QSAR models and scaffold-based analysis of non-nucleoside HIV RT inhibitors

© 2015 Elsevier B.V. A selection of 289 pyrimidine derivatives with anti-HIV RT activities as non-nucleoside HIV RT inhibitors (NNRTI) were studied. The associative neural network (ASNN) method was applied to develop a quantitative structure-activity relationship (QSAR) for anti-HIV RT activity. The calculated models were validated using the bagging approach. A consensus model with R2=0.87 and RMSE=0.5 was obtained from 10 individual models. Scaffold analysis and molecular docking of the compounds used in the QSAR model identified a potential chemical scaffold. The results showed that scaffold-based analysis of the QSAR model could be helpful in identifying potent scaffolds for further exploration than analyzing the overall model. Matched molecular pair analysis (MMPA) was applied in the QSAR model to characterize molecular transformations causing a significant change in the anti-HIV activity. The linear QSAR model was calculated to explore the structural features important for NNRTI activity. The results revealed that the activity of NNRT inhibitors is strongly dependent on their aromaticity and structural flexibility. The scaffold-based analysis of QSAR models with molecular docking and MMPA was found to be helpful in characterizing potential scaffolds for anti-HIV RT derivatives. The outcome of this study provides a deeper insight into the computer-aided scaffold-based design of novel molecules with HIV RT activities. It was also clearly shown that the consensus model's failure to correctly predict new chemical series could be due to the limitation of its applicability domain (AD). Redevelopment of models using new measurements can dramatically increase their AD and performance

ANALYSIS OF BIOLOGICAL CHARACTERISTICS OF BRYOBIA RUBRIOCULUS SCHEUTEN (ACARI: TETRANYCHIDAE) CONCERNING THE PHYSIOLOGICAL ASPECTS OF SOUR CHERRY

Sour cherry is considered as an economically important fruit tree, providing a valuable and delicious fruit across the world. Recently, large numbers of the brown mite, Bryobia rubrioculus Scheuten (Acari: Tetranychidae) attacked sour cherry orchards of Hamedan, Iran. In 2013, biological experiments were conducted on two sour cherry cultivars under constant conditions (26 ± 0.5)oC, (L:D) (16:8), and (60 ± 5) RH. Pre-imaginal development time was 22.4 and 24.89 days, gross fecundity rate was 11.59 and 9.87 eggs, and rm assumed to be (was determined) 0.0164 and 0.0048 day-1 respectively. Few biological parameters of the brown mite had correlation with physiological aspects of the sour cherry. The results of this research provide important data about brown mite for integrate pest management

QSAR models and scaffold-based analysis of non-nucleoside HIV RT inhibitors

© 2015 Elsevier B.V. A selection of 289 pyrimidine derivatives with anti-HIV RT activities as non-nucleoside HIV RT inhibitors (NNRTI) were studied. The associative neural network (ASNN) method was applied to develop a quantitative structure-activity relationship (QSAR) for anti-HIV RT activity. The calculated models were validated using the bagging approach. A consensus model with R2=0.87 and RMSE=0.5 was obtained from 10 individual models. Scaffold analysis and molecular docking of the compounds used in the QSAR model identified a potential chemical scaffold. The results showed that scaffold-based analysis of the QSAR model could be helpful in identifying potent scaffolds for further exploration than analyzing the overall model. Matched molecular pair analysis (MMPA) was applied in the QSAR model to characterize molecular transformations causing a significant change in the anti-HIV activity. The linear QSAR model was calculated to explore the structural features important for NNRTI activity. The results revealed that the activity of NNRT inhibitors is strongly dependent on their aromaticity and structural flexibility. The scaffold-based analysis of QSAR models with molecular docking and MMPA was found to be helpful in characterizing potential scaffolds for anti-HIV RT derivatives. The outcome of this study provides a deeper insight into the computer-aided scaffold-based design of novel molecules with HIV RT activities. It was also clearly shown that the consensus model's failure to correctly predict new chemical series could be due to the limitation of its applicability domain (AD). Redevelopment of models using new measurements can dramatically increase their AD and performance

QSAR models and scaffold-based analysis of non-nucleoside HIV RT inhibitors

© 2015 Elsevier B.V. A selection of 289 pyrimidine derivatives with anti-HIV RT activities as non-nucleoside HIV RT inhibitors (NNRTI) were studied. The associative neural network (ASNN) method was applied to develop a quantitative structure-activity relationship (QSAR) for anti-HIV RT activity. The calculated models were validated using the bagging approach. A consensus model with R2=0.87 and RMSE=0.5 was obtained from 10 individual models. Scaffold analysis and molecular docking of the compounds used in the QSAR model identified a potential chemical scaffold. The results showed that scaffold-based analysis of the QSAR model could be helpful in identifying potent scaffolds for further exploration than analyzing the overall model. Matched molecular pair analysis (MMPA) was applied in the QSAR model to characterize molecular transformations causing a significant change in the anti-HIV activity. The linear QSAR model was calculated to explore the structural features important for NNRTI activity. The results revealed that the activity of NNRT inhibitors is strongly dependent on their aromaticity and structural flexibility. The scaffold-based analysis of QSAR models with molecular docking and MMPA was found to be helpful in characterizing potential scaffolds for anti-HIV RT derivatives. The outcome of this study provides a deeper insight into the computer-aided scaffold-based design of novel molecules with HIV RT activities. It was also clearly shown that the consensus model's failure to correctly predict new chemical series could be due to the limitation of its applicability domain (AD). Redevelopment of models using new measurements can dramatically increase their AD and performance

QSAR models and scaffold-based analysis of non-nucleoside HIV RT inhibitors

© 2015 Elsevier B.V. A selection of 289 pyrimidine derivatives with anti-HIV RT activities as non-nucleoside HIV RT inhibitors (NNRTI) were studied. The associative neural network (ASNN) method was applied to develop a quantitative structure-activity relationship (QSAR) for anti-HIV RT activity. The calculated models were validated using the bagging approach. A consensus model with R2=0.87 and RMSE=0.5 was obtained from 10 individual models. Scaffold analysis and molecular docking of the compounds used in the QSAR model identified a potential chemical scaffold. The results showed that scaffold-based analysis of the QSAR model could be helpful in identifying potent scaffolds for further exploration than analyzing the overall model. Matched molecular pair analysis (MMPA) was applied in the QSAR model to characterize molecular transformations causing a significant change in the anti-HIV activity. The linear QSAR model was calculated to explore the structural features important for NNRTI activity. The results revealed that the activity of NNRT inhibitors is strongly dependent on their aromaticity and structural flexibility. The scaffold-based analysis of QSAR models with molecular docking and MMPA was found to be helpful in characterizing potential scaffolds for anti-HIV RT derivatives. The outcome of this study provides a deeper insight into the computer-aided scaffold-based design of novel molecules with HIV RT activities. It was also clearly shown that the consensus model's failure to correctly predict new chemical series could be due to the limitation of its applicability domain (AD). Redevelopment of models using new measurements can dramatically increase their AD and performance

TEMPERATURE-DEPENDENT DEMOGRAPHIC PARAMETERS OF BRYOBIA RUBRIOCULUS (ACARI: TETRANYCHIDAE) ON SWEET CHERRY

The brown mite, Bryobia rubrioculus (Acari: Tetranychidae), is a serious pest in orchards in Iran. Laboratory experiments were preformed in growth chambers at eight constant temperatures (15, 17.5, 20, 22.5, 25, 27.5, 30 and 32.5oC), 60 ±5% RH and a photoperiod of 16:8 h. (Light: Dark) using the sweet cherry leaves (Prunus avium). The survival rate (lx) was the highest at 15oC and lowest at 32.5oC. The life expectancies (ex) of 1-day adults were determined 38.28 to 11.51 at 15 to 32.5oC, respectively. There were significant differences between demographic parameters of B. rubrioculus at various temperatures. Net reproduction rate (R0), generation time (tG) and intrinsic rate of increase (rm) ranged from 5.83, 42.79 and 0.041 at 15oC to 0.67, 24.15 and 0.025 at 32.5oC. The gross fertility (4.4 eggs) was recorded at 32.5oC, and the highest (29.5 eggs) was at 20oC. 20oC is the optimal temperature for B. rubrioculus population growth

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