7 resultados para Structure Prediction Servers
em Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP)
Resumo:
The dengue virus NS1 protein has been shown to be a protective antigen under different experimental conditions but the recombinant protein produced in bacterial expression systems is usually not soluble and loses structural and immunological features of the native viral protein In the present study, experimental conditions leading to purification and refolding of the recombinant dengue virus type 2 (DENV-2) NS1 protein expressed in Escherichia coil are described The refolded recombinant protein was recovered as heat-stable soluble dimers with preserved structural features, as demonstrated by spectroscopic methods In addition, antibodies against epitopes of the NS1 protein expressed in eukaryotic cells recognized the refolded protein expressed in E coli but not the denatured form or the same protein submitted to a different refolding condition Collectively, the results demonstrate that the recombinant NS1 protein preserved important conformation and antigenic determinants of the native virus protein and represents a valuable reagent either for the development of vaccines or for diagnostic methods. (C) 2010 Elsevier B V All rights reserved
Resumo:
Hepatitis C virus (HCV), exhibits considerable genetic diversity, but presents a relatively well conserved 5 ` noncoding region (5 ` NCR) among all genotypes. In this study, the structural features and translational efficiency of the HCV 5 ` NCR sequences were analyzed using the programs RNAfold, RNAshapes and RNApdist and with a bicistronic dual luciferase expression system, respectively. RNA structure prediction software indicated that base substitutions will alter potentially the 5 ` NCR structure. The heterogeneous sequence observed on 5 ` NCR led to important changes in their translation efficiency in different cell culture lines. Interactions of the viral RNA with cellular transacting factors may vary according to the cell type and viral genome polymorphisms that may result in the translational efficiency observed. J. Med. Virol. 81: 1212-1219, 2009. (C) 2009 Wiley-Liss, Inc.
Resumo:
The objective of this article is to find out the influence of the parameters of the ARIMA-GARCH models in the prediction of artificial neural networks (ANN) of the feed forward type, trained with the Levenberg-Marquardt algorithm, through Monte Carlo simulations. The paper presents a study of the relationship between ANN performance and ARIMA-GARCH model parameters, i.e. the fact that depending on the stationarity and other parameters of the time series, the ANN structure should be selected differently. Neural networks have been widely used to predict time series and their capacity for dealing with non-linearities is a normally outstanding advantage. However, the values of the parameters of the models of generalized autoregressive conditional heteroscedasticity have an influence on ANN prediction performance. The combination of the values of the GARCH parameters with the ARIMA autoregressive terms also implies in ANN performance variation. Combining the parameters of the ARIMA-GARCH models and changing the ANN`s topologies, we used the Theil inequality coefficient to measure the prediction of the feed forward ANN.
Resumo:
The genome sequence of Aedes aegypti was recently reported. A significant amount of Expressed Sequence Tags (ESTs) were sequenced to aid in the gene prediction process. In the present work we describe an integrated analysis of the genomic and EST data, focusing on genes with preferential expression in larvae (LG), adults (AG) and in both stages (SG). A total of 913 genes (5.4% of the transcript complement) are LG, including ion transporters and cuticle proteins that are important for ion homeostasis and defense. From a starting set of 245 genes encoding the trypsin domain, we identified 66 putative LG, AG, and SG trypsins by manual curation. Phylogenetic analyses showed that AG trypsins are divergent from their larval counterparts (LG), grouping with blood-induced trypsins from Anopheles gambiae and Simulium vittatum. These results support the hypothesis that blood-feeding arose only once, in the ancestral Culicomorpha. Peritrophins are proteins that interlock chitin fibrils to form the peritrophic membrane (PM) that compartmentalizes the food in the midgut. These proteins are recognized by having chitin-binding domains with 6 conserved Cys and may also present mucin-like domains (regions expected to be highly O-glycosylated). PM may be formed by a ring of cells (type 2, seen in Ae. aegypti larvae and Drosophila melanogaster) or by most midgut cells (type 1, found in Ae. aegypti adult and Tribolium castaneum). LG and D. melanogaster peritrophins have more complex domain structures than AG and T. castaneum peritrophins. Furthermore, mucin-like domains of peritrophins from T. castaneum (feeding on rough food) are lengthier than those of adult Ae. aegypti (blood-feeding). This suggests, for the first time, that type 1 and type 2 PM may have variable molecular architectures determined by different peritrophins and/or ancillary proteins, which may be partly modulated by diet.
Resumo:
Flash points (T(FP)) of hydrocarbons are calculated from their flash point numbers, N(FP), with the relationship T(FP) (K) = 23.369N(FP)(2/3) + 20.010N(FP)(1/3) + 31.901 In turn, the N(FP) values can be predicted from experimental boiling point numbers (Y(BP)) and molecular structure with the equation N(FP) = 0.987 Y(BP) + 0.176D + 0.687T + 0.712B - 0.176 where D is the number of olefinic double bonds in the structure, T is the number of triple bonds, and B is the number of aromatic rings. For a data set consisting of 300 diverse hydrocarbons, the average absolute deviation between the literature and predicted flash points was 2.9 K.
Resumo:
This paper describes a new module of the expert system SISTEMAT used for the prediction of the skeletons of neolignans by (13)C NMR, (1)H NMR and botanical data obtained from the literature. SISTEMAT is composed of MACRONO, SISCONST, C13MACH, H1MACH and SISOCBOT programs, each analyzing data of the neolignan in question to predict the carbon skeleton of the compound. From these results, the global probability is computed and the most probable skeleton predicted. SISTEMAT predicted the skeletons of 75% of the 20 neolignans tested, in a rapid and simple procedure demonstrating its advantage for the structural elucidation of new compounds.
Resumo:
This article describes the integration of the LSD (Logic for Structure Determination) and SISTEMAT expert systems that were both designed for the computer-assisted structure elucidation of small organic molecules. A first step has been achieved towards the linking of the SISTEMAT database with the LSD structure generator. The skeletal descriptions found by the SISTEMAT programs are now easily transferred to LSD as substructural constraints. Examples of the synergy between these expert systems are given for recently reported natural products.