Genome wide analysis and comparative docking studies of new diaryl furan derivatives against human cyclooxygenase-2, lipoxygenase, thromboxane synthase and prostacyclin synthase enzymes involved in inflammatory pathway

In an effort to develop potent anti-inflammatory and antithrombotic drugs, a series of new 4-(2-phenyltetrahydrofuran-3-yl) benzene sulfonamide analogs were designed and docked against homology models of human cyclooxygenase-2 (COX-2), lipoxygenase and thromboxane synthase enzymes built using MODELLER 7v7 software and refined by molecular dynamics for 2 ns in a solvated layer. Validation of these homology models by procheck, verify-3D and ERRAT programs revealed that these models are highly reliable. Docking studies of 4-(2-phenyltetrahydrofuran-3-yl) benzene sulfonamide analogs designed by substituting different chemical groups on benzene rings replacing 1H pyrazole in celecoxib with five membered thiophene, furan, 1H pyrrole, 1H imidazole, thiazole and 1,3-oxazole showed that diaryl furan molecules showed good binding affinity towards mouse COX-2. Further, docking studies of diaryl furan derivatives are likely to have superior thromboxane synthase and COX-2 selectivity. Docking studies against site directed mutagenesis of Arg120Ala, Ser530Ala, Ser530Met and Tyr355Phe enzymes displayed the effect of inhibition of COX-2. Drug likeliness and activity decay for these inhibitors showed that these molecules act as best drugs at very low concentrations.status: publishe

Investigations on neomycin production with immobilized cells ofStreptomyces marinensis Nuv-5 in calcium alginate matrix

The purpose of this investigation was to study the effect ofStreptomyces marinensis NUV-5 cells immobilized in calcium alginate for the production of neomycin. The effect of various parameters, such as the effect of alginate concentration (1%, 2%, 3%, 4%, and 5% wt/vol), the effect of cation (caCl2, BaCl2, and SrCl2), the concentration of cation (0.01M, 0.125M, 0.25M, 0.375M, and 0.5M), the curing times (1, 6, 11, 16, and 21 hours), and the diameter of the bead (1.48, 2.16, 3.24, 4.46, and 5.44 mm), on neomycin production and bead stability were studied. The effect of maltose (4%, 3%, 2%, and 1% wt/vol) and sodium glutamate (0.6%, 0.3%, 0.15%, and 0.075%) wt/vol) concentration on neomycin production was also studied. Better neomycin production was achieved with optimized parameters, such as alginate at 2% wt/vol, 0.25M CaCl2, 1-hour curing time, and 3.24 mm bead diameter. Effective neomycin production was achieved with 3% wt/vol maltose and 0.6% wt/vol sodium glutamate concentration. The repeated batch fermentations were conducted (every 96 hours) using the optimized alginate beads, employing the production medium with 3% wt/vol maltose and 0.6% wt/vol sodium glutamate along with minerals salts solution. The increase in antibiotic production was observed up to the 5th cycle, and later gradual decrease in antibiotic production was observed. Comparison of the total antibiotic production with free cells and immobilized cells was also done. An enhanced antibiotic productivity of 32% was achieved with immobilized cells over the conventional free-cell fermentation, while 108% more productivity was achieved over the washed free-cell fermentation. From these results it is concluded that the immobilized cells ofS marinensis NUV-5 in calcium alginate are more efficient for the production of neomycin with repeated batch fermentation

Learning protein binding affinity using privileged information

Abstract Background Determining protein-protein interactions and their binding affinity are important in understanding cellular biological processes, discovery and design of novel therapeutics, protein engineering, and mutagenesis studies. Due to the time and effort required in wet lab experiments, computational prediction of binding affinity from sequence or structure is an important area of research. Structure-based methods, though more accurate than sequence-based techniques, are limited in their applicability due to limited availability of protein structure data. Results In this study, we propose a novel machine learning method for predicting binding affinity that uses protein 3D structure as privileged information at training time while expecting only protein sequence information during testing. Using the method, which is based on the framework of learning using privileged information (LUPI), we have achieved improved performance over corresponding sequence-based binding affinity prediction methods that do not have access to privileged information during training. Our experiments show that with the proposed framework which uses structure only during training, it is possible to achieve classification performance comparable to that which is obtained using structure-based features. Evaluation on an independent test set shows improved performance over the PPA-Pred2 method as well. Conclusions The proposed method outperforms several baseline learners and a state-of-the-art binding affinity predictor not only in cross-validation, but also on an additional validation dataset, demonstrating the utility of the LUPI framework for problems that would benefit from classification using structure-based features. The implementation of LUPI developed for this work is expected to be useful in other areas of bioinformatics as well