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.

Tris(8-hydroxyquinolinate) metallic complexes, Mq(3), are one of the most important classes of organic semiconductor materials. Herein, the nature of the chemical bond in Mq(3) complexes and its implications on their molecular properties were investigated by a combined experimental and computational approach. Various Mq(3) complexes, resulting from the alteration of the metal and substitution of the 8-hydroxyquinoline ligand in different positions, were prepared. The mer-/fac-isomerism in Mq(3) was explored by FTIR and NMR spectroscopy, evidencing that, irrespective of the substituent, mer- and fac-are the most stable molecular configurations of Al(III) and In(III) complexes, respectively. The relative M-ligand bond dissociation energies were evaluated experimentally by electrospray ionization tandem mass spectrometry (ESI-MS-MS), showing a non-monotonous variation along the group (Al > In > Ga). The results reveal a strong covalent character in M-ligand bonding, which allows for through-ligand electron delocalization, and explain the preferred molecular structures of Mq3 complexes as resulting from the interplay between bonding and steric factors. The mer-isomer reduces intraligand repulsions, being preferred for smaller metals, while the fac-isomer is favoured for larger metals where stronger covalent M-ligand bonds can be formed due to more extensive through-ligand conjugation mediated by metal "d" orbitals.

Medium-chain acyl-CoA dehydrogenase deficiency (MCADD) is the most common genetic disorder affecting the mitochondrial fatty acid beta-oxidation pathway. The mature and functional form of human MCAD (hMCAD) is a homotetramer assembled as a dimer of dimers (monomers A/B and C/D). Each monomer binds a FAD cofactor, necessary for the enzyme's activity. The most frequent mutation in MCADD results from the substitution of a lysine with a glutamate in position 304 of mature hMCAD (p.K329E in the precursor protein). Here, we combined in vitro and in silico approaches to assess the impact of the p.K329E mutation on the protein's structure and function. Our in silico results demonstrated for the first time that the p.K329E mutation, despite lying at the dimer dimer interface and being deeply buried inside the tetrameric core, seems to affect the tetramer surface, especially the beta-domain that forms part of the catalytic pocket wall. Additionally, the molecular dynamics data indicate a stronger impact of the mutation on the protein's motions in dimer A/B, while dimer C/D remains similar to the wild type. For dimer A/B, severe disruptions in the architecture of the pockets and in the FAD and octanoyl-CoA binding affinities were also observed. The presence of unaffected pockets (C/D) in the in silico studies may explain the decreased enzymatic activity determined for the variant protein (46% residual activity). Moreover, the in silico structural changes observed for the p.K329E variant protein provide an explanation for the structural instability observed experimentally, namely, the disturbed oligomeric profile, thermal stability, and conformational flexibility, with respect to the wild type.

This work reports a detailed study of the ability of linear and non-linear classification methods to estimate the estrogenic activities of a series of 55 natural estrogen-like isoflavonoid and diphenolic compounds. In doing so, we examined the use of linear discriminant analysis (LDA) and nonlinear support vector machines (SVMs) techniques along with feature selection algorithms. The structural characteristics of each of the studied compounds were calculated from the optimized molecular geometries. Both the LDA and SVMs models contain four descriptors, however, the SVMs model (total accuracy 89.1%) was found to be superior to the LDA model (total accuracy 80.0%). The analysis of molecular descriptors within our models provided essential insights towards a better understanding of the estrogenic mechanisms of natural estrogen-like phytoestrogens. Furthermore, the derived models can be applied in the future screening of other natural estrogen-like compounds.

Tumor suppressor protein p53 regulates the cell cycle and inhibits tumor growth. It is inactivated by mutation or binding with human double minute 2 (HDM2) protein. The HDM2 is a promising target for treatment of p53 protein related cancers. Molecular modeling techniques such as 2D-QSAR, pharmacophore mapping and 3D-QSAR analyses were performed on 155 structurally diverse HDM2 inhibitors to understand structural and physicochemical requirements for higher activity. The linear and spline 2D-QSAR models were developed through multiple linear regression and genetic functional algorithm methods. The 2D-QSAR models suggested that number of fluorine, chlorine, tertiary nitrogen atoms as well as donor feature, stereogenic centers and higher value of solvent accessible surface area are important features in defining activity. Monte Carlo method was applied to generate QSAR models that determined structural indicators (alerts) for increase or decrease of the biological activity. Ligand-based pharmacophore mapping showed importance of two hydrophobic, one hydrophobic aromatic, one ring aromatic and one donor features. The structure-based pharmacophore model demonstrated significance of two hydrophobic, one ring aromatic and two acceptor features. The pharmacophore (ligand) aligned structures were subjected to 3D-QSAR analyses. The structure-based pharmacophore was also used for pharmacophore restraint molecular docking to analyze ligand-receptor interactions and for adjudging predictability as well as validation of different modeling techniques. These comparative molecular modeling techniques may help to design novel HDM2 inhibitors. © 2014 Elsevier Masson SAS.

Methanol decomposition is one of the key reactions in direct methanol fuel cell (DMFC) state-of-the-art technology, research, and development. However, its mechanism still presents many uncertainties, which, if answered, would permit us to refine the manufacture of DMFCs. The mechanism of methanol decomposition on ruthenium surfaces was investigated using density functional theory and a periodic supercell approach. The possible pathways, involving either initial C-H, C-O or O-H scission, were defined from experimental evidence regarding the methanol decomposition on ruthenium and other metallic surfaces. The study yielded the O-H scission pathway as having both the most favorable energetics and kinetics. The computational data, which present a remarkable closeness with the experimental results, also indicate methanol adsorption, the starting point in all possible pathways, to be of weak nature, implying a considerable rate of methanol desorption from the ruthenium, compromising the reaction.

Aims: We introduce the first quantitative structure-activity relationship (QSAR) perturbation model for probing multiple antibacterial profiles of nanoparticles (NPs) under diverse experimental conditions. Materials & methods: The dataset is based on 300 nanoparticles containing dissimilar chemical compositions, sizes, shapes and surface coatings. In general terms, the NPs were tested against different bacteria, by considering several measures of antibacterial activity and diverse assay times. The QSAR perturbation model was created from 69,231 nanoparticle-nanoparticle (NP-NP) pairs, which were randomly generated using a recently reported perturbation theory approach. Results: The model displayed an accuracy rate of approximately 98% for classifying NPs as active or inactive, and a new copper-silver nanoalloy was correctly predicted by this model with consensus accuracy of 77.73%. Conclusion: Our QSAR perturbation model can be used as an efficacious tool for the virtual screening of antibacterial nanomaterials.