Interest in gas shale, a novel source rock of natural gas, has increased tremendously in recent years. Better understanding of the kerogen-rock interaction is of crucial importance for efficient gas extraction and, hence, asset management. In this study, we explore the possible chemical bonds between kerogen and silica, one of the most predominant mineral constituents of gas shale, by means of quantum chemistry. Energetically favorable bond formation reactions are found between alcoholic hydroxyl, carboxylate, and aldehyde groups, as well as aliphatic double bonds of kerogen and the silica surface. The performance of a reactive force field was also assessed in a representative set of chemical reactions and found to be satisfactory. The potential impact of bond formation reactions between the two phases on the actual kerogen-silica interface is discussed as a function of the kerogen type, maturity, and density. Finally, a methodology aiming to reconstruct realistic kerogen-silica interfaces is presented.

Human immunodeficiency virus (HIV) is the responsible causal agent of acquired immunodeficiency syndrome (AIDS), a condition in humans where the immune system begins to fail, permitting the entry of diverse opportunistic infections. Until now, there is currently no available vaccine or cure for HIV or AIDS. Thus, the search for new anti-HIV therapies is a very active area. The viral infection takes place through a phenomenon called entry process, and proteins known as gp120, CCR5 and CXCR4 are essential for the prevention of the HIV entry. Bioinformatics has emerged as a powerful science to provide better understanding of biochemical or biological processes or phenomena, where 3D-QSAR methodologies and molecular modeling techniques have served as strong support. The present review is focused on the 3D-QSAR methodologies and molecular modeling techniques as parts of Bioinformatics for the rational design of entry inhibitors. Also, we propose here, a chemo-bioinformatic approach which is based on a model using substructural descriptors and allowing the prediction of multi-target (mt) inhibitors against five proteins related with the HIV entry process. By employing the model we calculated the quantitative contributions of some fragments to the inhibitory activity against all the proteins. This allowed us to automatically extract the desirable fragments for design of new, potent and versatile entry inhibitors.

The NO dissociation on a series of doped gold surfaces (type TMn@Au(111) or TMn@Au(110), with TMn = Ni, Ir, Rh, or Ag and referring n to the number of dopant atoms per unit cell) was investigated through periodic density functional theory calculations. Generally, doping of Au(111) and Au(110) matrices was found to strengthen the interaction with NO species, with the exception of Ag, and was found to increase the energy barrier for dissociation, with the exception of Ni on Au(111). The calculations suggest that the NO dissociation is only possible in the case of the Ir@Au(110) bimetallic surface but only at high temperatures. The increase of the contents of Ir on Au(110) was found to improve significantly the catalytic activity of gold towards the NO dissociation (E-act = similar to 1 eV). Nevertheless, this energy barrier is almost the double of that calculated for NO dissociation on pure Ir(110). However, mixing the two most interesting dopant atoms resulted in a catalyst model of the type Ir@Ni(110) that was found to decrease the energy barrier to values close to those calculated for pure Ir surfaces, i.e., similar to 0.4 eV, and at the same time the dissociation reaction became mildly exothermic. (C) 2013 American Institute of Physics. [http://dx.doi.org/10.1063/1.4790602]

The aza-Diels-Alder reaction of cyclopentadiene with protonated propynylimine of methyl (Me) and tert-butyl (t-Bu) carboxylate was studied, using density functional theory (DFT) at the X3LYP/6-31G (d) level, in order to elucidate the role of the ester group of the dienophile. Four independent reaction pathways were found for each diene/dienophile pair, all of them proceeding through a concerted, highly asynchronous, mechanism. Both systems exhibit a clear tendency for the formation of exo adducts and show the same overall behavior, despite steric hindrances. The exo cycloadducts are always favored relative to the endo analogs, both by kinetic and thermodynamic reasons. Gas-phase results showed that the exo/endo rate would be mostly controlled by the relative thermodynamic stability of the final products. However, the application of the polarizable continuum model (PCM) preferentially lowered the activation enthalpy of the reaction exo channels, stressing the importance of the kinetic over the thermodynamic aspects of the proposed mechanism. This resulted in a reaction model that satisfies the experimental evidence.

We introduce the novel concept of an intrinsic free energy profile, allowing one to remove the artificial smearing caused by thermal capillary waves, which renders difficulties for the calculation of free energy profiles across fluid interfaces in computer simulations. We apply this concept to the problem of a chloride ion crossing the interface between water and 1,2-dichloroethane and show that the present approach is able to reveal several important features of the free energy profile which are not detected with the usual, nonintrinsic calculations. Thus, in contrast to the nonintrinsic profile, a free energy barrier is found at the aqueous side of the (intrinsic) interface, which is attributed to the formation of a water "finger" the ion pulls with itself upon approaching the organic phase. Further, by the presence of a nonsampled region, the intrinsic free energy profile clearly indicates the coextraction of the first hydration shell water molecules of the ion when entering the organic phase.

The identification of sialylated Thomsen-Friedenreich antigens in proteins poses much interest in the context of cancer research. MALDI-TOF-MS is a powerful technique for this purpose; still it shows considerable low sensitivity for sialylated molecules due to in-source and metastable decomposition. Herein, we report a target-driven strategy to identify these antigens in minute amounts of glycoproteins isolated in polyacrylamide gels. The glycans were recovered from gel spots by reductive -elimination, permethylated and analyzed by nano-LC-MALDI-TOF-MS. A computational algorithm was developed to filter spectral noise and enhance/isolate the signals of interest. Sialylated antigens were identified in minute amounts of fetuin (0.1 g) and plasminogen (1.0 g) by this approach.MS assignments were further validated by enzymatic methods. This methodology allowed a fivefold decrease in the current LOD of fetuin sialylated O-glycans by MALDI-TOF-MS.

Streptococci are a group of Gram-positive bacteria which are responsible for causing many diverse diseases in humans and other animals worldwide. The high prevalence of resistance of these bacteria to current antibacterial drugs is an alarming problem for the scientific community. The battle against streptococci by using antimicrobial chemotherapies will depend on the design of new chemicals with high inhibitory activity, having also as low toxicity as possible. Multi-target approaches based on quantitative-structure activity relationships (mt-QSAR) have played a very important role, providing a better knowledge about the molecular patterns related with the appearance of different pharmacological profiles including antimicrobial activity. Until now, almost all mt-QSAR models have considered the study of biological activity or toxicity separately. In the present study, we develop by the first time, a unified multitasking (mtk) QSAR model for the simultaneous prediction of anti-streptococci activity and toxic effects against biological models like Mus musculus and Rattus norvegicus. The mtk-QSAR model was created by using artificial neural networks (ANN) analysis for the classification of compounds as positive (high biological activity and/or low toxicity) or negative (otherwise) under diverse sets of experimental conditions. Our mtk-QSAR model, correctly classified more than 97% of the cases in the whole database (more than 11,500 cases), serving as a promising tool for the virtual screening of potent and safe anti-streptococci drugs.

Enterococci are dangerous opportunistic pathogens which are responsible of a huge number of nosocomial infections, displaying intrinsic resistance to many antibiotics. The battle against enterococci by using antimicrobial chemotherapies will depend on the design of new antibacterial agents with high activity and low toxicity. Multi-target methodologies focused on quantitative-structure activity relationships (mt-QSAR), have contributed to rationalize the process of drug discovery, improving the knowledge about the molecular patterns related with antimicrobial activity. Until know, almost all mt-QSAR models have considered the study of biological activity or toxicity separately. Here, we developed a unified mtk-QSBER (multitasking quantitative-structure biological effect relationships) model for simultaneous prediction of anti-enterococci activity and toxicity on laboratory animal and human immune cells. The mtk-QSBER model was created by using artificial neural network (ANN) analysis combined with topological indices, with the aim of classifying compounds as positive (high biological activity and/or low toxicity) or negative (otherwise) under multiple experimental conditions. The mtk-QSBER model correctly classified more than 90% of the whole dataset (more than 10900 cases). We used the model to predict multiple biological effects of the investigational drug BC-3781. Results demonstrate that our mtk-QSBER may represent a new horizon for the discovery of desirable anti-enterococci drugs.