Nitric Oxide (NO) is the smallest known molecule in the mammalian biological signaling system. It is well established in the field of molecular biology as an endothelium derived growth factor. It is biosynthesized from L-arginine by three enzyme isoforms - endothelial NOS (eNOS or NOS3), neuronal NOS (nNOS or NOS1) and inducible NOS (iNOS or NOS2). In last two decades, all these enzyme isoforms were investigated to understand their roles in different pathological conditions. The relation between these enzymes and cancer is ambiguous and complex as NOS enzymes have been found to be both pro- and anti-tumorigenic. Although the exact functions of NOS enzymes in cancer progression is still under investigation, some recent reports repeatedly highlighted roles of iNOS and eNOS enzymes in the development of angiogenesis in cancer. As per the latest reports, eNOS should be the most important NOS isoform involved in angiogenesis. The current review highlights the aspect and latest developments of NOS inhibitors as anti-angiogenic agents in cancer. In addition, we report molecular modeling analyses (2D and 3D QSAR, pharmacophore modeling and molecular docking) of some reported eNOS inhibitors to understand structural requirements of these molecules for higher activity. © 2016 Bentham Science Publishers.

Eleven different complexes were found between heroin (or its main metabolite, morphine) and cocaine (or its main metabolite, ecgonine methyl ester) in a B3LYP computational study. Ten of these complexes display intermolecular hydrogen bonds, while only one is a stacked structure. All of them display positive complexation energies in the gas phase that turn into negative values in aqueous solution according to PCM calculations. Complexation energies become even more negative when explicit solvation water molecules are taken into account. Thus, complexes between these compounds could be present in biological media. The electron density of the 11 complexes was analysed within the QTAIM framework distinguishing three terms for every atomic property: (i) geometry distortion; (ii) BSSE estimated by extending the counterpoise (CP) method; and (iii) binding. We notice that: (i) geometry distortion effects are basically localised in the atoms involved in intermolecular bonds (interaction sites); (ii) counterpoise corrections for atomic properties are very small; and (iii) binding CP-corrected effects spread throughout the whole complex. The later are the most significant and indicate the important role played by hydrogens outside the interaction sites as electron density sources and sinks in complex formation.

Interactions between the single walled carbon nanotube (SWCNT) family and a mitochondrial ADP/ATP carrier (ANT-1) were evaluated using constitutional (functional groups, number of carbon atoms, etc.) and electronic nanodescriptors defined by (n, m)-Hamada indexes (armchair, zig-zag and chiral). The Free Energy of Binding (FEB) was determined by molecular docking simulation and the results showed that FEB was statistically more negative (p < 0.05), following the order SWCNT-COOH > SWCNT-OH > SWCNT, suggesting that polar groups favor the anchorage to ANT-1. In this regard, it was showed that key ANT-1 amino acids (Arg 79, Asn 87, Lys 91, Arg 187, Arg 234 and Arg 279) responsible for ADP-transport were conserved in ANT-1 from different species examined to predict SWCNT interactions, including shrimp Litopenaeus vannamei and fish Danio rerio commonly employed in ecotoxicology. The SWCNT-ANT-1 inter-atomic distances for the key ANT-1 amino acids were similar to that with carboxyatractyloside, a classical inhibitor of ANT-1. Significant linear relationships between FEB and n-Hamada index were found for zig-zag SWCNT and SWCNT-COOH (R2 = 0.95 in both cases). A Perturbation Theory-Nano-Quantitative Structure-Binding Relationship (PT-NQSBR) model was fitted that was able to distinguish between strong (FEB < -14.7 kcal mol-1) and weak (FEB ≥ -14.7 kcal mol-1) SWCNT-ANT-1 interactions. A simple ANT-1-inhibition respiratory assay employing mitochondria suspension from L. vannamei, showed good accordance with the predicted model. These results indicate that this methodology can be employed in massive virtual screenings and used for making regulatory decisions in nanotoxicology. © The Royal Society of Chemistry 2016.

In this work we report the first attempt to study the effect of activity cliffs over the generalization ability of machine learning (ML) based QSAR classifiers, using as study case a previously reported diverse and noisy dataset focused on drug induced liver injury (DILI) and more than 40 ML classification algorithms. Here, the hypothesis of structure-activity relationship (SAR) continuity restoration by activity cliffs removal is tested as a potential solution to overcome such limitation. Previously, a parallelism was established between activity cliffs generators (ACGs) and instances that should be misclassified (ISMs), a related concept from the field of machine learning. Based on this concept we comparatively studied the classification performance of multiple machine learning classifiers as well as the consensus classifier derived from predictive classifiers obtained from training sets including or excluding ACGs. The influence of the removal of ACGs from the training set over the virtual screening performance was also studied for the respective consensus classifiers algorithms. In general terms, the removal of the ACGs from the training process slightly decreased the overall accuracy of the ML classifiers and multi-classifiers, improving their sensitivity (the weakest feature of ML classifiers trained with ACGs) but decreasing their specificity. Although these results do not support a positive effect of the removal of ACGs over the classification performance of ML classifiers, the "balancing effect" of ACG removal demonstrated to positively influence the virtual screening performance of multi-classifiers based on valid base ML classifiers. Specially, the early recognition ability was significantly favored after ACGs removal. The results presented and discussed in this work represent the first step towards the application of a remedial solution to the activity cliffs problem in QSAR studies.

Broad range of selectivity possesses serious limitation for the development of matrix metalloproteinase-2 (MMP-2) inhibitors for clinical purposes. To develop potent and selective MMP-2 inhibitors, initially multiple molecular modeling techniques were adopted for robust design. Predictive and validated regression models (2D and 3D QSAR and ligand-based pharmacophore mapping studies) were utilized for estimating the potency whereas classification models (Bayesian and recursive partitioning analyses) were used for determining the selectivity of MMP-2 inhibitors over MMP-9. Bayesian model fingerprints were used to design selective lead molecule which was modified using structure-based de novo technique. A series of designed molecules were prepared and screened initially for inhibitions of MMP-2 and MMP-9, respectively, as these are designed followed by other MMPs to observe the broader selectivity. The best active MMP-2 inhibitor had IC50value of 24 nM whereas the best selective inhibitor (IC50 = 51 nM) showed at least 4 times selectivity to MMP-2 against all tested MMPs. Active derivatives were non-cytotoxic against human lung carcinoma cell line—A549. At non-cytotoxic concentrations, these inhibitors reduced intracellular MMP-2 expression up to 78% and also exhibited satisfactory anti-migration and anti-invasive properties against A549 cells. Some of these active compounds may be used as adjuvant therapeutic agents in lung cancer after detailed study. © 2016 Elsevier Ltd

The possibility of linkage isomerism in a number of vanadium(IV) and vanadium(V) complexes with acetate was surveyed using Density Functional Theory (DFT) and Bader's Quantum Theory of Atoms in Molecules (QTAIM). The results show that vanadium-acetate linkages may be classified as bidentate symmetrical, bidentate asymmetrical, or monodentate, the latter being observed in about 40% of the cases. These latter ones correspond to situations where the two oxygen atoms of the acetate moiety are not equivalent. They are associated with an energy penalty of about 263 kJ.mol(-1), as determined by the distribution of the scaled kinetic energy of the atomic basins forming the acetate ligand. In the presence of bidentate symmetrical vanadium acetate linkages, the inner valence-shell charge concentrations on the vanadium atom deviate from the-traditional VSEPR-derived arrangement, with an energy penalty of about 780 kJ.mol(-1). A compromise situation is partially accomplished in the case of bidentate asymmetrical linkages, which allow a Gillespiean-like arrangement of the inner valence-shell charge concentrations. In this case, one of these local charge concentrations lies close to a V-O-ACO bond, which slightly disrupts the equivalence between the two oxygen atoms in the acetate ligand.

Recent simulations have improved our knowledge of the molecular-level structure and hydration properties of mixed self-assembled monolayers (SAMs) with equal and unequal alkyl thiols at three different arrangements, namely, random, patchy, and Janus. In our previous work [V. Vasumathi et al., J. Phys. Chem. C 119, 3199-3209 (2015)], we showed that the bending of longer thiols over shorter ones clearly depends on the thiols' arrangements and chemical nature of their terminal groups. In addition, such a thiol bending revealed to have a strong impact on the structural and hydration properties of SAMs coated on gold nanoparticles (AuNPs). In this paper, we extend our previous atomistic simulation study to investigate the bending of longer thiols by increasing the stripe thickness of mixed SAMs of equal and unequal lengths coated on AuNPs. We study also the effect of stripe thickness on the structural morphology and hydration of the coated SAMs. Our results show that the structural and hydration properties of SAMs are affected by the stripe thickness for mixtures of alkyl thiols with unequal chain length but not for equal length. Hence, the stability of the stripe configuration depends on the alkyl's chain length, the length difference between the thiol mixtures, and solvent properties. Published by AIP Publishing.