to the quantitative structure-property relationship (QSPR) study of (13) C-NMR chemical shifts of 2-mono substituted pyridines. In this method, descriptors are generated from pixels of images and are analyzed with different multivariate methods. Correlation ranking-principal component regression and correlation ranking-principal component-artificial neural networks were applied in constructing predictor models. In this article, the role of weight update function in artificial neural networks was investigated too. Obtained results using the correlation ranking-principal component-artificial neural network method showed high performance for predicting of (13) C-NMR chemical shifts of pyridine derivatives. Also, these results indicated that MIA descriptors may be useful to predict (13) C-NMR chemical shifts. Finally, The MIA-QSPR approach coupled to artificial neural networks revealed that the predictive ability of MIA descriptors is comparable or even superior for the pyridine derivatives when compared with the ChemDraw program or gauge included atomic orbital procedure for (13) C chemical shifts calculations.

Application of multivariate image analysis in QSPR study of 13C chemical shifts of naphthalene derivatives: a comparative study.

A new implemented QSPR method, whose descriptors achieved from bidimensional images, was applied for predicting (13)C NMR chemical shifts of 25 mono substituted naphthalenes. The resulted descriptors were subjected to principal component analysis (PCA) and the most significant principal components (PCs) were extracted. MIA-QSPR (multivariate image analysis applied to quantitative structure-property relationship) modeling was done by means of principal component regression (PCR) and principal component-artificial neural network (PC-ANN) methods. Eigen value ranking (EV) and correlation ranking (CR) were used here to select the most relevant set of PCs as inputs for PCR and PC-ANN modeling methods. The results supported that the correlation ranking-principal component-artificial neural network (CR-PC-ANN) model could predict the (13)C NMR chemical shifts of all 10 carbon atoms in mono substituted naphthalenes with R(2) ≥ 0.922 for training set, R(2) ≥ 0.963 for validation set and R(2) ≥ 0.936 for the test set. Comparison of the results with other existing factor selection method revealed that less accurate results were obtained by the eigen value ranking procedure.

Quantitative structure-retention relationship study of some phenol derivatives in gas chromatography.

A quantitative structure-retention relationship (QSRR) model has been developed for the gas chromatographic retention times of 37 phenolic derivatives in a DB-5 non-polar column (95% dimethyl and 5% diphenyl-polysiloxane). As a first step, multiple linear regression (MLR) was employed to gain informative descriptors that can predict the retention times of these compounds. Descriptors appearing in the MLR model are categorized as topological and geometric parameters that comply with the applied column. Furthermore, each molecular descriptor in this model was examined to unfold the relationship between molecular structures and their retention times. Then, a 4-4-1 neural network was developed using the descriptors selected by the MLR model. The comparison of the standard errors and correlation coefficients reveals the superiority of artificial neural networks (ANN) over the MLR model. This refers to the fact that the retention behaviors of molecules display non-linear characteristics. The consistency and reliability of ANN model was investigated using the L4O cross-validation technique. The obtained results are closely in compliance with the experiment. Moreover, the mean effect of descriptors shows that Kier symmetry index is the most important factor affecting the retention behavior of molecules.

Modeling the antileishmanial activity screening of 5-nitro-2-heterocyclic benzylidene hydrazides using different chemometrics methods.

QSAR analysis for modeling the antileishmanial activity screening of a series of 49 nitro derivatives of Hydrazides were carried out using different Chemometrics methods. First, a large number of descriptors were calculated using Hyperchem, Mopac and Dragon softwares. Then, a suitable number of these descriptors were selected using multiple linear regression (MLR) technique. Then selected descriptors were used as inputs for artificial neural networks with three different weight update functions including Levenberg-Marquardt back propagation network (LM-ANN), resilient back propagation network (RP-ANN) and variable learning rate algorithm (GDX-ANN). The best artificial neural network model was an LM-ANN with a 5-5-1 architecture. Comparison of the results indicates that the LM-ANN method has better predictive power than the other methods.