This paper shows an iterative clustering method based on kernel k-means, which changes the parameter k automatically in each iteration of the algorithm. In addition, a way to initialize the centroids is proposed. The method is applied to a binning process in metagenomics using a complex database with different organisms. The aim of this method is to reduce the sensitivity of clusters based on strength measures. The results demonstrate that the proposed method is better than the simple kernel k-means for metagenome databases.

The growing concern due to the presence of plastics, especially micro and nanoplastics, in environmental aquatic media requires the development of new methodologies to study the distribution of these particles and the effects that might cause in many organisms. In this work we have performed experiments using synthetic polystyrene microplastics (6-90 µm diameter) and mussels (Mytilus galloprovincialis) and we have studied the distribution of these particles by different techniques including FTIR and Raman spectroscopy, light and polarized light microscopy after being exposed for different periods of time (1-72 h). As a result of this work we were able to fine tune the preparation of the samples, from conservation to image and spectra analysis, and it was concluded that it was better to freeze the samples and to prepare the cryosections instead of embedding in paraffin. Regarding the light microscopy darkfield illumination offered less background signals than polarized one and therefore it was more suitable for small size particles. Finally, Raman spectroscopy allowed the characterization of the polystyrene particles better than FTIR allowing the development of image analysis techniques.

2-Nitromethylacrylates have proved to be suitable 1,3-dinucleophiles reacting with α,β-unsaturated aldehydes in the presence of a secondary-amine catalyst to furnish Michael/Henry cascade products in moderate yields and with high enantioselectivities although with moderate diastereoselectivities. The reaction proceeds by iminium ion activation of the enal, which reacts regioselectively with the γ-carbon of the nitronate anion formed in situ, furnishing the desired cyclohexenes with three new stereocenters. Furthermore, and trying to avoid the diastereoselectivity issue, an efficient sequential Michael/Henry/dehydration reaction has been developed leading to enantiopure cyclohexadienes in moderate yields and excellent enantioselectivities.

Virtual methodologies have become essential components of the drug discovery pipeline. Specifically, structure-based drug design methodologies exploit the 3D structure of molecular targets to discover new drug candidates through molecular docking. Recently, dual target ligands of the Adenosine A2A Receptor and Monoamine Oxidase B enzyme have been proposed as effective therapies for the treatment of Parkinson’s disease. To the best of our knowledge, no theoretical study has been devoted to developing structure-based virtual screening methodologies for the discovery of dual A_2AAR antagonists and MAO-B inhibitors. In this communication we propose a structure-based methodology for the discovery this type of molecules

Objective: To draw a representing consensus sequence of each DENV serotype, align all four consensus sequences to draw a global consensus sequence and also study the highly conserved
residues. Methods: A total of 376 DENV NS3 sequences, belonging to four serotypes, reported from all over the world were aligned to develop global consensus sequence. Results: The active site residues Met343, Thr366, which are involved in nuclear localization and also interact with the NS3 viral, are highly conserved among all the DENV serotypes. Cys450, Gly466 and Ala468, Arg482 are highly conserved in all the serotypes. Structural zinc (Zn1) sited consist of Cys- 446, Cys-449, His-441, and the carboxylate group of Glu-437. This pocket is also found near the functionally important residues Ser-710 and Arg-729, which bind to the incoming rNTP. Meth530, Thr543 Asp597, Glu616 and Arg659, Pro671 are structurally conserved in all serotypes. Identification of four out of six conserved sequence motifs accountable for NTP binding and GDD catalytic active site, located in the palm domain. Leu766, Ala776 residues have high conservancy in all serotypes are observed in consensus sequence analysis. The thumb domain also has Zinc binding site (Zn2) and is synchronized by His-712, His-714, Cys-728 of motif E, and Cys-847. Pharmacological blockage of cavity B could potentially lead to suppression of initiation of the viral RNA synthesis and/or inhibition of NS3/NS5 interaction. Thirteen different peptides from the highly conserved regions of DENV NS5 protein were drawn which can be used to develop peptidic inhibitors. Conclusions: In spite of a high mutation rate in DENV, the residues which are present in the Nuclear localization signal (NLS), Di-valet ion binding sites, NTP binding, GDD catalytic active site, Thumb domain, priming loop are highly conserved. These are target sites for the development of antiviral agents or peptide vaccines.

Virtual Screening methodologies have emerged as efficient alternatives for the discovery of new drug candidates. At the same time, ensemble methods are nowadays frequently used to overcome the limitations of employing a single model in ligand-based drug design. However, many applications of ensemble methods to this area do not consider important aspects related to both virtual screening and the modeling process. During the application of ensemble methods to virtual screening the proper validation of the models in virtual screening conditions is often neglected. Frequently no analysis is performed of the diversity of the ensemble members or no considerations regarding the applicability domain of the base model are made. In this research we propose a method employing genetic algorithms optimization for the generation of virtual screening tailored ensembles that address problems in the current applications of ensemble methods to virtual screening. The proposed methodology is successfully applied to the generation of ensemble models for the ligand-based virtual screening of dual target A2A adenosine receptor antagonists and MAO-B inhibitors as potential Parkinson’s disease therapeutics.

The pharmacokinetic properties of absorption, distribution, metabolism and excretion (ADME) play a crucial role in drug discovery and development, since many drug candidates fail due to an inappropriate pharmacokinetic profile. Cytochrome P450 (CYP) enzymes are predominantly involved in Phase 1 metabolism of xenobiotics. Thus, it is important to better understand and prognosticate substrate binding and inhibition of CYP450.The goal of this study was to obtain QSAR (Quantitative Structure-Activity Relationship) models to identify substrates and inhibitors of CYP3A4. The data sets were collected and curated from online available databases and literature. Several QSAR models were obtained and validated according to the recommendations of the Organization for Economic Co-operation Development (OECD). The combination of different descriptors and machine learning methods led to robust and predictive QSAR models with high coverage. The interpretation of developed models was performed using the predicted probability maps (PPM). These maps help to encode major structural fragments to classify compounds as inhibitors or not inhibitors of CYP3A4. In conclusion, the obtained models can reliably identify substrates and non-substrates, and inhibitors and non-inhibitors of CYP3A4, which is very important  in the early stages of the development of new drugs.

In this study the uptake of different organic pollutants, including musk fragrances (tonalide and galaxolide), polybrominated diphenyl ethers (PBDEs), perfluorocarboxylic acids (PFCAs), perfluorosulfonic acids (PFSAs) and perfluorosulfonamide (FOSA) by carrot samples was compared. The bioconcentration factors (BCFs), defined as ratio of the concentration in the dry plant tissue and the concentration in the compost amended soils, were compared and correlation with the water solubility of the target compounds was studied. A good correlation was obtained between the water solubility and the BCFs in the different plant tissues (carrot root peel, root core and leaves). Besides, while the target analytes with the lowest solubility (musk fragrances and PBDEs) tended to accumulate in the peel of the carrot, the most water soluble target analytes (the perfluorinated compounds) tended to translocate to the carrot leaves.

Recently the presence of phosphoinositides (PIPns) in the eukaryotic cell nucleoplasm and nuclear membrane has been described (1). We have studied the interaction of the nuclear proteins, histones, with these lipids in model membranes. Turbidimetric studies revealed that histones induce extensive aggregation of vesicles when containing phosphatidylinositol-4-phosphate (PtdIns(4)P) and in a smaller extent when containing phosphatidylinositol (PtdIns). Binding and aggregation events were visualized by confocal microscopy. Using isothermal calorimetry we determined that the binding of histone H1 to PtdIns(4)P was one order of magnitude higher than with PtdIns.

Intervesicular mixing of total lipids and inner monolayer lipids was found in vesicles containing PtdIns(4)P, but not on those containing PtdIns. Supporting our hypothesis about the role histones could play promoting fusion events.

Taking this together we suggest that a complex interplay of histones and phosphoinositides could be taking place in the nucleoplasm where histones could have an important role during the remodeling processes of the mitotic cell cycle.

1. Byrne, R. D., M. Garnier-Lhomme, K. Han, M. Dowicki, N. Michael, N. Totty, V. Zhendre, A. Cho, T. R. Pettitt, M. J. Wakelam, D. L. Poccia, and B. Larijani. 2007. PLCgamma is enriched on poly-phosphoinositide-rich vesicles to control nuclear envelope assembly. Cellular signalling 19:913-922.

Alzheimer's disease (AD) is the most prevalent form of dementia, and current indications show that twenty-nine million people live with AD worldwide, a figure expected rise exponentially over the coming decades. AD is characterize with several pathologies this disease, amyloid plaques, composed of the β-amyloid peptide and γ-amyloid peptide are hallmark neuropathological lesions in Alzheimer's disease brain. Indeed, a wealth of evidence suggests that β-amyloid is central to the pathophysiology of AD and is likely to play an early role in this intractable neurodegenerative disorder. For this reason, we developed a new QSAR (QSAR) model to discover new drugs. A public databases ChEMBL contain Big Data sets of inhibitors of β-secretase. We revised QSAR studies using method of Artificial Neural Network (ANN) in order to understand the essential structural requirement for binding with receptor for β-secretase inhibitors.