Molecular recognition of docosahexaenoic acid by peroxisome proliferator-activated receptors and retinoid-X receptor alpha

The family of peroxisome proliferator-activated receptors (PPARs) is the molecular target of synthetic antidiabetic and hypolipidemic drugs. The side effects of these drugs are limiting their use in patients with high lipid levels. Natural compounds, like Docosahexaenoic acid (DHA) from fish oil, have beneficial effects in the treatment of metabolic diseases, and several DHA derivatives are known to activate PPAR genes. Experimental studies on affinities of DHA and its derivatives for PPARs are not available. In the present study we are therefore using computational docking, molecular dynamics simulation, and several scoring programs to predict affinities and binding modes of DHA for PPARs and retinoid-X receptor alpha, which is the DNA binding partner of PPARs. The calculations indicated that DHA binds to PPARs and the retinoid-X receptor alpha with high affinity, and that different PPARs exhibited different structural effects on the first four carbons atoms of DHA. Our data indicate that the beneficial health effects of DHA may be obtained by high affinity binding to the PPARs.     

Interaction between peroxisome proliferator-activated receptor gamma and its agonists: docking study of oximes having 5-benzyl-2,4-thiazolidinedione

The molecular modelling of oximes having 5-benzyl-2,4-thiazolidinedione moieties, agonists of the peroxisome proliferator-activated receptor gamma (PPAR gamma), was performed with respect to their structures complexed with the ligand binding domain of PPAR gamma. For each ligand molecule, the 5-benzyl-2,4-thiazolidinedione head group was used as an anchor and the conformation of the rest of the molecule was searched for the most energetically favorable interaction with the receptor by systematic conformation search and manual modelling. Although both tail-up and tail-down configurations, which have been observed in the crystal structure of eicosapentaenoic acid when complexed with PPAR delta, appeared among the lowest energy structures for most of the compounds, potent agonists were found to adopt a configuration similar to that of rosiglitazone when bound to PPAR gamma, according to the crystal structure. The structure-activity relationships were analyzed based on the receptor-ligand interaction. The alkyl group and the aromatic ring of the tail group of the ligands had hydrophobic interactions with the receptor, and these interactions were found to be essential for the strong activity.     

A single amino acid change humanizes long-chain fatty acid binding and activation of mouse peroxisome proliferator-activated receptor α

Peroxisome proliferator-activated receptor α (PPARα) is an important regulator of hepatic lipid metabolism which functions through ligand binding. Despite high amino acid sequence identity (>90%), marked differences in PPARα ligand binding, activation and gene regulation have been noted across species. Similar to previous observations with synthetic agonists, we have recently reported differences in ligand affinities and extent of activation between human PPARα (hPPARα) and mouse PPARα (mPPARα) in response to long chain fatty acids (LCFA). The present study was aimed to determine if structural alterations could account for these differences. The binding of PPARα to LCFA was examined through in silico molecular modeling and docking simulations. Modeling suggested that variances at amino acid position 272 are likely to be responsible for differences in saturated LCFA binding to hPPARα and mPPARα. To confirm these results experimentally, LCFA binding, circular dichroism, and transactivation studies were performed using a F272I mutant form of mPPARα. Experimental data correlated with in silico docking simulations, further confirming the importance of amino acid 272 in LCFA binding. Although the driving force for evolution of species differences at this position are yet unidentified, this study enhances our understanding of ligand-induced regulation by PPARα and demonstrates the efficacy of molecular modeling and docking simulations.     

Prediction of the PPARα agonism of fibrates by combined MM-docking approaches

Fibrates are peroxisome proliferator-activated alpha receptor (PPARα) activators derived from fibric acid and are the most clinically used therapeutics in the treatment of hypertriglyceridemia. Long standing studies on these drugs have accumulated a large body of experimental data about their biological activity and, more recently, on the molecular mechanism mediating their PPARα agonism. An immense interest for the discovery of new fibrates with improved potency and PPARα selectivity has stimulated many investigations toward a deeper understanding of structure-activity relationships controlling their activity. The present study aimed at investigating the binding properties of a set of 23 fibrates, characterized by similar carboxylic heads but differing in the size and orientation of the hydrophobic portion, using computational approaches. We combined standard docking and molecular mechanics approaches to better describe the adaptation of the protein target to the bound ligand. The agonist potencies were then regressed against the calculated binding energies to elaborate predictive model equations. The obtained models were characterized by good performances realizing a fair trade-off between accuracy and computational costs. The best model was obtained with a regression procedure allowing automatic generation of a training subset from the whole set of trials and filtering out outliers, thus highlighting the importance of regression strategies.     

Agglomerative independent variable group analysis

Independent variable group analysis (IVGA) is a method for grouping dependent variables together while keeping mutually independent or weakly dependent variables in separate groups. In this paper two variants of an agglomerative method for learning a hierarchy of IVGA groupings are presented. The method resembles hierarchical clustering, but the choice of clusters to merge is based on variational Bayesian model comparison. This is approximately equivalent to using a distance measure based on a model-based approximation of mutual information between groups of variables. The approach also allows determining optimal cutoff points for the hierarchy. The method is demonstrated to find sensible groupings of variables that can be used for feature selection and ease construction of a predictive model.     

Terrain analysis from landsat using a color TV enhancement system

Visual interpretation of Landsat false-color composite imagery was employed to map terrain types on the Navajo Indian Reservation in northeastern Arizona and northwestern New Mexico. Three main difficulties were encountered in the visual analysis:(1) determination of the exact boundary positions; (2) repeatability of the boundary decisions over an area; and (3) discrimination among subtle tone differences associated with certain terrain type groupings. A simple image enhancement technique, based on a color television system was developed to make the visual analysis more accurate. A color TV camera was used to view the image and a color monitor used for the display. Image enhancement was achieved by use of the chroma, hue, contrast, and brightness controls on the monitor. The use of a zoom lens on the camera allowed rapid scale change and facilitated study of small areas of the image. By working directly from a color image, the technique obviated the need for separation positives with their attendant registration problems.     

Using edit distance to analyze card sorts

Abstract: Card sorts are a knowledge elicitation technique in which participants are given a collection of items and are asked to partition them into groups based on their own criteria. Information about the participant's knowledge structure is inferred from the groups formed and the names used to describe the groups through various methods ranging from simple quantitative statistical measures (e.g. co‐occurrence frequencies) to complex qualitative methods (e.g. content analysis on the group names). This paper introduces a new technique for analyzing card sort data that uses quantitative measures to discover rich qualitative results. This method is based upon a distance metric between sorts that allows one to measure the similarity of groupings and then look for clusters of closely related sorts across individuals. By using software for computing these clusters, it is possible to identify common concepts across individuals, despite the use of different terminology.     

A comparison of the behavior of different customer clusters towards Internet bookstores

This research was initiated in an attempt to understand customer intentions towards purchasing from an Internet bookstore. Ajzen's theory of planned behavior was used to predict the intentions and behavior of different customer groupings based on their lifestyle and personality. Factor and cluster analyses were used to segment the sample into three clusters. Data were then collected in seven colleges in Taiwan. After analyzing the results, various relationships between attitudes, subjective norms, perceived behavioral control, behavioral intention and real behavior were determined for the different groups.     

A Probabilistic Approach to Perceptual Grouping

We present a general framework for determining probability distributions over the space of possible image feature groupings. The framework can be used to find several of the most probable partitions of image features into groupings, rather than just returning a single partition of the features as do most feature grouping techniques. In addition to the groupings themselves, the probability of each partition is computed, providing information on the relative probability of multiple partitions that few grouping techniques offer. In determining the probability distribution of groupings, no parameters are estimated, thus eliminating problems that occur with small data sets and outliers such as the compounding of errors that can occur when parameters are estimated and the estimated parameters are used in the next grouping step. We have instantiated our framework for the two special cases of grouping line segments into straight lines and for grouping bilateral symmetries with parallel axes, where bilateral symmetries are formed by pairs of edges. Results are presented for these cases on several real images.     

Set restrictions for semantic groupings

Most research on semantic integrity has taken place in the traditional database fields, specifically the relational data model. Advanced models, such as semantic and object-oriented data models, have developed higher level abstractions to increase their expressive power in order to meet the needs of newly emerging application domains. This allows them to incorporate some semantic constraints directly into their schemas. There are, however, many types of restrictions that cannot be expressed solely by these high-level constructs. Therefore we extend the potential of advanced models by augmenting their abstractions with useful set restrictions. In particular, we identify and formulate four of their most common semantic groupings: set groupings, is-a related set groupings, power set groupings, and Cartesian product groupings. For each, we define a number of restrictions that control its structure and composition. We exploit the notion of object identity for the definition of these semantic restrictions. This permits each grouping to capture more subtle distinctions of the concepts in the application environment, as demonstrated by numerous examples throughout this paper. The resulting set of restrictions forms a general framework for integrity constraint management in advanced data models     

Uncertainty propagation and the matching of junctions as featuregroupings

The interpretation of the 3D world from image sequences requires the identification and correspondences of key features in the scene. We describe a robust algorithm for matching groupings of features related to the objects in the scene. We consider the propagation of uncertainty from the feature detection stage through the grouping stage to provide a measure of uncertainty at the matching stage. We focus upon indoor scenes and match junctions, which are groupings of line segments that meet at a single point. A model of the uncertainty in junction detection is described, and the junction uncertainty under the epipolar constraint is determined. Junction correspondence is achieved through matching of each line segment associated with the junction. A match likelihood is then derived based upon the detection uncertainties and then combined with information on junction topology to create a similarity measure. A robust matching algorithm is proposed and used to match junctions between pairs of images. The presented experimental results on real images show that the matching algorithm produces sufficiently reliable results for applications such as structure from motion     

A computational model for visual selection.

We propose a computational model for detecting and localizing instances from an object class in static gray-level images. We divide detection into visual selection and final classification, concentrating on the former: drastically reducing the number of candidate regions that require further, usually more intensive, processing, but with a minimum of computation and missed detections. Bottom-up processing is based on local groupings of edge fragments constrained by loose geometrical relationships. They have no a priori semantic or geometric interpretation. The role of training is to select special groupings that are moderately likely at certain places on the object but rate in the background. We show that the statistics in both populations are stable. The candidate regions are those that contain global arrangements of several local groupings. Whereas our model was not conceived to explain brain functions, it does cohere with evidence about the functions of neurons in V1 and V2, such as responses to coarse or incomplete patterns (e.g., illusory contours) and to scale and translation invariance in IT. Finally, the algorithm is applied to face and symbol detection.     

Getting it right: modeling of pH, solvent and "nearly" everything else in virtual screening of biological targets

“Getting it right” refers to the careful modeling of all elements in the living system, i.e. biological macromolecules, ligands and water molecules. In addition, careful attention should be paid to the protonation state of ionizable functional groups on the ligands and residues at the active site. Computational technology based on the empirical HINT program is described to: (1) calculate free energy scores for ligand binding; (2) include the implicit and explicit effects of water in and around the ligand binding site; and (3) incorporate the effects of global and local pH in molecular models. This last point argues for the simultaneous consideration of a number of molecular models, each with different protonation profiles. Data from recent studies of protein–ligand systems (trypsin, thrombin, neuraminidase, HIV-1 protease and others) are used to illustrate the concepts in the paper. Also discussed are experimental factors related to accurate free energy predictions with this and other computational technologies.     

Interaction between peroxisome proliferator-activated receptor γ and its agonists: docking study of oximes having 5-benzyl-2,4-thiazolidinedione

The molecular modelling of oximes having 5-benzyl-2,4-thiazolidinedione moieties, agonists of the peroxisome proliferator-activated receptor γ (PPARγ), was performed with respect to their structures complexed with the ligand binding domain of PPARγ. For each ligand molecule, the 5-benzyl-2,4-thiazolidinedione head group was used as an anchor and the conformation of the rest of the molecule was searched for the most energetically favorable interaction with the receptor by systematic conformation search and manual modelling. Although both tail-up and tail-down configurations, which have been observed in the crystal structure of eicosapentaenoic acid when complexed with PPARδ, appeared among the lowest energy structures for most of the compounds, potent agonists were found to adopt a configuration similar to that of rosiglitazone when bound to PPARγ, according to the crystal structure. The structure–activity relationships were analyzed based on the receptor–ligand interaction. The alkyl group and the aromatic ring of the tail group of the ligands had hydrophobic interactions with the receptor, and these interactions were found to be essential for the strong activity.     

Prevention of accidents and injuries in developing countries

Available data show that injuries at the workplace are a serious and growing problem in developing compared to industrialized countries. A major factor responsible for this state of affairs is the fact that largely illiterate and impoverished peasants are being transformed into wage-earning industrial workers. Others include hazardous working environments, effects of heat and humidity, inadequacy or absence of mechanization, and a multiplicity of small-scale enterprises. Preventive measures must be based on the understanding of these factors and their relative importance. Recommendations are made for accident and injury prevention in developing countries based on methods that have been tried and found effective in industrialized countries. Three approaches are advocated: (a) improvements at workplaces aimed at hazard elimination and a positive approach to health and safety at work; (b) national efforts (legislation and enforcement); and (c) international cooperation between industrialized and developing countries. The role of such bodies as the World Health Organisation (through its Global Accident Prevention Programme), the International Labour Organisation, and other regional groupings (e.g. The Organisation for African Unity) in the prevention of accidents and injuries at work the world over are highlighted.     

Molecular determinants of LXRalpha agonism

Liver X receptors (LXRs) are nuclear receptors that participate in the regulation of cholesterol, bile acid, and glucose metabolism. Despite the identification of the natural oxysterol and nonsteroidal ligands for LXRalpha, little is known about the structure of the LXRalpha ligand-binding domain (LBD). We constructed a three-dimensional (3D) homology model of the LBD of LXRalpha based on the crystal structure of the retinoic acid receptor gamma (RARgamma) and all-trans retinoic acid complex. We combined molecular modeling and classical structure-function techniques to define the interactions between the LBD and three structurally diverse ligands, 22(R)-hydroxycholesterol (22RHC), N-(2,2,2-trifluoro-ethyl)-N-[4-(2,2,2-trifluoro-1-hydroxy-1-trifluoromethyl-ethyl)-phenyl]-benzenesulfonamide (T0901317) and (3-[3-[(2-chloro-3-trifluoromethyl-benzyl)-(2,2-diphenyl-ethyl)-amino]-propoxy]-phenyl)-acetic acid (GW3965). Sixteen individual amino acid point mutations were made in the predicted ligand-binding cavity of the LBD, and each of these mutant receptors was assessed for their ability to be activated by these three ligands. The majority of individual mutations resulted in lack of activation by all three ligands. Two residues were identified that resulted in a significant increase in basal activity while retaining responsiveness to the ligands. Interestingly, a number of residues were identified that appear to be selective in their response to a particular ligand, indicating that these three ligands recognize distinct structural components within the ligand-binding cavity. These data, together with our docking study, enable us to identify the amino acids that coordinate the interaction of both steroidal and non-steroidal ligands in the ligand-binding pocket of LXRalpha.