Thesis (Ph. D.)--University of Washington, 2007. / Vita. Includes bibliographical references (leaves 87-94).

Thesis (Ph. D.)--University of Washington, 2007. / Vita. Includes bibliographical references (leaves 139-147).

Thesis (Ph. D.)--University of Alabama at Birmingham, 2007. / Title from first page of PDF file (viewed Feb. 6, 2008). Includes bibliographical references.

Orientadores: Goran Neshich, João Alexandre Ribeiro Gonçalves Barbosa / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Biologia / Made available in DSpace on 2018-08-21T21:50:32Z (GMT). No. of bitstreams: 1 Pereira_JoseGeraldodeCarvalho_M.pdf: 10985777 bytes, checksum: 2610df8bda1ef229c4bcdc8c6c5d8325 (MD5) Previous issue date: 2012 / Resumo: A propriedade das proteínas de se ligarem umas as outras de forma altamente específica, formando complexos estáveis, é uma característica fundamental para todos os processos biológicos. Uma melhor compreensão da formação do complexo abre perspectivas para muitas aplicações práticas, entre elas o design racional de novos fármacos. Trabalhos anteriores demonstraram, através de experimentos de varredura por alaninas, que um pequeno número de resíduos das interfaces protéicas contribui com a maior parte da energia de ligação e por isso foram chamados de hot spots. Devido à importância desses resíduos para as interações proteína-proteína, diversos métodos computacionais têm sido propostos para predizer os hot spots complementando assim o procedimento experimental. Entre esses, estão métodos physics-based como dinâmica molecular, e também métodos knowledge-based, onde dados experimentais são utilizados para treinar métodos computacionais que aprendem as regras para classificar corretamente os hot spots e usados posteriormente para classificar novos casos em estruturas de complexos protéicos. Entre os algoritmos de aprendizado computacionais mais utilizados estão árvores de decisão, redes neurais, máquinas de vetor de suporte. Nesse trabalho, desenvolvemos métodos de predição de hot spots utilizando máquinas de vetor de suporte, que foram abastecidas na entrada com um conjunto de 186 descritores estruturais extraídos do banco de dados STING_DB e também com 112 novos descritores propostos neste trabalho. Os métodos propostos nesse trabalho apresentaram desempenho superior aos métodos de predição de hot spots mais conhecidos da literatura, como KFC, Minerva, Rosetta e FOLDEF. Além disso, a análise estatística dos descritores e também a seleção dos descritores mais eficientes na tarefa de classificar hot spots permitiu que observássemos diversas características que são distintas entre resíduos que são hot spots e os que não são. Entre estas características, a entalpia de hidratação ao redor do resíduo sugere que essa região é mais hidrofílica em hot spots. Essa região, que para hot spots é denominada de anel-O, tem a função de impedir o contato do solvente com o hot spot e por isso, alguns autores acreditavam tratar-se de uma região hidrofóbica, algo que os resultados deste trabalho não confirmaram. Futuramente, os novos descritores propostos neste trabalho serão agregados ao STING_DB e o método de predição de hot spots será integrado ao STING permitindo a predição de hot spots de todos os complexos protéicos depositados no Protein Data Bank (PDB) assim como de complexos protéicos fornecidos pelo usuário / Abstract: The property of the proteins to bind each other in a highly specific way, forming stable complexes, is a key feature for all biological processes. A better understanding of the formation of protein complexes provides many practical applications, including the rational design of new drugs. Through experiments of alanine scanning, it was shown that a small number of residues belonging to protein interfaces contribute decisively to the binding energy and so were called hot spots. Because of the importance of these residues for protein-protein interactions many computational methods have been proposed to predict the hot spots and thus complement the experimental procedure. These include physics-based methods such as molecular dynamics and also knowledge-based methods where experimental data are used to train computational methods that learn the rules for correctly classifying the hot spots and are then used to classify new cases in structures of protein complexes. Among the computational learning algorithms most frequently used are decision trees, neural networks, support vector machines, among others. In this work, we developed methods to predict hot spots using support vector machines, using at the input 186 structural descriptors extracted from the STING_DB and 112 new descriptors proposed in this work. The methods proposed here showed superior performance to methods of predicting hot spots best known from the literature, such as KFC, Minerva, Rosetta and FOLDEF. In addition, statistical analysis of the descriptors and also the selection of the descriptors more efficient in the task of classifying hot spots allowed us to observe several characteristics that are distinct for residues that are hot spots. Among these features, the enthalpy of hydration suggests that the region around hot spots is more hydrophilic. This region, which for hot spots is called O-ring, serves to prevent the contact of the solvent with the hot spot and therefore some authors believe that this was a hydrophobic region whereas results presented here show otherwise. In future, the new descriptors described in this work will be added to the STING_DB and the method of prediction of hot spots will be integrated with STING allowing the prediction of hot spots of all protein complexes deposited in the Protein Data Bank (PDB) as well as protein complexes supplied by the user / Mestrado / Bioinformatica / Mestre em Genética e Biologia Molecular

Thesis (Ph.D. in Bioinformatics) -- University of Colorado Denver, 2008. / Typescript. Includes bibliographical references (leaves 141-150). Free to UCD Anschutz Medical Campus. Online version available via ProQuest Digital Dissertations;

Hao, Yan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 98-115). / Abstracts in English and Chinese. / Acknowledgement --- p.i / Abstract --- p.iii / 摘要 --- p.v / List of Abbreviations --- p.vii / List of Figures --- p.x / List of Tables --- p.xi / List of Primers --- p.xii / Publications arising from this study --- p.xiii / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Alzheimer's disease --- p.1 / Chapter 1.2 --- APP and its functions --- p.4 / Chapter 1.2.1 --- APP processing --- p.7 / Chapter 1.3 --- APPc-interacting proteins --- p.10 / Chapter 1.3.1 --- FE65 --- p.10 / Chapter 1.3.2 --- Xllα and Xl1β --- p.12 / Chapter 1.3.3 --- JIP-1 --- p.13 / Chapter 1.3.4 --- Dabl and Dab2 --- p.15 / Chapter 1.3.5 --- SNX17 --- p.15 / Chapter 1.3.6 --- Numb --- p.15 / Chapter 1.3.7 --- AIDA-1 --- p.16 / Chapter 1.4 --- Objectives of the project --- p.18 / Chapter 1.4.1 --- Engulfment adaptor protein 1 (GULP1) --- p.19 / Chapter 1.4.2 --- Specific aims of my study --- p.20 / Chapter Chapter 2 --- General Methodology --- p.22 / Chapter 2.1 --- Bacterial culture --- p.22 / Chapter 2.2 --- Mini-preparation/Midi-preparation of plasmid DNA --- p.22 / Chapter 2.3 --- Spectrophotometric analysis of DNA --- p.22 / Chapter 2.4 --- Agarose gel electrophoresis for DNA --- p.23 / Chapter 2.5 --- Preparation of competent E. coli --- p.23 / Chapter 2.6 --- Transformation of competent E. coli --- p.24 / Chapter 2.7 --- Molecular cloning --- p.24 / Chapter 2.7.1 --- Preparation of the cloning vector and insert --- p.25 / Chapter 2.7.2 --- Isolation of DNA from agarose gel --- p.25 / Chapter 2.7.3 --- DNA ligation and transformation --- p.25 / Chapter 2.7.4 --- Rapid screening for ligated plasmid --- p.26 / Chapter 2.8 --- Site-directed mutagenesis --- p.26 / Chapter 2.9 --- Cell culture and transfection --- p.27 / Chapter 2.10 --- Sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS/PAGE) --- p.28 / Chapter 2.11 --- Western blotting --- p.29 / Chapter Chapter 3 --- Investigation of the GULP1-APP interaction and the effect of GULP1 on APP processing --- p.31 / Chapter 3.1 --- Introduction --- p.31 / Chapter 3.2 --- Materials and methods --- p.34 / Chapter 3.2.1 --- DNA constructs --- p.34 / Chapter 3.2.2 --- Antibodies --- p.34 / Chapter 3.2.3 --- GST pull-down assays --- p.35 / Chapter 3.2.4 --- Rat tissues preparation --- p.36 / Chapter 3.2.5 --- Immunostaining --- p.36 / Chapter 3.2.6 --- "siRNA knockdown of GULPl in CHO, HEK293 and SHSY5Y cells" --- p.37 / Chapter 3.2.7 --- Luciferase assays --- p.37 / Chapter 3.2.9 --- Tricine-SDS/PAGE analysis for APP CTFs --- p.38 / Chapter 3.2.9 --- Aβ enzyme-linked immunosorbent assay (ELISA) --- p.39 / Chapter 3.2.10 --- Statistical analysis --- p.40 / Chapter 3.3 --- Results --- p.40 / Chapter 3.3.1 --- GULP1 F145V mutant abandons the GULP1-APP interaction --- p.40 / Chapter 3.3.2 --- GULP1 and APP colocalize in neurons --- p.45 / Chapter 3.3.3 --- "siRNA mediated knockdown of GULPl in CHO, HEK293 and SHSY5Y cells" --- p.48 / Chapter 3.3.4 --- GULP1 enhances the cleavage of APP in APP-GAL4 cleavage system --- p.49 / Chapter 3.3.5 --- GULP1 alters APP processing by increasing the secretion of APP CTFs --- p.52 / Chapter 3.3.6 --- GULP1 stimulates Aβ secretion --- p.55 / Chapter 3.4 --- Discussion --- p.57 / Chapter Chapter 4 --- Identification and characterization of GULPl phosphorylation sites --- p.60 / Chapter 4.1 --- Introduction --- p.60 / Chapter 4.2 --- Materials and Methods --- p.60 / Chapter 4.2.1 --- DNA constructs --- p.61 / Chapter 4.2.2 --- Antibodies --- p.61 / Chapter 4.2.3 --- Expression and purification of GST fusion proteins --- p.61 / Chapter 4.2.4 --- In vitro phosphorylation of GULP1 by cdk5/p35 --- p.62 / Chapter 4.3 --- Results --- p.62 / Chapter 4.3.1 --- GULP1 Ser223 can be phosphorylated by cdk5/p35 in vivo --- p.62 / Chapter 4.3.2 --- The phosphorylation ofGULPl Thr35 completely abolished the GULP1-APP interaction --- p.67 / Chapter 4.4 --- Discussion --- p.70 / Chapter Chapter 5 --- Crystallization of the PTB domains of GULPl and GULP1t35d…… --- p.72 / Chapter 5.1 --- Introduction --- p.72 / Chapter 5.2 --- Materials and Methods --- p.72 / Chapter 5.2.1 --- DNA constructs --- p.72 / Chapter 5.2.2 --- Small-scale protein expression and purification --- p.73 / Chapter 5.2.3 --- Large-scale protein expression and purification --- p.73 / Chapter 5.2.4 --- Dynamic light scattering measurement --- p.76 / Chapter 5.2.5 --- Crystallization screening GULP1-PTB --- p.76 / Chapter 5.2.6 --- Optimization of GULP1-PTB crystals by grid screen --- p.76 / Chapter 5.2.7 --- Optimization of GULPl -PTB crystals by additive screen and detergent screen --- p.79 / Chapter 5.3 --- Results --- p.79 / Chapter 5.3.1 --- Large-scale expression and purification of GULP 1-PTB --- p.79 / Chapter 5.3.2 --- Small-scale expression and purification of GULP1T35d-PTB --- p.86 / Chapter 5.3.3 --- Crystallization screening and optimization --- p.88 / Chapter 5.4 --- Discussion --- p.91 / Chapter Chapter 6 --- Conclusion and future perspective --- p.94 / Chapter 6.1 --- Conclusion --- p.94 / Chapter 6.2 --- Future perspective --- p.95 / References --- p.98