QSAR analysis on tacrine-related acetylcholinesterase inhibitors

The evaluation of the clinical effects of Tacrine has shown efficacy in delaying the deterioration of the symptoms of Alzheimer's disease, while confirming the adverse events consisting mainly in the elevated liver transaminase levels. The study of tacrine analogs presents a continuous interest, and for this reason we establish Quantitative Structure-Activity Relationships on their Acetylcholinesterase inhibitory activity. Ten groups of new developed Tacrine-related inhibitors are explored, which have been experimentally measured in different biochemical conditions and AChE sources. The number of included descriptors in the structure-activity relationship is characterized by 'Rule of Thumb'. The 1502 applied molecular descriptors could provide the best linear models for the selected Alzheimer's data base and the best QSAR model is reported for the considered data sets. The QSAR models developed in this work have a satisfactory predictive ability, and are obtained by selecting the most representative molecular descriptors of the chemical structure, represented through more than a thousand of constitutional, topological, geometrical, quantum-mechanical and electronic descriptor types.Fil: Wong, Kai Y.. Imperial College London; Reino UnidoFil: Mercader, Andrew Gustavo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico la Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentina. Universidad Nacional de La Plata; ArgentinaFil: Saavedra Reyes, Laura Marcela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico la Plata. Centro de Investigación y Desarrollo En Ciencias Aplicadas; Argentina. Universidad Nacional de La Plata; ArgentinaFil: Honarparvar, Bahareh. University of Kwazulu-Natal; SudáfricaFil: Romanelli, Gustavo Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico la Plata. Centro de Investigación y Desarrollo En Ciencias Aplicadas; Argentina. Universidad Nacional de La Plata; ArgentinaFil: Duchowicz, Pablo Román. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico la Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentina. Universidad Nacional de La Plata; Argentin

Application of quantitative structure-property relationship analysis to estimate the vapor pressure of pesticides

The application of molecular descriptors in describing Quantitative Structure Property Relationships (QSPR) for the estimation of vapor pressure (VP) of pesticides is of ongoing interest. In this study, QSPR models were developed using multiple linear regression (MLR) methods to predict the vapor pressure values of 162 pesticides. Several feature selection methods, namely the replacement method (RM), genetic algorithms (GA), stepwise regression (SR) and forward selection (FS), were used to select the most relevant molecular descriptors from a pool of variables. The optimum subset of molecular descriptors was used to build a QSPR model to estimate the vapor pressures of the selected pesticides. The Replacement Method improved the predictive ability of vapor pressures and was more reliable for the feature selection of these selected pesticides. The results provided satisfactory MLR models that had a satisfactory predictive ability, and will be important for predicting vapor pressure values for compounds with unknown values. This study may open new opportunities for designing and developing new pesticide.Fil: Goodarzi, Mohammad. Katholikie Universiteit Leuven; BélgicaFil: Coelho, Leandro dos Santos. Universidade Federal do Paraná; BrasilFil: Honarparvar, Bahareh. University of KwaZulu-Natal; SudáfricaFil: Ortiz, Erlinda del Valle. Universidad Nacional de Catamarca. Facultad de Tecnología y Ciencias Aplicadas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Duchowicz, Pablo Román. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentin

Molecular insight on the non-covalent interactions between carbapenems and L,D-transpeptidase 2 from Mycobacterium tuberculosis: ONIOM study

Tuberculosis remains a dreadful disease that has claimed many human lives worldwide and elimination of the causative agent Mycobacterium tuberculosis also remains elusive. Multidrug-resistant TB is rapidly increasing worldwide; therefore, there is an urgent need for improving the current antibiotics and novel drug targets to successfully curb the TB burden. L,D-Transpeptidase 2 is an essential protein in Mtb that is responsible for virulence and growth during the chronic stage of the disease. Both D,D- and L,D-transpeptidases are inhibited concurrently to eradicate the bacterium. It was recently discovered that classic penicillins only inhibit D,D-transpeptidases, while L,D-transpeptidases are blocked by carbapenems. This has contributed to drug resistance and persistence of tuberculosis. Herein, a hybrid two-layered ONIOM (B3LYP/6-31G+(d): AMBER) model was used to extensively investigate the binding interactions of LdtMt2 complexed with four carbapenems (biapenem, imipenem, meropenem, and tebipenem) to ascertain molecular insight of the drug-enzyme complexation event. In the studied complexes, the carbapenems together with catalytic triad active site residues of LdtMt2 (His187, Ser188 and Cys205) were treated at with QM [B3LYP/6-31+G(d)], while the remaining part of the complexes were treated at MM level (AMBER force field). The resulting Gibbs free energy (ΔG), enthalpy (ΔH) and entropy (ΔS) for all complexes showed that the carbapenems exhibit reasonable binding interactions towards LdtMt2. Increasing the number of amino acid residues that form hydrogen bond interactions in the QM layer showed significant impact in binding interaction energy differences and the stabilities of the carbapenems inside the active pocket of LdtMt2. The theoretical binding free energies obtained in this study reflect the same trend of the experimental observations. The electrostatic, hydrogen bonding and Van der Waals interactions between the carbapenems and LdtMt2 were also assessed. To further examine the nature of intermolecular interactions for carbapenem–LdtMt2 complexes, AIM and NBO analysis were performed for the QM region (carbapenems and the active residues of LdtMt2) of the complexes. These analyses revealed that the hydrogen bond interactions and charge transfer from the bonding to anti-bonding orbitals between catalytic residues of the enzyme and selected ligands enhances the binding and stability of carbapenem–LdtMt2 complexes.The College of Health Sciences (CHS), Aspen Pharmacare, MRC and NRF.https://link.springer.com/journal/108222019-06-01hj2018Chemistr

A comparative modeling and molecular docking study on <i>Mycobacterium tuberculosis</i> targets involved in peptidoglycan biosynthesis

<p>An alarming rise of multidrug-resistant <i>Mycobacterium tuberculosis</i> strains and the continuous high global morbidity of tuberculosis have reinvigorated the need to identify novel targets to combat the disease. The enzymes that catalyze the biosynthesis of peptidoglycan in <i>M. tuberculosis</i> are essential and noteworthy therapeutic targets. In this study, the biochemical function and homology modeling of MurI, MurG, MraY, DapE, DapA, Alr, and Ddl enzymes of the CDC1551 <i>M. tuberculosis</i> strain involved in the biosynthesis of peptidoglycan cell wall are reported. Generation of the 3D structures was achieved with Modeller 9.13. To assess the structural quality of the obtained homology modeled targets, the models were validated using PROCHECK, PDBsum, QMEAN, and ERRAT scores. Molecular dynamics simulations were performed to calculate root mean square deviation (RMSD) and radius of gyration (Rg) of MurI and MurG target proteins and their corresponding templates. For further model validation, RMSD and Rg for selected targets/templates were investigated to compare the close proximity of their dynamic behavior in terms of protein stability and average distances. To identify the potential binding mode required for molecular docking, binding site information of all modeled targets was obtained using two prediction algorithms. A docking study was performed for MurI to determine the potential mode of interaction between the inhibitor and the active site residues. This study presents the first accounts of the 3D structural information for the selected <i>M. tuberculosis</i> targets involved in peptidoglycan biosynthesis.</p