A structure-activity relationship (SAR) study of neolignan compounds with anti-schistosomiasis activity

Um conjunto de dezoito compostos de neolignanas com atividade antiesquistossomose foi estudado com o método semi-empírico PM3 e outros métodos teóricos com o intuito de avaliar algumas propriedades (variáveis ou descritores) moleculares selecionadas e correlacioná-las com a atividade biológica. Análise exploratória dos dados (análise de componentes principais, PCA, e análise hierárquica de agrupamentos, HCA), análise discriminante (DA) e o método KNN foram utilizados na obtenção de possíveis correlações entre os descritores calculados e a atividade biológica em questão e na predição da atividade antiesquistossimose de algumas moléculas teste. Os descritores moleculares responsáveis pela separação entre os compostos ativos e inativos foram: energia de hidratação (HE), refratividade molecular (MR) e carga sobre o átomo C19 (Q19). Estes descritores fornecem informações a respeito do tipo de interação que pode ocorrer entre os compostos e seu respectivo receptor biológico. Após a construção do modelo para compostos ativos e inativos, os métodos PCA, HCA, DA e KNN foram empregados em um estudo de predição. Foram estudados 10 novos compostos e somente 5 deles foram classificados como ativos contra esquistossomose.

Abordagem para o desenvolvimento de um etiquetador de alta acurácia para o Português do Brasil

A etiquetagem morfossintática é uma tarefa básica requerida por muitas aplicações de processamento de linguagem natural, tais como análise gramatical e tradução automática, e por aplicações de processamento de fala, por exemplo, síntese de fala. Essa tarefa consiste em etiquetar palavras em uma sentença com as suas categorias gramaticais. Apesar dessas aplicações requererem etiquetadores que demandem maior precisão, os etiquetadores do estado da arte ainda alcançam acurácia de 96 a 97%. Nesta tese, são investigados recursos de corpus e de software para o desenvolvimento de um etiquetador com acurácia superior à do estado da arte para o português brasileiro. Centrada em uma solução híbrida que combina etiquetagem probabilística com etiquetagem baseada em regras, a proposta de tese se concentra em um estudo exploratório sobre o método de etiquetagem, o tamanho, a qualidade, o conjunto de etiquetas e o gênero dos corpora de treinamento e teste, além de avaliar a desambiguização de palavras novas ou desconhecidas presentes nos textos a serem etiquetados. Quatro corpora foram usados nos experimentos: CETENFolha, Bosque CF 7.4, Mac-Morpho e Selva Científica. O modelo de etiquetagem proposto partiu do uso do método de aprendizado baseado em transformação(TBL) ao qual foram adicionadas três estratégias, combinadas em uma arquitetura que integra as saídas (textos etiquetados) de duas ferramentas de uso livre, o TreeTagger e o -TBL, com os módulos adicionados ao modelo. No modelo de etiquetador treinado com o corpus Mac-Morpho, de gênero jornalístico, foram obtidas taxas de acurácia de 98,05% na etiquetagem de textos do Mac-Morpho e 98,27% em textos do Bosque CF 7.4, ambos de gênero jornalístico. Avaliou-se também o desempenho do modelo de etiquetador híbrido proposto na etiquetagem de textos do corpus Selva Científica, de gênero científico. Foram identificadas necessidades de ajustes no etiquetador e nos corpora e, como resultado, foram alcançadas taxas de acurácia de 98,07% no Selva Científica, 98,06% no conjunto de teste do Mac-Morpho e 98,30% em textos do Bosque CF 7.4. Esses resultados são significativos, pois as taxas de acurácia alcançadas são superiores às do estado da arte, validando o modelo proposto em busca de um etiquetador morfossintático mais confiável.

A bi-objective genetic approach for the selection of sugarcane varieties to comply with environmental and economic requirements

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Efeito da suplementação de erva mate (Ilex paraguariensis) sobre a capacidade antioxidante total em atletas futebolistas em teste exaustivo em esteira

Introduction: Physical exercise are related to high oxygen consumption, leading to increase on reactive oxygen species (ROS) generation and subsequent oxidative stress. Concomitantly, physical training can improve the antioxidant defense systems, reducing the deleterious activity of ROS. The yerba mate (Ilex paraguariensis) has several bioactive compounds in its composition, providing important antioxidant activity in improving defense systems and reducing the damage caused by ROS. Few studies related to yerba mate with antioxidant effects during exercise. Objective: Evaluate whether the consumption-based drink yerba mate (Ilex paraguariensis) is able to increase the total antioxidant performance (TAP) after an exhaustive test on a treadmill. Methods: The sample counted with 15 female soccers players from Botucatu-SP female soccer team with a mean age of 22.1 ± 4.2 years. For laboratory tests , it was evaluated: triglycerides (TG), total cholesterol (TC) and fractions, glucose and gamma-GT were dosed by dry chemistry (Vitros® System, Johnson & Johnson). LDL-cholesterol was obtained by Friedwald formula. Total antioxidant performance (TAP) was obtained by the method of fluorescence assay for the measurement of plasma. Weight, height and body mass index (IMC) were measured, percentage of body fat was obtained by bioeletrical impedance analysis (Biodinâmics, modelo 450, USA). Arterial blood pressure was checked by auscultatory method and cardiorespiratory fitness was determined by ergoespirometric test (Ramp Protocol). Maltodextrin was supplemented (30 g in 400 ml) 30 minutes after M0 with placebo (400 ml) or mate (5 g in 400 ml of water). Statistical analysis: ANOVA for repeated measures followed by Tukey's test set, p<0.05. Results: There was a decrease in the pH after exhaustive testing for water and tea (p<0.0001), the same was observed for bicarbonate (p<0.0001). In both groups pO₂ increased for ...

Classification of intact açai (Euterpe oleracea Mart.) and jucara (Euterpe edulis Mart) fruits based on dry matter content by means of near infrared spectroscopy

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Ferramenta computacional para identificação de micro-organismos com base em assinaturas genômicas

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Avaliação in vitro do tempo de fotopolimerização com led de alta potência de bráquetes ortodônticos e remoção de adesivo remanescente sob luz ultravioleta

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Theoretical models for the antitrypanosomal activity of thiosemicarbazone derivatives

Thiosemicarbazones are cruzain inhibitors which have been identified as potential antitrypanosomal agents. In this work, several molecular properties were calculated at the density functional theory (DFT)/B3LYP/6-311G* level for a set of 44 thiosemicarbazones. Unsupervised and supervised pattern recognition techniques (hierarchical cluster analysis, principal component analysis, kth-nearest neighbors, and soft independent modeling by class analogy) were used to obtain structureactivity relationship models, which are able to classify unknown compounds according to their activities. The chemometric analyses performed here revealed that 12 descriptors can be considered responsible for the discrimination between high and low activity compounds. Classification models were validated with an external test set, showing that predictive classifications were achieved with the selected variable set. The results obtained here are in good agreement with previous findings from the literature, suggesting that our models can be useful on further investigations on the molecular determinants for the antichagasic activity. (C) 2012 Wiley Periodicals, Inc.

Advanced QSAR Studies on PPAR delta Ligands Related to Metabolic Diseases

PPAR delta is a nuclear receptor that, when activated, regulates the metabolism of carbohydrates and lipids and is related to metabolic syndrome and type 2 diabetes. To understand the main interactions between ligands and PPAR delta, we have constructed 2D and 3D QSAR models and compared them with HOMO, LUMO and electrostatic potential maps of the compounds studied, as well as docking results. All QSAR models showed good statistical parameters and prediction outcomes. The QSAR models were used to predict the biological activity of an external test set, and the predicted values are in good agreement with the experimental results. Furthermore, we employed all maps to evaluate the possible interactions between the ligands and PPAR delta. These predictive QSAR models, along with the HOMO, LUMO and MEP maps, can provide insights into the structural and chemical properties that are needed in the design of new PPAR delta ligands that have improved biological activity and can be employed to treat metabolic diseases.

Hybrid Water Demand Forecasting Model Associating Artificial Neural Network with Fourier Series

This paper addressed the problem of water-demand forecasting for real-time operation of water supply systems. The present study was conducted to identify the best fit model using hourly consumption data from the water supply system of Araraquara, Sa approximate to o Paulo, Brazil. Artificial neural networks (ANNs) were used in view of their enhanced capability to match or even improve on the regression model forecasts. The ANNs used were the multilayer perceptron with the back-propagation algorithm (MLP-BP), the dynamic neural network (DAN2), and two hybrid ANNs. The hybrid models used the error produced by the Fourier series forecasting as input to the MLP-BP and DAN2, called ANN-H and DAN2-H, respectively. The tested inputs for the neural network were selected literature and correlation analysis. The results from the hybrid models were promising, DAN2 performing better than the tested MLP-BP models. DAN2-H, identified as the best model, produced a mean absolute error (MAE) of 3.3 L/s and 2.8 L/s for training and test set, respectively, for the prediction of the next hour, which represented about 12% of the average consumption. The best forecasting model for the next 24 hours was again DAN2-H, which outperformed other compared models, and produced a MAE of 3.1 L/s and 3.0 L/s for training and test set respectively, which represented about 12% of average consumption. DOI: 10.1061/(ASCE)WR.1943-5452.0000177. (C) 2012 American Society of Civil Engineers.

Computation of (3)J(HH) coupling constants with a combination of density functional theory and semiempirical calculations. Application to complex molecules

This work evaluates the efficiency of economic levels of theory for the prediction of (3)J(HH) spin-spin coupling constants, to be used when robust electronic structure methods are prohibitive. To that purpose, DFT methods like mPW1PW91. B3LYP and PBEPBE were used to obtain coupling constants for a test set whose coupling constants are well known. Satisfactory results were obtained in most of cases, with the mPW1PW91/6-31G(d,p)//B3LYP/6-31G(d,p) leading the set. In a second step. B3LYP was replaced by the semiempirical methods PM6 and RM1 in the geometry optimizations. Coupling constants calculated with these latter structures were at least as good as the ones obtained by pure DFT methods. This is a promising result, because some of the main objectives of computational chemistry - low computational cost and time, allied to high performance and precision - were attained together. (C) 2012 Elsevier B.V. All rights reserved.

Structure- and Ligand-Based Structure-Activity Relationships for a Series of Inhibitors of Aldolase

Aldolase has emerged as a promising molecular target for the treatment of human African trypanosomiasis. Over the last years, due to the increasing number of patients infected with Trypanosoma brucei, there is an urgent need for new drugs to treat this neglected disease. In the present study, two-dimensional fragment-based quantitative-structure activity relationship (QSAR) models were generated for a series of inhibitors of aldolase. Through the application of leave-one-out and leave-many-out cross-validation procedures, significant correlation coefficients were obtained (r(2) = 0.98 and q(2) = 0.77) as an indication of the statistical internal and external consistency of the models. The best model was employed to predict pK(i) values for a series of test set compounds, and the predicted values were in good agreement with the experimental results, showing the power of the model for untested compounds. Moreover, structure-based molecular modeling studies were performed to investigate the binding mode of the inhibitors in the active site of the parasitic target enzyme. The structural and QSAR results provided useful molecular information for the design of new aldolase inhibitors within this structural class.

A fragment-based approach for ligand binding affinity and selectivity for the liver X receptor beta

Selective modulation of liver X receptor beta (LXR beta) has been recognized as an important approach to prevent or reverse the atherosclerotic process. In the present work, we have developed robust conformation-independent fragment-based quantitative structure-activity and structure-selectivity relationship models for a series of quinolines and cinnolines as potent modulators of the two LXR sub-types. The generated models were then used to predict the potency of an external test set and the predicted values were in good agreement with the experimental results, indicating the potential of the models for untested compounds. The final 2D molecular recognition patterns obtained were integrated to 3D structure-based molecular modeling studies to provide useful insights into the chemical and structural determinants for increased LXR beta binding affinity and selectivity. (C) 2011 Elsevier Inc. All rights reserved.

A fragment-based approach for the in silico prediction of blood-brain barrier permeation

Blood-brain barrier (BBB) permeation is an essential property for drugs that act in the central nervous system (CNS) for the treatment of human diseases, such as epilepsy, depression, Alzheimer's disease, Parkinson disease, schizophrenia, among others. In the present work, quantitative structure-property relationship (QSPR) studies were conducted for the development and validation of in silico models for the prediction of BBB permeation. The data set used has substantial chemical diversity and a relatively wide distribution of property values. The generated QSPR models showed good statistical parameters and were successfully employed for the prediction of a test set containing 48 compounds. The predictive models presented herein are useful in the identification, selection and design of new drug candidates having improved pharmacokinetic properties.

QSAR models for inhibitors of physiological impact of Escherichia coli that leads to diarrhea

Quantitative structure – activity relationships (QSARs) developed to evaluate percentage of inhibition of STa-stimulated (Escherichia coli) cGMP accumulation in T84 cells are calculated by the Monte Carlo method. This endpoint represents a measure of biological activity of a substance against diarrhea. Statistical quality of the developed models is quite good. The approach is tested using three random splits of data into the training and test sets. The statistical characteristics for three splits are the following: (1) n = 20, r2 = 0.7208, q2 = 0.6583, s = 16.9, F = 46 (training set); n = 11, r2 = 0.8986, s = 14.6 (test set); (2) n = 19, r2 = 0.6689, q2 = 0.5683, s = 17.6, F = 34 (training set); n = 12, r2 = 0.8998, s = 12.1 (test set); and (3) n = 20, r2 = 0.7141, q2 = 0.6525, s = 14.7, F = 45 (training set); n = 11, r2 = 0.8858, s = 19.5 (test set). Based on the proposed here models hypothetical compounds which can be useful agents against diarrhea are suggested.