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.

Acridone alkaloids as potent inhibitors of cathepsin V

Cathepsin V is a lysosomal cysteine peptidase highly expressed in thymus, testis and corneal epithelium. Eleven acridone alkaloids were isolated from Swinglea glutinosa (Bl.) Merr. (Rutaceae), with eight of them being identified as potent and reversible inhibitors of cathepsin V (IC(50) values ranging from 1.2 to 3.9 mu M). Detailed mechanistic characterization of the effects of these compounds on the cathepsin V-catalyzed reaction showed clear competitive inhibition with respect to substrate, with dissociation constants (K(i)) in the low micromolar range (2, K(i) = 1.2 mu M; 6, K(i) = 1.0 mu M; 7, K(i) = 0.2 mu M; and 11, K(i) = 1.7 mu M). Molecular modeling studies provided important insight into the structural basis for binding affinity and enzyme inhibition. Experimental and computational approaches, including biological evaluation, mode of action assessment and modeling studies were successfully employed in the discovery of a small series of acridone alkaloid derivatives as competitive inhibitors of catV. The most potent inhibitor (7) has a K(i) value of 200 nM. (C) 2011 Elsevier Ltd. All rights reserved.

A neural networks study of quinone compounds with trypanocidal activity

This work investigates neural network models for predicting the trypanocidal activity of 28 quinone compounds. Artificial neural networks (ANN), such as multilayer perceptrons (MLP) and Kohonen models, were employed with the aim of modeling the nonlinear relationship between quantum and molecular descriptors and trypanocidal activity. The calculated descriptors and the principal components were used as input to train neural network models to verify the behavior of the nets. The best model for both network models (MLP and Kohonen) was obtained with four descriptors as input. The descriptors were T(5) (torsion angle), QTS1 (sum of absolute values of the atomic charges), VOLS2 (volume of the substituent at region B) and HOMO-1 (energy of the molecular orbital below HOMO). These descriptors provide information on the kind of interaction that occurs between the compounds and the biological receptor. Both neural network models used here can predict the trypanocidal activity of the quinone compounds with good agreement, with low errors in the testing set and a high correctness rate. Thanks to the nonlinear model obtained from the neural network models, we can conclude that electronic and structural properties are important factors in the interaction between quinone compounds that exhibit trypanocidal activity and their biological receptors. The final ANN models should be useful in the design of novel trypanocidal quinones having improved potency.

Structural studies of prephenate dehydratase from Mycobacterium tuberculosis H37Rv by SAXS, ultracentrifugation, and computational analysis

Tuberculosis (TB) is one of the most common infectious diseases known to man and responsible for millions of human deaths in the world. The increasing incidence of TB in developing countries, the proliferation of multidrug resistant strains, and the absence of resources for treatment have highlighted the need of developing new drugs against TB. The shikimate pathway leads to the biosynthesis of chorismate, a precursor of aromatic amino acids. This pathway is absent from mammals and shown to be essential for the survival of Mycobacterium tuberculosis, the causative agent of TB. Accordingly, enzymes of aromatic amino acid biosynthesis pathway represent promising targets for structure-based drug design. The first reaction in phenylalanine biosynthesis involves the conversion of chorismate to prephenate, catalyzed by chorismate mutase. The second reaction is catalyzed by prephenate dehydratase (PDT) and involves decarboxylation and dehydratation of prephenate to form phenylpyruvate, the precursor of phenylalanine. Here, we describe utilization of different techniques to infer the structure of M. tuberculosis PDT (MtbPDT) in solution. Small angle X-ray scattering and ultracentrifugation analysis showed that the protein oligomeric state is a tetramer and MtbPDT is a flat disk protein. Bioinformatics tools were used to infer the structure of MtbPDT A molecular model for MtbPDT is presented and molecular dynamics simulations indicate that MtbPDT i.s stable. Experimental and molecular modeling results were in agreement and provide evidence for a tetrameric state of MtbPDT in solution.

CDK9 a potential target for drug development

The family of Cyclin-Dependent Kinases (CDKs) can be subdivided into two major functional groups based on their roles in cell cycle and/or transcriptional control. CDK9 is the catalytic subunit of positive transcription elongation factor b (P-TEFb). CDK9 is the kinase of the TAK complex (Tat-associated kinase complex), and binds to Tat protein of HIV, suggesting a possible role for CDK9 in AIDS progression. CDK9 complexed with its regulatory partner cyclin T1, serves as a cellular mediator of the transactivation function of the HIV Tat protein. P-TEFb is responsible for the phosphorylation of the carboxyl-terminal domain of RNA Pol II, resulting in stimulation of transcription. Furthermore, the complexes containing CDK9 induce the differentiation in distinct tissue. The CDK9/cyclin T1 complex is expressed at higher level in more differentiated primary neuroectodermal and neuroblastoma tumors, showing a correlation between the kinase expression and tumor differentiation grade. This may have clinical and therapeutical implications for these tumor types. Among the CDK inhibitors two have shown to be effective against CDK9: Roscovitine and Flavopiridol. These two inhibitors prevented the replication of human immunodeficiency virus (HIV) type 1 by blocking Tat transactivation of the HIV type 1 promoter. These compounds inhibit CDKs by binding to the catalytic domain in place of ATP, preventing transfer of a phosphate group to the substrate. More sensitive therapeutic agents of CDK9 can be designed, and structural studies can add information in the understanding of this kinase. The major features related to CDK9 inhibition will be reviewed in this article.

Adaptive Approach for a Maximum Entropy Algorithm in Ecological Niche Modeling

This paper presents an Adaptive Maximum Entropy (AME) approach for modeling biological species. The Maximum Entropy algorithm (MaxEnt) is one of the most used methods in modeling biological species geographical distribution. The approach presented here is an alternative to the classical algorithm. Instead of using the same set features in the training, the AME approach tries to insert or to remove a single feature at each iteration. The aim is to reach the convergence faster without affect the performance of the generated models. The preliminary experiments were well performed. They showed an increasing on performance both in accuracy and in execution time. Comparisons with other algorithms are beyond the scope of this paper. Some important researches are proposed as future works.

Modeling extended Petri nets compatible with GHENeSys IEC61131 for industrial automation

Petri net (PN) modeling is one of the most used formal methods in the automation applications field, together with programmable logic controllers (PLCs). Therefore, the creation of a modeling methodology for PNs compatible with the IEC61131 standard is a necessity of automation specialists. Different works dealing with this subject have been carried out; they are presented in the first part of this paper [Frey (2000a, 2000b); Peng and Zhou (IEEE Trans Syst Man Cybern, Part C Appl Rev 34(4):523-531, 2004); Uzam and Jones (Int J Adv Manuf Technol 14(10):716-728, 1998)], but they do not present a completely compatible methodology with this standard. At the same time, they do not maintain the simplicity required for such applications, nor the use of all-graphical and all-mathematical ordinary Petri net (OPN) tools to facilitate model verification and validation. The proposal presented here completes these requirements. Educational applications at the USP and UEA (Brazil) and the UO (Cuba), as well as industrial applications in Brazil and Cuba, have already been carried out with good results.

Modeling Vinyl Acetate and n-Butyl Acrylate Emulsion Copolymerization Process

This work presents a mathematical model for the vinyl acetate and n-butyl acrylate emulsion copolymerization process in batch reactors. The model is able to explain the effects of simultaneous changes in emulsifier concentration, initiator concentration, monomer-to-water ratio, and monomer feed composition on monomer conversion, copolymer composition and, to lesser extent, average particle size evolution histories. The main features of the system, such as the increase in the rate of polymerization as temperature, emulsifier, and initiator concentrations increase are correctly represented by the model. The model accounts for the basic features of the process and may be useful for practical applications, despite its simplicity and a reduced number of adjustable parameters.

Molecular cloning and biochemical characterization of a myotoxin inhibitor from Bothrops alternatus snake plasma

Phospholipases A(2) (PLA(2)s) are important components of Bothrops snake venoms, that can induce several effects on envenomations such as myotoxicity, inhibition or induction of platelet aggregation and edema. It is known that venomous and non-venomous snakes present PLA(2) inhibitory proteins (PLIs) in their blood plasma. An inhibitory protein that neutralizes the enzymatic and toxic activities of several PLA2s from Bothrops venoms was isolated from Bothrops alternatus snake plasma by affinity chromatography using the immobilized myotoxin BthTX-I on CNBr-activated Sepharose. Biochemical characterization of this inhibitory protein, denominated alpha BaltMIP, showed it to be a glycoprotein with Mr of similar to 24,000 for the monomeric subunit. CD spectra of the PLA(2)/inhibitor complexes are considerably different from those corresponding to the individual proteins and data deconvolution suggests that the complexes had a relative gain of helical structure elements in comparison to the individual protomers, which may indicate a more compact structure upon complexation. Theoretical and experimental structural studies performed in order to obtain insights into the structural features of aBaltMIP indicated that this molecule may potentially trimerize in solution, thus strengthening the hypothesis previously raised by other authors about snake PLIs oligomerization. (C) 2010 Elsevier Masson SAS. All rights reserved.

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.

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.

Molecular Basis of the Thermostability and Thermophilicity of Laminarinases: X-ray Structure of the Hyperthermostable Laminarinase from Rhodothermus marinus and Molecular Dynamics Simulations

Glycosyl hydrolases are enzymes capable of breaking the glycosidic linkage of polysaccharides and have considerable industrial and biotechnological applications. Driven by the later applications, it is frequently desirable that glycosyl hydrolases display stability and activity under extreme environment conditions, such as high temperatures and extreme pHs. Here, we present X-ray structure of the hyperthermophilic laminarinase from Rhodothermus marinus (RmLamR) determined at 1.95 angstrom resolution and molecular dynamics simulation studies aimed to comprehend the molecular basis, for the thermal stability of this class of enzymes. As most thermostable proteins, RmLamR contains a relatively large number of salt bridges, which are not randomly distributed on the structure. On the contrary, they form clusters interconnecting beta-sheets of the catalytic domain. Not all salt bridges, however, are beneficial for the protein thermostability: the existence of charge-charge interactions permeating the hydrophobic core of the enzymes actually contributes to destabilize the structure by facilitating water penetration into hydrophobic cavities, as can be seen in the case of mesophilic enzymes. Furthermore, we demonstrate that the mobility of the side-chains is perturbed differently in each class of enzymes. The side-chains of loop residues surrounding the catalytic cleft in the mesophilic laminarinase gain mobility and obstruct the active site at high temperature. By contrast, thermophilic laminarinases preserve their active site flexibility, and the active-site cleft remains accessible for recognition of polysaccharide substrates even at high temperatures. The present results provide structural insights into the role played by salt-bridges and active site flexibility on protein thermal stability and may be relevant for other classes of proteins, particularly glycosyl hydrolases.

Spatial pattern detection modeling of thrips (Thrips tabaci) on onion fields

Onion (Allium cepa) is one of the most cultivated and consumed vegetables in Brazil and its importance is due to the large laborforce involved. One of the main pests that affect this crop is the Onion Thrips (Thrips tabaci), but the spatial distribution of this insect, although important, has not been considered in crop management recommendations, experimental planning or sampling procedures. Our purpose here is to consider statistical tools to detect and model spatial patterns of the occurrence of the onion thrips. In order to characterize the spatial distribution pattern of the Onion Thrips a survey was carried out to record the number of insects in each development phase on onion plant leaves, on different dates and sample locations, in four rural properties with neighboring farms under different infestation levels and planting methods. The Mantel randomization test proved to be a useful tool to test for spatial correlation which, when detected, was described by a mixed spatial Poisson model with a geostatistical random component and parameters allowing for a characterization of the spatial pattern, as well as the production of prediction maps of susceptibility to levels of infestation throughout the area.

Scavenging processes of atmospheric particulate matter: a numerical modeling of case studies

Below cloud scavenging processes have been investigated considering a numerical simulation, local atmospheric conditions and particulate matter (PM) concentrations, at different sites in Germany. The below cloud scavenging model has been coupled with bulk particulate matter counter TSI (Trust Portacounter dataset, consisting of the variability prediction of the particulate air concentrations during chosen rain events. The TSI samples and meteorological parameters were obtained during three winter Campaigns: at Deuselbach, March 1994, consisting in three different events; Sylt, April 1994 and; Freiburg, March 1995. The results show a good agreement between modeled and observed air concentrations, emphasizing the quality of the conceptual model used in the below cloud scavenging numerical modeling. The results between modeled and observed data have also presented high square Pearson coefficient correlations over 0.7 and significant, except the Freiburg Campaign event. The differences between numerical simulations and observed dataset are explained by the wind direction changes and, perhaps, the absence of advection mass terms inside the modeling. These results validate previous works based on the same conceptual model.