MODELING THE EVOLUTION OF COMPLEX NETWORKS THROUGH THE PATH-STAR TRANSFORMATION AND OPTIMAL MULTIVARIATE METHODS

The topology of real-world complex networks, such as in transportation and communication, is always changing with time. Such changes can arise not only as a natural consequence of their growth, but also due to major modi. cations in their intrinsic organization. For instance, the network of transportation routes between cities and towns ( hence locations) of a given country undergo a major change with the progressive implementation of commercial air transportation. While the locations could be originally interconnected through highways ( paths, giving rise to geographical networks), transportation between those sites progressively shifted or was complemented by air transportation, with scale free characteristics. In the present work we introduce the path-star transformation ( in its uniform and preferential versions) as a means to model such network transformations where paths give rise to stars of connectivity. It is also shown, through optimal multivariate statistical methods (i.e. canonical projections and maximum likelihood classification) that while the US highways network adheres closely to a geographical network model, its path-star transformation yields a network whose topological properties closely resembles those of the respective airport transportation network.

Modeling worldwide highway networks

This Letter addresses the problem of modeling the highway systems of different countries by using complex networks formalism. More specifically, we compare two traditional geographical models with a modified geometrical network model where paths, rather than edges, are incorporated at each step between the origin and the destination vertices. Optimal configurations of parameters are obtained for each model and used for the comparison. The highway networks of Australia, Brazil, India, and Romania are considered and shown to be properly modeled by the modified geographical model. (C) 2009 Elsevier B.V. All rights reserved.

Modeling connectivity in terms of network activity

A new complex network model is proposed which is founded on growth, with new connections being established proportionally to the current dynamical activity of each node, which can be understood as a generalization of the Barabasi-Albert static model. By using several topological measurements, as well as optimal multivariate methods (canonical analysis and maximum likelihood decision), we show that this new model provides, among several other theoretical kinds of networks including Watts-Strogatz small-world networks, the greatest compatibility with three real-world cortical networks.

On the efficiency of data representation on the modeling and characterization of complex networks

Specific choices about how to represent complex networks can have a substantial impact on the execution time required for the respective construction and analysis of those structures. In this work we report a comparison of the effects of representing complex networks statically by adjacency matrices or dynamically by adjacency lists. Three theoretical models of complex networks are considered: two types of Erdos-Renyi as well as the Barabasi-Albert model. We investigated the effect of the different representations with respect to the construction and measurement of several topological properties (i.e. degree, clustering coefficient, shortest path length, and betweenness centrality). We found that different forms of representation generally have a substantial effect on the execution time, with the sparse representation frequently resulting in remarkably superior performance. (C) 2011 Elsevier B.V. All rights reserved.

Modeling Adsorption Processes of Poly-p-phenylenevinylene Precursor and Sodium Acid Dodecylbenzenesulfonate onto Layer-by-Layer Films Using a Langmuir-type Metastable Equilibrium Model

The adsorption kinetics curves of poly(xylylidene tetrahydrothiophenium chloride) (PTHT), a poly-p-phenylenevinylene (PPV) precursor, and the sodium salt of dodecylbenzene sulfonic acid (DBS), onto (PTHT/DBS)(n) layer-by-layer (LBL) films were characterized by means of UV-vis spectroscopy. The amount of PTHT/DBS and PTHT adsorbed on each layer was shown to be practically independent of adsorption time. A Langmuir-type metastable equilibrium model was used to adjust the adsorption isotherms data and to estimate adsorption/desorption coefficients ratios, k = k(ads)/k(des), values of 2 x 10(5) and 4 x 10(6) for PTHT and PTHT/DBS layers, respectively. The desorption coefficient has been estimated, using literature values for poly(o-methoxyaniline) desorption coefficient, as was found to be in the range of 10(-9) to 10(-6) s(-1), indicating that quasi equilibrium is rapidly attained.

Role of Halogen Bonds in Thyroid Hormone Receptor Selectivity: Pharmacophore-Based 3D-QSSR Studies

Most physiological effects of thyroid hormones are mediated by the two thyroid hormone receptor subtypes, TR alpha and TR beta. Several pharmacological effects mediated by TR beta might be beneficial in important medical conditions such as obesity, hypercholesterolemia and diabetes, and selective TR beta activation may elicit these effects while maintaining an acceptable safety profile, To understand the molecular determinants of affinity and subtype selectivity of TR ligands, we have successfully employed a ligand- and structure-guided pharmacophore-based approach to obtain the molecular alignment of a large series of thyromimetics. Statistically reliable three-dimensional quantitative structure-activity relationship (3D-QSAR) and three-dimensional quantitative structure-selectivity relationship (3D-QSSR) models were obtained using the comparative molecular field analysis (CoMFA) method, and the visual analyses of the contour maps drew attention to a number of possible opportunities for the development of analogs with improved affinity and selectivity. Furthermore, the 3D-QSSR analysis allowed the identification of a novel and previously unmentioned halogen bond, bringing new insights to the mechanism of activity and selectivity of thyromimetics.

Cross-situational learning of object-word mapping using Neural Modeling Fields

The issue of how children learn the meaning of words is fundamental to developmental psychology. The recent attempts to develop or evolve efficient communication protocols among interacting robots or Virtual agents have brought that issue to a central place in more applied research fields, such as computational linguistics and neural networks, as well. An attractive approach to learning an object-word mapping is the so-called cross-situational learning. This learning scenario is based on the intuitive notion that a learner can determine the meaning of a word by finding something in common across all observed uses of that word. Here we show how the deterministic Neural Modeling Fields (NMF) categorization mechanism can be used by the learner as an efficient algorithm to infer the correct object-word mapping. To achieve that we first reduce the original on-line learning problem to a batch learning problem where the inputs to the NMF mechanism are all possible object-word associations that Could be inferred from the cross-situational learning scenario. Since many of those associations are incorrect, they are considered as clutter or noise and discarded automatically by a clutter detector model included in our NMF implementation. With these two key ingredients - batch learning and clutter detection - the NMF mechanism was capable to infer perfectly the correct object-word mapping. (C) 2009 Elsevier Ltd. All rights reserved.

CoMFA and CoMSIA 3D QSAR Models for a Series of Cyclic Imides with Analgesic Activity

Three-dimensional quantitative structure-activity relationships (3D-QSAR) were performed for a series of analgesic cyclic imides using the CoMFA and CoMSIA methods. Significant correlation coefficients ( CoMFA, r(2) = 0.95 and q(2) = 0.72; CoMSIA, r(2) = 0.96 and q(2) = 0.76) were obtained, and the generated models were externally validated using test sets. The final QSAR models as well as the information gathered from 3D contour maps should be useful for the design of novel cyclic imides having improved analgesic activity.

Structure-Based Approach for the Study of Estrogen Receptor Binding Affinity and Subtype Selectivity

Estrogens exert important physiological effects through the modulation of two human estrogen receptor (hER) subtypes, alpa (hER alpha) and beta (hER beta). Because the levels and relative proportion of hER alpha and hER beta differ significantly in different target cells, selective hER ligands could target specific tissues or pathways regulated by one receptor subtype without affecting the other. To understand the structural and chemical basis by which small molecule modulators are able to discriminate between the two subtypes, we have applied three-dimensional target-based approaches employing a series of potent hER-ligands. Comparative molecular field analysis (CoMFA) studies were applied to a data set of 81 hER modulators, for which binding affinity values were collected for both hER alpha and hER beta. Significant statistical coefficients were obtained (hER alpha, q(2) = 0.76; hER beta, q(2) = 0.70), indicating the internal consistency of the models. The generated models were validated using external test sets, and the predicted values were in good agreement with the experimental results. Five hER crystal structures were used in GRID/PCA investigations to generate molecular interaction fields (MIF) maps. hER alpha and hER beta were separated using one factor. The resulting 3D information was integrated with the aim of revealing the most relevant structural features involved in hER subtype selectivity. The final QSAR and GRID/PCA models and the information gathered from 3D contour maps should be useful for the design or novel hER modulators with improved selectivity.

Two-dimensional QSAR studies on arylpiperazines as high-affinity 5-HT(1A) receptor ligands

5-HT(1A) receptor plays an important role in the delayed onset of antidepressant action of a class of selective serotonin reuptake inhibitors. Moreover, 5-HT(1A) receptor levels have been shown to be altered in patients suffering from major depression. In this work, hologram quantitative structure-activity relationship (HQSAR) studies were performed on a series of arylpiperazine compounds presenting affinity to the 5-HT(1A) receptor. The models were constructed with a training set of 70 compounds. The most significant HQSAR model (q(2) = 0.81, r(2) = 0.96) was generated using atoms, bonds, connections, chirality, and donor and acceptor as fragment distinction, with fragment size of 6-9. Predictions for an external test set containing 20 compounds are in good agreement with experimental results showing the robustness of the model. Additionally, useful information can be obtained from the 2D contribution maps.

Quantitative structure-activity studies on a series of migrastatin analogs as inhibitors of cancer cell metastasis

Migrastatin, a macrolide natural product, and its structurally related analogs are potent inhibitors of cancer cell metastasis, invasion and migration. In the present work, a specialized fragment-based method was employed to develop QSAR models for a series of migrastatin and isomigrastatin analogs. Significant correlation coefficients were obtained (best model, q(2) = 0.76 and r(2) = 0.91) indicating that the QSAR models possess high internal consistency. The best model was then used to predict the potency of an external test set, and the predicted values were in good agreement with the experimental results (R(2) (pred) = 0.85). The final model and the corresponding contribution maps, combined with molecular modeling studies, provided important insights into the key structural features for the anticancer activity of this family of synthetic compounds based on natural products.

Classical and hologram QSAR studies on a series of inhibitors of trypanosomatid Glyceraldehyde-3-Phosphate Dehydrogenase

Leishmaniasis and trypanosomiasis are major causes of morbidity and mortality in both tropical and subtropical regions of the world. The current available drugs are limited, ineffective, and require long treatment regimens. Due to the high dependence of trypanosomatids on glycolysis as a source of energy, some glycolytic enzymes have been identified as attractive targets for drug design. In the present work, classical Two-Dimensional Quantitative Structure -Activity Relationships (2D QSAR) and Hologram QSAR (HQSAR) studies were performed on a series of adenosine derivatives as inhibitors of Leishmania mexicana Glyceraldehyde-3-Phosphate Dehydrogenase (LmGAPDH). Significant correlation coefficients (classical QSAR, r(2)=0.83 and q(2) =0.81; HQSAR, r(2)=0.91 and q(2) =0.86) were obtained for the 56 training set compounds, indicating the potential of the models for untested compounds. The models were then externally validated using a test set of 14 structurally related compounds and the predicted values were in good agreement with the experimental results (classical QSAR, r(pred)(2) = 0.94; HQSAR, r(pred)(2) = 0.92).

Structural modeling and mutational analysis of yeast eukaryotic translation initiation factor 5A reveal new critical residues and reinforce its involvement in protein synthesis

Eukaryotic translation initiation factor 5A (eIF5A) is a protein that is highly conserved and essential for cell viability. This factor is the only protein known to contain the unique and essential amino acid residue hypusine. This work focused on the structural and functional characterization of Saccharomyces cerevisiae eIF5A. The tertiary structure of yeast eIF5A was modeled based on the structure of its Leishmania mexicana homologue and this model was used to predict the structural localization of new site-directed and randomly generated mutations. Most of the 40 new mutants exhibited phenotypes that resulted from eIF-5A protein-folding defects. Our data provided evidence that the C-terminal alpha-helix present in yeast eIF5A is an essential structural element, whereas the eIF5A N-terminal 10 amino acid extension not present in archaeal eIF5A homologs, is not. Moreover, the mutants containing substitutions at or in the vicinity of the hypusine modification site displayed nonviable or temperature-sensitive phenotypes and were defective in hypusine modification. Interestingly, two of the temperature-sensitive strains produced stable mutant eIF5A proteins - eIF5A(K56A) and eIF5A(Q22H,L93F)- and showed defects in protein synthesis at the restrictive temperature. Our data revealed important structural features of eIF5A that are required for its vital role in cell viability and underscored an essential function of eIF5A in the translation step of gene expression.

Structural modeling of high-affinity thyroid receptor-ligand complexes

Understanding the molecular basis of the binding modes of natural and synthetic ligands to nuclear receptors is fundamental to our comprehension of the activation mechanism of this important class of hormone regulated transcription factors and to the development of new ligands. Thyroid hormone receptors (TR) are particularly important targets for pharmaceuticals development because TRs are associated with the regulation of metabolic rates, body weight, and circulating levels of cholesterol and triglycerides in humans. While several high-affinity ligands are known, structural information is only partially available. In this work we obtain structural models of several TR-ligand complexes with unknown structure by docking high affinity ligands to the receptors` ligand binding domain with subsequent relaxation by molecular dynamics simulations. The binding modes of these ligands are discussed providing novel insights into the development of TR ligands. The experimental binding free energies are reasonably well-reproduced from the proposed models using a simple linear interaction energy free-energy calculation scheme.

Inhibition of Mycobacterium tuberculosis tyrosine phosphatase PtpA by synthetic chalcones: Kinetics, molecular modeling, toxicity and effect on growth

Tuberculosis (TB) is a major cause of morbidity and mortality throughout the world, and it is estimated that one-third of the world`s population is infected with Mycobacterium tuberculosis. Among a series of tested compounds, we have recently identified five synthetic chalcones which inhibit the activity of M. tuberculosis protein tyrosine phosphatase A (PtpA), an enzyme associated with M. tuberculosis infectivity. Kinetic studies demonstrated that these compounds are reversible competitive inhibitors. In this work we also carried out the analysis of the molecular recognition of these inhibitors on their macromolecular target, PtpA, through molecular modeling. We observed that the predominant determinants responsible for the inhibitory activity of the chalcones are the positions of the two methoxyl groups at the A-ring, that establish hydrogen bonds with the amino acid residues Arg17, His49, and Thr12 in the active site of PtpA, and the substitution of the phenyl ring for a 2-naphthyl group as B-ring, that undergoes p stacking hydrophobic interaction with the Trp48 residue from PtpA. Interestingly, reduction of mycobacterial survival in human macrophages upon inhibitor treatment suggests their potential use as novel therapeutics. The biological activity, synthetic versatility, and low cost are clear advantages of this new class of potential tuberculostatic agents. (C) 2010 Elsevier Ltd. All rights reserved.