p3d – Python module for structural bioinformatics

<p>Abstract</p> <p>Background</p> <p>High-throughput bioinformatic analysis tools are needed to mine the large amount of structural data via knowledge based approaches. The development of such tools requires a robust interface to access the structural data in an easy way. For this the Python scripting language is the optimal choice since its philosophy is to write an understandable source code.</p> <p>Results</p> <p>p3d is an object oriented Python module that adds a simple yet powerful interface to the Python interpreter to process and analyse three dimensional protein structure files (PDB files). p3d's strength arises from the combination of a) very fast spatial access to the structural data due to the implementation of a binary space partitioning (BSP) tree, b) set theory and c) functions that allow to combine a and b and that use human readable language in the search queries rather than complex computer language. All these factors combined facilitate the rapid development of bioinformatic tools that can perform quick and complex analyses of protein structures.</p> <p>Conclusion</p> <p>p3d is the perfect tool to quickly develop tools for structural bioinformatics using the Python scripting language.</p

VASCo: computation and visualization of annotated protein surface contacts

<p>Abstract</p> <p>Background</p> <p>Structural data from crystallographic analyses contain a vast amount of information on protein-protein contacts. Knowledge on protein-protein interactions is essential for understanding many processes in living cells. The methods to investigate these interactions range from genetics to biophysics, crystallography, bioinformatics and computer modeling. Also crystal contact information can be useful to understand biologically relevant protein oligomerisation as they rely in principle on the same physico-chemical interaction forces. Visualization of crystal and biological contact data including different surface properties can help to analyse protein-protein interactions.</p> <p>Results</p> <p>VASCo is a program package for the calculation of protein surface properties and the visualization of annotated surfaces. Special emphasis is laid on protein-protein interactions, which are calculated based on surface point distances. The same approach is used to compare surfaces of two aligned molecules. Molecular properties such as electrostatic potential or hydrophobicity are mapped onto these surface points. Molecular surfaces and the corresponding properties are calculated using well established programs integrated into the package, as well as using custom developed programs. The modular package can easily be extended to include new properties for annotation. The output of the program is most conveniently displayed in PyMOL using a custom-made plug-in.</p> <p>Conclusion</p> <p>VASCo supplements other available protein contact visualisation tools and provides additional information on biological interactions as well as on crystal contacts. The tool provides a unique feature to compare surfaces of two aligned molecules based on point distances and thereby facilitates the visualization and analysis of surface differences.</p

Levels and Concentration Ratios of Polychlorinated Biphenyls and Polybrominated Diphenyl Ethers in Serum and Breast Milk in Japanese Mothers

Blood and/or breast milk have been used to assess human exposure to various environmental contaminants. Few studies have been available to compare the concentrations in one matrix with those in another. The goals of this study were to determine the current levels of polybrominated diphenyl ethers (PBDEs) and polychlorinated biphenyls (PCBs) in Japanese women, with analysis of the effects of lifestyle and dietary habits on these levels, and to develop a quantitative structure–activity relationship (QSAR) with which to predict the ratio of serum concentration to breast milk concentration. We measured PBDEs and PCBs in 89 paired samples of serum and breast milk collected in four regions of Japan in 2005. The geometric means of the total concentrations of PBDE (13 congeners) in milk and serum were 1.56 and 2.89 ng/g lipid, respectively, whereas those of total PCBs (15 congeners) were 63.9 and 37.5 ng/g lipid, respectively. The major determinant of total PBDE concentration in serum and milk was the geographic area within Japan, whereas nursing duration was the major determinant of PCB concentration. BDE-209 was the most predominant PBDE congener in serum but not in milk. The excretion of BDE 209 in milk was lower than that of BDE 47 and BDE 153. QSAR analysis revealed that two parameters, calculated octanol/water partition and number of hydrogen-bond acceptors, were significant descriptors. During the first weeks of lactation, the predicted partitioning of PBDE and PCB congeners from serum to milk agreed with the observed values. However, the prediction became weaker after 10 weeks of nursing

Structural diversity of biologically interesting datasets: a scaffold analysis approach

ABSTRACT:The recent public availability of the human metabolome and natural product datasets has revitalized "metabolite-likeness" and "natural product-likeness" as a drug design concept to design lead libraries targeting specific pathways. Many reports have analyzed the physicochemical property space of biologically important datasets, with only a few comprehensively characterizing the scaffold diversity in public datasets of biological interest. With large collections of high quality public data currently available, we carried out a comparative analysis of current day leads with other biologically relevant datasets.In this study, we note a two-fold enrichment of metabolite scaffolds in drug dataset (42%) as compared to currently used lead libraries (23%). We also note that only a small percentage (5%) of natural product scaffolds space is shared by the lead dataset. We have identified specific scaffolds that are present in metabolites and natural products, with close counterparts in the drugs, but are missing in the lead dataset. To determine the distribution of compounds in physicochemical property space we analyzed the molecular polar surface area, the molecular solubility, the number of rings and the number of rotatable bonds in addition to four well-known Lipinski properties. Here, we note that, with only few exceptions, most of the drugs follow Lipinski's rule. The average values of the molecular polar surface area and the molecular solubility in metabolites is the highest while the number of rings is the lowest. In addition, we note that natural products contain the maximum number of rings and the rotatable bonds than any other dataset under consideration.Currently used lead libraries make little use of the metabolites and natural products scaffold space. We believe that metabolites and natural products are recognized by at least one protein in the biosphere therefore, sampling the fragment and scaffold space of these compounds, along with the knowledge of distribution in physicochemical property space, can result in better lead libraries. Hence, we recommend the greater use of metabolites and natural products while designing lead libraries. Nevertheless, metabolites have a limited distribution in chemical space that limits the usage of metabolites in library design.14 page(s

Study of the Interactions of Ionic Liquids in IC by QSRR

The nature of ionic liquids (ILs) facilitates their analysis by ion chromatography which, unlike conventional high-performance liquid chromatography, enables analysis both of cations and anions. This paper describes a pioneering ion-chromatographic investigation of IL cations and statistical evaluation of quantitative structure–retention relationships with the objective of predicting the molecular mechanism responsible for retention. Eleven ionic liquid imidazolium and pyridinium cations were analyzed on a CS15 cation-exchange column by isocratic elution with acetonitrile–methanesulfonic acid mixtures. Structural descriptors of the cations obtained from molecular modeling were used to describe their hydrophobicity as determined by chromatography. The most statistically significant were three-term QSRR regression equations relating log kw to analyte n-octanol–water partition coefficient (log P), dipole moment (μ), solvent accessible surface area (ASAS), and hydration energy (HE). They indicate the important role of both hydrophobic and polar the retention of ILs on the CS15 column

Physiochemical property space distribution among human metabolites, drugs and toxins

<p>Abstract</p> <p>Background</p> <p>The current approach to screen for drug-like molecules is to sieve for molecules with biochemical properties suitable for desirable pharmacokinetics and reduced toxicity, using predominantly biophysical properties of chemical compounds, based on empirical rules such as Lipinski's "rule of five" (Ro5). For over a decade, Ro5 has been applied to combinatorial compounds, drugs and ligands, in the search for suitable lead compounds. Unfortunately, till date, a clear distinction between drugs and non-drugs has not been achieved. The current trend is to seek out drugs which show metabolite-likeness. In identifying similar physicochemical characteristics, compounds have usually been clustered based on some characteristic, to reduce the search space presented by large molecular datasets. This paper examines the similarity of current drug molecules with human metabolites and toxins, using a range of computed molecular descriptors as well as the effect of comparison to clustered data compared to searches against complete datasets.</p> <p>Results</p> <p>We have carried out statistical and substructure functional group analyses of three datasets, namely human metabolites, drugs and toxin molecules. The distributions of various molecular descriptors were investigated. Our analyses show that, although the three groups are distinct, present-day drugs are closer to toxin molecules than to metabolites. Furthermore, these distributions are quite similar for both clustered data as well as complete or unclustered datasets.</p> <p>Conclusion</p> <p>The property space occupied by metabolites is dissimilar to that of drugs or toxin molecules, with current drugs showing greater similarity to toxins than to metabolites. Additionally, empirical rules like Ro5 can be refined to identify drugs or drug-like molecules that are clearly distinct from toxic compounds and more metabolite-like. The inclusion of human metabolites in this study provides a deeper insight into metabolite/drug/toxin-like properties and will also prove to be valuable in the prediction or optimization of small molecules as ligands for therapeutic applications.</p

Exchange rate volatility and capital inflows: role of financial development

There is vast literature examining the impact of exchange rate volatility on various macroeconomic aggregates such as economic growth, trade flows, domestic investment, and more recently capital flows. However, these studies have ignored the role of financial development while examining the impact of exchange rate volatility on capital flows. This study aims to analyze the impact of exchange rate volatility on capital inflows towards developing countries by incorporating the role of financial development over the time period 1980–2013. In this regard, the behavior of two types of capital flows is examined: physical capital inflows measured as foreign direct investment, and financial inflows quantified through remittance inflows. The empirical investigation comprises the direct as well as indirect effect of exchange rate volatility on capital inflows. The study employs dynamic system GMM estimation technique to empirically estimate the effect of exchange rate volatility on capital inflows. The empirical results of the study identify that exchange rate volatility dampens both physical and financial inflows towards developing countries. The indirect impact of exchange rate volatility through financial development, however, turns out positive and statistically significant. This finding reflects that financial development helps in reduc- ing the harmful impact of exchange rate volatility on capital inflows. Hence, the study concludes that a developed financial system is an important channel through which developing countries may improve capital inflows in the long run.info:eu-repo/semantics/publishedVersio

Challenges Predicting Ligand-Receptor Interactions of Promiscuous Proteins: The Nuclear Receptor PXR

Transcriptional regulation of some genes involved in xenobiotic detoxification and apoptosis is performed via the human pregnane X receptor (PXR) which in turn is activated by structurally diverse agonists including steroid hormones. Activation of PXR has the potential to initiate adverse effects, altering drug pharmacokinetics or perturbing physiological processes. Reliable computational prediction of PXR agonists would be valuable for pharmaceutical and toxicological research. There has been limited success with structure-based modeling approaches to predict human PXR activators. Slightly better success has been achieved with ligand-based modeling methods including quantitative structure-activity relationship (QSAR) analysis, pharmacophore modeling and machine learning. In this study, we present a comprehensive analysis focused on prediction of 115 steroids for ligand binding activity towards human PXR. Six crystal structures were used as templates for docking and ligand-based modeling approaches (two-, three-, four- and five-dimensional analyses). The best success at external prediction was achieved with 5D-QSAR. Bayesian models with FCFP_6 descriptors were validated after leaving a large percentage of the dataset out and using an external test set. Docking of ligands to the PXR structure co-crystallized with hyperforin had the best statistics for this method. Sulfated steroids (which are activators) were consistently predicted as non-activators while, poorly predicted steroids were docked in a reverse mode compared to 5α-androstan-3β-ol. Modeling of human PXR represents a complex challenge by virtue of the large, flexible ligand-binding cavity. This study emphasizes this aspect, illustrating modest success using the largest quantitative data set to date and multiple modeling approaches

Novel Naphthalene-Based Inhibitors of Trypanosoma brucei RNA Editing Ligase 1

African sleeping sickness is a devastating disease that plagues sub-Saharan Africa. Neglected tropical diseases like African sleeping sickness cause significant death and suffering in the world's poorest countries. Current treatments for African sleeping sickness either have high costs, terrible side effects, or limited effectiveness. Consequently, new medicines are urgently needed. RNA editing ligase 1 is an important protein critical for the survival of Trypanosoma brucei, the unicellular parasite that causes African sleeping sickness. In this paper, we describe our recent efforts to use advanced computer techniques to identify chemicals predicted to prevent RNA editing ligase 1 from functioning properly. We subsequently tested our predicted chemicals and confirmed that a number of them inhibited the protein's function. Additionally, one of the chemicals was effective at stopping the growth of the parasite in culture. Although substantial work remains to be done in order to optimize these chemicals so they are effective and safe to use in human patients, the identification of these parasite-killing compounds is nevertheless a valuable step towards finding a better cure for this devastating disease