Biodegradação de hidrocarbonetos aromáticos policíclicos: prospecção metagenômica e modelagem computacional 3-D de proteínas

Knowledge of the native prokaryotes in hazardous locations favors the application of biotechnology for bioremediation. Independent strategies for cultivation and metagenomics contribute to further microbiological knowledge, enabling studies with non-cultivable about the "native microbiological status and its potential role in bioremediation, for example, of polycyclic aromatic hydrocarbons (HPA's). Considering the biome mangrove interface fragile and critical bordering the ocean, this study characterizes the native microbiota mangrove potential biodegradability of HPA's using a biomarker for molecular detection and assessment of bacterial diversity by PCR in areas under the influence of oil companies in the Basin Petroleum Geology Potiguar (BPP). We chose PcaF, a metabolic enzyme, to be the molecular biomarker in a PCR-DGGE detection of prokaryotes that degrade HPA s. The PCR-DGGE fingerprints obtained from Paracuru-CE, Fortim-CE and Areia Branca-RN samples revealed the occurrence of fluctuations of microbial communities according to the sampling periods and in response to the impact of oil. In the analysis of microbial communities interference of the oil industry, in Areia Branca-RN and Paracuru-CE was observed that oil is a determinant of microbial diversity. Fortim-CE probably has no direct influence with the oil activity. In order to obtain data for better understanding the transport and biodegradation of HPA's, there were conducted in silico studies with modeling and simulation from obtaining 3-D models of proteins involved in the degradation of phenanthrene in the transport of HPA's and also getting the 3-D model of the enzyme PcaF used as molecular marker in this study. Were realized docking studies with substrates and products to a better understanding about the transport mechanism and catalysis of HPA s

Análise de cDNAs identificados em bibliotecas substrativas de cDNA para floração de variedades de cana-de-açúcar cultivadas no Rio Grande do Norte(RN): fator de transição NAC, Calmodulina e Fosfatidiltransferase

The flowering is a physiological process that it is vital for plants. This physiological process has been well studied in the plant model Arabidopsis, but in sugarcane this process is not well known. The transition of the shoot apical meristem from vegetative to flowering is a critical factor for plant development. At Brazil northeastern region, the transition to flowering in sugarcane has an important effect as it may reduce up to 60% its production. This is a consequence of the sugar translocation from stalks to the shoot apical meristem which is necessary during the flowering process. Therefore, the aim of this work was to explore and analyze cDNAs previously identified using subtractive cDNA libraries. The results showed that these cDNAs showed differential expression profile in varieties of sugarcane (early x late flowering). The in silico analysis suggested that these cDNAs had homology to calmodulin, NAC transcription factor and phosphatidylinositol, a SEC14, which were described in the literature as having a role in the process of floral development. To better understand the role of the cDNA homologous to calmodulin, tobacco plants were transformed with overexpression cassettes in sense and antissense orientation. Plants overexpressing the cassette in sense orientation did not flowered, while plants overexpressing the cassette in the antissense orientation produced flowers. The data obtained in this study suggested the possible role from CAM sequence, SEC14 and NAC in the induction/floral development pathway in sugarcane, this is the first study in order to analyze these genes in the sugarcane flowering process.

Caracterização de rizóbios indicados para produção de inoculantes por meio de sequenciamento parcial do 16S rRNA

O objetivo deste trabalho foi confrontar as sequências parciais do gene 16S rRNA de estirpes padrão de rizóbios com as de estirpes recomendadas para a produção de inoculantes no Brasil, com vistas à verificação da confiabilidade do sequenciamento parcial desse gene para a identificação rápida de estirpes. Foram realizados sequenciamentos através de reação em cadeia da polimerase (PCR) com iniciadores relativos à região codificadora do gene 16S rRNA entre as bactérias estudadas. Os resultados foram analisados pela consulta de similaridade de nucleotídeos aos do Basic Local Alignment Search Tool (Blastn) e por meio da interpretação de árvores filogenéticas geradas usando ferramentas de bioinformática. A classificação taxonômica das estirpes Semia recomendadas para inoculação de leguminosas com base em propriedades morfológicas e especificidade hospedeira não foi confirmada em todas as estirpes. A maioria das estirpes estudadas, consultadas no Blastn, é consistente com a classificação proposta pela construção de árvores filogenéticas das sequências destas estirpes, com base na similaridade pelo sequenciamento parcial do gene considerado.

Predição de promotores de Bacillus subtilis usando técnicas de aprendizado de máquina

One of the most important goals of bioinformatics is the ability to identify genes in uncharacterized DNA sequences on world wide database. Gene expression on prokaryotes initiates when the RNA-polymerase enzyme interacts with DNA regions called promoters. In these regions are located the main regulatory elements of the transcription process. Despite the improvement of in vitro techniques for molecular biology analysis, characterizing and identifying a great number of promoters on a genome is a complex task. Nevertheless, the main drawback is the absence of a large set of promoters to identify conserved patterns among the species. Hence, a in silico method to predict them on any species is a challenge. Improved promoter prediction methods can be one step towards developing more reliable ab initio gene prediction methods. In this work, we present an empirical comparison of Machine Learning (ML) techniques such as Na¨ýve Bayes, Decision Trees, Support Vector Machines and Neural Networks, Voted Perceptron, PART, k-NN and and ensemble approaches (Bagging and Boosting) to the task of predicting Bacillus subtilis. In order to do so, we first built two data set of promoter and nonpromoter sequences for B. subtilis and a hybrid one. In order to evaluate of ML methods a cross-validation procedure is applied. Good results were obtained with methods of ML like SVM and Naïve Bayes using B. subtilis. However, we have not reached good results on hybrid database

Aplicação de técnicas de aprendizado de máquina no reconhecimento de classes estruturais de proteínas

Nowadays, classifying proteins in structural classes, which concerns the inference of patterns in their 3D conformation, is one of the most important open problems in Molecular Biology. The main reason for this is that the function of a protein is intrinsically related to its spatial conformation. However, such conformations are very difficult to be obtained experimentally in laboratory. Thus, this problem has drawn the attention of many researchers in Bioinformatics. Considering the great difference between the number of protein sequences already known and the number of three-dimensional structures determined experimentally, the demand of automated techniques for structural classification of proteins is very high. In this context, computational tools, especially Machine Learning (ML) techniques, have become essential to deal with this problem. In this work, ML techniques are used in the recognition of protein structural classes: Decision Trees, k-Nearest Neighbor, Naive Bayes, Support Vector Machine and Neural Networks. These methods have been chosen because they represent different paradigms of learning and have been widely used in the Bioinfornmatics literature. Aiming to obtain an improvment in the performance of these techniques (individual classifiers), homogeneous (Bagging and Boosting) and heterogeneous (Voting, Stacking and StackingC) multiclassification systems are used. Moreover, since the protein database used in this work presents the problem of imbalanced classes, artificial techniques for class balance (Undersampling Random, Tomek Links, CNN, NCL and OSS) are used to minimize such a problem. In order to evaluate the ML methods, a cross-validation procedure is applied, where the accuracy of the classifiers is measured using the mean of classification error rate, on independent test sets. These means are compared, two by two, by the hypothesis test aiming to evaluate if there is, statistically, a significant difference between them. With respect to the results obtained with the individual classifiers, Support Vector Machine presented the best accuracy. In terms of the multi-classification systems (homogeneous and heterogeneous), they showed, in general, a superior or similar performance when compared to the one achieved by the individual classifiers used - especially Boosting with Decision Tree and the StackingC with Linear Regression as meta classifier. The Voting method, despite of its simplicity, has shown to be adequate for solving the problem presented in this work. The techniques for class balance, on the other hand, have not produced a significant improvement in the global classification error. Nevertheless, the use of such techniques did improve the classification error for the minority class. In this context, the NCL technique has shown to be more appropriated

Um sistema computacional para diagnosticar viroses de plantas usando a técnica de PCR com construção de primers espécie-específicos

It proposes a established computational solution in the development of a software to construct species-specific primers, used to improve the diagnosis of virus of plant for PCR. Primers are indispensable to PCR reaction, besides providing the specificity of the diagnosis. Primer is a synthetic, short, single stranded piece of DNA, used as a starter in PCR technique. It flanks the sequence desired to amplify. Species-specific primers indicate the well known region of beginning and ending where the polymerase enzyme is going to amplify on a certain species, i.e. it is specific for only a species. Thus, the main objective of this work is to automatize the process of choice of primers, optimizing the specificity of chosen primers by the traditional method

Algoritmos de agrupamento aplicados a dados de expressão gênica de câncer: um estudo comparativo

The use of clustering methods for the discovery of cancer subtypes has drawn a great deal of attention in the scientific community. While bioinformaticians have proposed new clustering methods that take advantage of characteristics of the gene expression data, the medical community has a preference for using classic clustering methods. There have been no studies thus far performing a large-scale evaluation of different clustering methods in this context. This work presents the first large-scale analysis of seven different clustering methods and four proximity measures for the analysis of 35 cancer gene expression data sets. Results reveal that the finite mixture of Gaussians, followed closely by k-means, exhibited the best performance in terms of recovering the true structure of the data sets. These methods also exhibited, on average, the smallest difference between the actual number of classes in the data sets and the best number of clusters as indicated by our validation criteria. Furthermore, hierarchical methods, which have been widely used by the medical community, exhibited a poorer recovery performance than that of the other methods evaluated. Moreover, as a stable basis for the assessment and comparison of different clustering methods for cancer gene expression data, this study provides a common group of data sets (benchmark data sets) to be shared among researchers and used for comparisons with new methods

Structural bioinformatics study of cyclin-dependent kinases complexed with inhibitors

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

Modelagem molecular da Chiquimato quinase de Mycobacterium leprae

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

O uso de rede neural artificial MLP na predição de estruturas secundárias de proteínas

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Estudo estrutural de quinases dependentes de ciclinas por métodos de modelagem molecular comparativa

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Modelagem molecular da interação entre a proteína de fusão do vírus sincicial respiratório humano e inibidores da ação viral. -

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Caracterização molecular e expressão heteróloga do alérgeno fosfolipase A1 do veneno de Polybia paulista (Hymenoptera; Vespidae)

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Análise comparativa da expressão protéica em glândulas pós-faríngeas de saúva-limão, Atta sexdens rubropilosa Forel, 1908 (Hymenoptera: Formicidae) submetidas à suplementação lipídica

Pós-graduação em Ciências Biológicas (Biologia Celular e Molecular) - IBRC

Transcript finishing initiative: contribuição do laboratório IL2

Pós-graduação em Ciências Biológicas (Biologia Celular e Molecular) - IBRC