Current Computer - Aided Drug Design - Volume 12, Issue 4, 2016

Background: The article proposes a new concept explaining nerve conduction information. The conduction is based upon proton hopping, the fastest known chemical reaction. A summary of the proton hopping at several parts of the nerve structure are described. Methods: The details of each part of the nerve system are described in the article. Results: A summary of the parts of the nerve structure involving the role of proton hopping, are expressed to give a complete picture of the nerve function and the role of proton hopping. Conclusion: An overall description of the function of the nerve is described in terms of the role of proton hopping as the mechanism of message passage.

Background: Various applications of nanosubstances in industrial and consumer goods sectors are growing rapidly because of their useful chemical and physical properties. Objectives: Assessment of hazard posed by exposure to nanosubstances is essential for the protection of human and ecological health. Methods: We analyzed the proteomics patterns of Caco-2/HT29-MTX cells in co-culture exposed for three and twenty four hours to two kinds of nanoparticles: multi-walled carbon nanotubes (MWCNT) and TiO2 nanobelts (TiO2-NB). For each nanosubstance cells were exposed to two concentrations of the material before carrying out proteomics analyses: 10 μg and 100 μg. In each case over 3000 proteins were identified. A mathematically based similarity index, which measures the changes in abundances of cellular proteins that are highly affected by exposure to the nanosubstances, was used to characterize toxic effects of the nanomaterials. Results: We identified 8 and 25 proteins, which are most highly affected by MWCNT and TiO2-NB, respectively. These proteins may be responsible for specific response of cells to the nanoparticles. Further 14 reported proteins are affected by either of the two nanoparticles and they are probably related to nonspecific toxic response of the cells. Conclusion: The similarity methods proposed in this paper may be useful in the management and visualization of the large amount of data generated by proteomics technologies.

Background: Quantitative Structure-Activity Relationships (QSAR) is a well-established branch of computational chemistry. The presence of QSAR papers is decreasing for the last few years. Objective: To highlight recent trends of QSAR in general and trends of machine learning methods in particular. Method: A bibliometric analysis of articles published in top ten molecular modeling and medicinal chemistry journals was carried out. The bibliometric statistics was collected for papers published in 2009 and 2015 and compared. Results: During 5-year span studied, the fraction of QSAR studies underwent a twofold decrease. Top journals of both categories became less likely to publish Multiple Linear Regression models and increased the presence of Random forest and Naïve Bayes methods. 3D-QSAR remains the most popular method of studying structure-activity relationships with a slight decrease of its presence in molecular modeling journals but a relative increase in medicinal chemistry. Conclusion: The downward QSAR trend might have several reasons: more stringent criteria for QSAR studies acceptance by journals, transformation of QSAR studies into routine work due to wider availability of QSAR methods and the overall maturation of QSAR field, and possible disappointment in QSAR. We expect that the progress in machine learning methods being adopted by chem(o)informaticians finally will help QSAR to find its place in drug design and to move to the Plateau of Productivity.

Background: With cases of emergence of drug resistance to the current competitive inhibitors of neuraminidase (NA) such as oseltamivir and zanamavir, there is a present need for an alternative approach in the treatment of avian influenza. With this in view, some flavones and chalcones were designed based on quercetin, the most active naturally occurring noncompetitive inhibitor. Objective: We attempt to understand the binding of quercetin to H5N1-NA, and synthetic analogs of quercetin namely flavones and its precursors the chalcones using computational tools. Methods: Molecular docking was done using Libdock. Molecular dynamics (MD) simulations were performed using Amber14. We synthesized the two compounds; their structures were confirmed by infrared spectroscopy, 1H-NMR, and mass spectrometry. These molecules were then tested for H5N1-NA inhibition and kinetics of inhibition. Results: Molecular docking studies yielded two compounds i.e., 4’-methoxyflavone and 2’-hydroxy-4-methoxychalcone, as promising leads which identified them as binders of the 150-cavity of NA. Furthermore, MD simulation studies revealed that quercetin and the two compounds bind and hold the 150 loop in its open conformation, which ultimately perturbs the binding of sialic acid in the catalytic site. Estimation of the free energy of binding by MM-PBSA portrays quercetin as more potent than chalcone and flavone. These molecules were then determined as non-competitive inhibitors from the Lineweaver-Burk plots rendered from the enzyme kinetic studies. Conclusion: We conclude that non-competitive type of inhibition, as shown in this study, can serve as an effective method to block NA and evade the currently seen drug resistance.

Background: Plasmodium falciparum leucyl aminopeptidase (PfA-M17) regulates the intracellular pool of amino acids required for the growth and development of parasites. Thus, PfA-M17 is a promising target for anti-malarial drug development. Method: In the present study, structure-based drug design was used to identify novel PfA-M17 inhibitors, which were subsequently validated by in vitro PfA-M17 and human LAP3 enzyme inhibition assay. A library of 3,147,882 compounds was screened using receptor-based virtual screening against the active site of PfA-M17, and three levels of accuracy were used: high-throughput virtual screening, gridbased ligand docking with energetics (Glide standard precision) and Glide extra precision. Results: Seventeen screened compounds were selected and tested in the rPfA-M17 enzyme inhibition assay. Of these nine compounds were found to be effective inhibitors. To test the target activity, all nine PfA-M17 inhibitors were tested against rhLAP3, the human homolog of PfA-M17. One compound (compound 2) was found to be moderately effective against PfA-M17 (Ki = 287 μM) with limited inhibitory activity against hLAP3 (Ki of 4,464 μM). Subsequently, induced fit docking and pharmacophore modelling were used to further understand more precise ligand–protein interactions in the protein–inhibitor complexes. Conclusion: Among the 9 effective PfA-M17 inhibitors, 5 compounds were found effective in the P. falciparum schizont maturation inhibition (SMI) assay. A good correlation (r =0.83) was observed between the rPfA-M17 enzyme inhibition concentration and SMI assay.

Background: Computed mathematical descriptors of molecules are used for the prediction of their property/ bioactivity. In the 1970s only a few descriptors could be calculated, currently available software can calculate a large number of descriptors for molecules or biomolecules like DNA/ RNA, proteins. Objective: When p molecular descriptors are calculated for n molecules, the data set can be viewed as n vectors in p dimensions, each chemical being represented as a point in Rp. Because many of the descriptors are strongly correlated, the n points in Rp will lie on a subspace of dimension lower than p. Methods like principal components analysis (PCA) can be used to characterize the intrinsic dimensionality of chemical spaces. Taking motivation from the work of Basak et al. in 1980s in using PCA of descriptors calculated for various congeneric and structurally diverse sets of chemicals relevant to new drug discovery and predictive toxicology, this paper explores the intrinsic dimensionality of chemical spaces for robust QSAR model development. Methodology: Intrinsic dimensionality of chemical spaces was studied using three new statistical approaches and two data sets, viz. a congeneric set of 95 aromatic and heteroaromatic amine mutagens and a structurally diverse set of 508 chemical mutagens. Results: The new outlier-robust methods applied here yield favorable prediction results compared to previous studies on same datasets. Conclusion: We conclude that while analyzing data on large number of chemical descriptors, it is advisable to build QSAR models that are outlier-robust, and take into consideration the underlying correlations among predictors.

Background: Checkpoint kinase 1 (Chk1) has emerged as a potential therapeutic target for design and development of novel anticancer drugs. Objective: Herein, we have performed three-dimensional quantitative structure-activity relationship (3D-QSAR) and molecular docking analyses on a series of diazacarbazoles to design potent Chk1 inhibitors. Methods: 3D-QSAR models were developed using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques. Docking studies were performed using AutoDock. Results: The best CoMFA and CoMSIA models exhibited cross-validated correlation coefficient (q2) values of 0.631 and 0.585, and non-cross-validated correlation coefficient (r2) values of 0.933 and 0.900, respectively. CoMFA and CoMSIA models showed reasonable external predictabilities (r2 pred) of 0.672 and 0.513, respectively. Conclusion: A satisfactory performance in the various internal and external validation techniques indicated the reliability and robustness of the best model. Docking studies were performed to explore the binding mode of inhibitors inside the active site of Chk1. Molecular docking revealed that hydrogen bond interactions with Lys38, Glu85 and Cys87 are essential for Chk1 inhibitory activity. The binding interaction patterns observed during docking studies were complementary to 3D-QSAR results. Information obtained from the contour map analysis was utilized to design novel potent Chk1 inhibitors. Their activities and binding affinities were predicted using the derived model and docking studies. Designed inhibitors were proposed as potential candidates for experimental synthesis.