Дисертації з теми "Protein Structure Networks (PSNs)"
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Zhao, Jing. "Protein Structure Prediction Based on Neural Networks." Thèse, Université d'Ottawa / University of Ottawa, 2013. http://hdl.handle.net/10393/23636.
Повний текст джерелаZotenko, Elena. "Computational methods in protein structure comparison and analysis of protein interaction networks." College Park, Md.: University of Maryland, 2007. http://hdl.handle.net/1903/7621.
Повний текст джерелаThesis research directed by: Dept. of Computer Science. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
Grochow, Joshua A. "On the structure and evolution of protein interaction networks." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/42053.
Повний текст джерелаThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 107-114).
The study of protein interactions from the networks point of view has yielded new insights into systems biology [Bar03, MA03, RSM+02, WS98]. In particular, "network motifs" become apparent as a useful and systematic tool for describing and exploring networks [BP06, MKFV06, MSOI+02, SOMMA02, SV06]. Finding motifs has involved either exact counting (e.g. [MSOI+02]) or subgraph sampling (e.g. [BP06, KIMA04a, MZW05]). In this thesis we develop an algorithm to count all instances of a particular subgraph, which can be used to query whether a given subgraph is a significant motif. This method can be used to perform exact counting of network motifs faster and with less memory than previous methods, and can also be combined with subgraph sampling to find larger motifs than ever before -- we have found motifs with up to 15 nodes and explored subgraphs up to 20 nodes. Unlike previous methods, this method can also be used to explore motif clustering and can be combined with network alignment techniques [FNS+06, KSK+03]. We also present new methods of estimating parameters for models of biological network growth, and present a new model based on these parameters and underlying binding domains. Finally, we propose an experiment to explore the effect of the whole genome duplication [KBL04] on the protein-protein interaction network of S. cerevisiae, allowing us to distinguish between cases of subfunctionalization and neofunctionalization.
by Joshua A. Grochow.
M.Eng.
Tsilo, Lipontseng Cecilia. "Protein secondary structure prediction using neural networks and support vector machines." Thesis, Rhodes University, 2009. http://hdl.handle.net/10962/d1002809.
Повний текст джерелаAlistair, Chalk. "PREDICTION OF PROTEIN SECONDARY STRUCTURE by Incorporating Biophysical Information into Artificial Neural Networks." Thesis, University of Skövde, Department of Computer Science, 1998. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-235.
Повний текст джерелаThis project applied artificial neural networks to the field of secondary structure prediction of proteins. A NETtalk architecture with a window size 13 was used. Over-fitting was avoided by the use of 3 real numbers to represent amino acids, reducing the number of adjustable weights to 840. Two alternative representations of amino acids that incorporated biophysical data were created and tested. They were tested both separately and in combination on a standard 7-fold cross-validation set of 126 proteins. The best performance was achieved using an average result from two predictions. This was then filtered and gave the following results. Accuracy levels for core structures were: Q3total accuracy of 61.3% consisting of Q3 accuracy’s of 54.0%, 38.1% & 77.0% for Helix, Strand and Coil respectively with Matthew’s correlation’s Ca = 0.34, Cb = 0.26 , Cc = 0.31. The average lengths of structures predicted were 9.8, 4.9 and 11.0, for helix, sheet and coil respectively. These results are lower than those of other methods using single sequences and localist representations. The most likely reason for this is over generalisation caused by using a small number of units.
Reyaz-Ahmed, Anjum B. "Protein Secondary Structure Prediction Using Support Vector Machines, Nueral Networks and Genetic Algorithms." Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/cs_theses/43.
Повний текст джерелаMulnaes, Daniel [Verfasser]. "TopSuite: A meta-suite for protein structure prediction using deep neural networks / Daniel Mulnaes." Düsseldorf : Universitäts- und Landesbibliothek der Heinrich-Heine-Universität Düsseldorf, 2020. http://d-nb.info/1222261634/34.
Повний текст джерелаRoyer, Loic. "Unraveling the Structure and Assessing the Quality of Protein Interaction Networks with Power Graph Analysis." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-62562.
Повний текст джерелаPlanas, Iglesias Joan 1980. "On the study of 3D structure of proteins for developing new algorithms to complete the interactome and cell signalling networks." Doctoral thesis, Universitat Pompeu Fabra, 2013. http://hdl.handle.net/10803/104152.
Повний текст джерелаLes proteïnes tenen un paper indispensable en virtualment qualsevol procés biològic. Les funcions de les proteïnes estan determinades per la seva estructura tridimensional (3D) i són coordinades per mitjà d’una complexa xarxa d’interaccions protiques (en anglès, protein-protein interactions, PPIs). Axí doncs, una comprensió en profunditat d’aquestes xarxes és fonamental per entendre la biologia cel•lular. Per a l’anàlisi de les xarxes d’interacció de proteïnes, l’ús de tècniques computacionals ha esdevingut fonamental als darrers temps. Els mètodes in silico aprofiten el coneixement actual sobre les interaccions proteiques per fer prediccions de noves interaccions o de les funcions de les proteïnes. Actualment existeixen diferents mètodes per a la predicció de noves interaccions de proteines. De tota manera, resultats recents demostren que aquests mètodes poden beneficiar-se del coneixement sobre parelles de proteïnes no interaccionants (en anglès, non-interacting pairs, NIPs). Per a la tasca de predir la funció de les proteïnes, el principi de “culpable per associació” (en anglès, guilt by association, GBA) és usat per extendre l’anotació de proteïnes de funció coneguda a través de xarxes d’interacció de proteïnes. En aquesta tesi es presenta un nou mètode pre a la predicció d’interaccions proteiques i un nou protocol basat per a completar xarxes de senyalització cel•lular. iLoops és un mètode que utilitza dades de parells no interaccionants i coneixement de l’estructura 3D de les proteïnes per a predir interaccions de proteïnes. També s’ha desenvolupat un nou protocol per a completar xarxes de senyalització cel•lular, una tasca relacionada amb la predicció de les funcions de les proteïnes. Aquest protocol es basa en aplicar el principi GBA a xarxes d’interaccions proteiques.
Senekal, Frederick Petrus. "Protein secondary structure prediction using amino acid regularities." Diss., Pretoria : [s.n.], 2008. http://upetd.up.ac.za/thesis/available/etd-01232009-120040/.
Повний текст джерелаClayton, Arnshea. "The Relative Importance of Input Encoding and Learning Methodology on Protein Secondary Structure Prediction." Digital Archive @ GSU, 2006. http://digitalarchive.gsu.edu/cs_theses/19.
Повний текст джерелаZhu, Shaoming. "Multiscale analysis of protein functions and stochastic modelling of gene transcriptional regulatory networks." Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/41693/1/Shaoming_Zhu_Thesis.pdf.
Повний текст джерелаSardana, Divya. "Analysis of Meso-scale Structures in Weighted Graphs." University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1510927111275038.
Повний текст джерелаDi, Domenico Tomás. "Computational Analysis and Annotation of Proteome Data: Sequence, Structure, Function and Interactions." Doctoral thesis, Università degli studi di Padova, 2014. http://hdl.handle.net/11577/3423805.
Повний текст джерелаCon l'avvento delle tecnologie di sequenziamento moderne, la quantità di dati biologici disponibili ha cominciato a sfidare la nostra capacità di elaborarli. È diventato quindi essenziale sviluppare nuovi strumenti e tecniche capaci di produrre dei risultati basati su grandi moli di informazioni. Questa tesi si concentra sullo sviluppo di tali strumenti computazionali e dei metodi per lo studio dei dati proteici. Viene dapprima presento il lavoro svolto per la comprensione delle proteine intrinsecamente disordinate. Attraverso lo sviluppo di nuovi predittori di disordine, siamo stati in grado di sfruttare le fonti di dati attualmente disponibili per annotare qualsiasi proteina avente sequenza nota. Memorizzando queste predizioni, insieme ai dati provenienti da altre fonti, è stato creato MobiDB. Questa risorsa fornisce una visione completa sulle annotazioni di disordine disponibili per una qualsiasi proteina di interesse presente nel database UniProt. Sulla base delle osservazioni ottenute da questo strumento, è stato quindi creato un workflow di analisi dei dati con l'obiettivo di approfondire la nostra comprensione delle proteine intrinsecamente disordinate. La seconda parte della tesi si concentra sulle proteine ripetute. Il metodo RAPHAEL è stato sviluppato per contribuire nell'identificazione di strutture proteiche ripetute all'interno dei file PDB. Le strutture selezionate da questo strumento sono state poi catalogate manualmente utilizzando uno schema formale di classificazione, e pubblicate quindi come parte del database RepeatsDB. Infine, viene descritto lo sviluppo di strumenti basati su grafi per l'analisi di dati proteici. RING consente all'utente di visualizzare e studiare la struttura di una proteina come una rete di nodi collegati da tra loro da proprietà fisico-chimiche. Il secondo metodo, PANADA, consente all'utente di creare reti di similarità di proteine e di valutare la trasferibilità delle annotazioni funzionali tra cluster diversi.
Royer, Loic [Verfasser], Michael [Akademischer Betreuer] Schroeder, and Ralf [Gutachter] Zimmer. "Unraveling the Structure and Assessing the Quality of Protein Interaction Networks with Power Graph Analysis / Loic Royer ; Gutachter: Ralf Zimmer ; Betreuer: Michael Schroeder." Dresden : Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2017. http://d-nb.info/1150309210/34.
Повний текст джерелаKarami, Yasaman. "Joint analysis of dynamically correlated networks and coevolved residue clusters : large-scale analysis and methods for predicting the effects of genetic disease associated mutations." Thesis, Paris 6, 2016. http://www.theses.fr/2016PA066375/document.
Повний текст джерелаWe presented COMMA, a method to describe and compare the dynamical architectures of different proteins or different variants of the same protein. COMMA extracts dynamical properties from conformational ensembles to identify communication pathways, chains of residues linked by stable interactions that move together, and independent cliques, clusters of residues that fluctuate in a concerted way. It provides a description of the infostery of a protein or protein complex that goes beyond the notions of chain, domain and secondary structure element/motif, and beyond classical measures of how a protein moves and/or changes its shape. We showed the efficiency of our approach in providing mechanistic insights on the effects of deleterious mutations by pinpointing residues playing key roles in the propagation of these effects. In addition COMMA reveals a link between clusters of coevolving residues and networks of dynamical correlations. It enables to contrast the different types of communication occurring between residues and to hierarchise the different regions of a protein depending on their communication efficiency. Furthermore, we presented an approach to exploit both the sequences and structural dynamics to predict a mutational landscape. The discussion of examples, revealed physical interpretation on how the study of conservation brings significant insights on the sensitivity of conserved positions to mutations. Our proposed method, can detect protein regions that are prone to disorder or substantial conformational rearrangements. Moreover, it enabled us to suggest mutations that regulate the stability of the disordered coiled-coils
Malik, Sheriff Rahuman S. [Verfasser], Eli [Akademischer Betreuer] Zamir, Philippe I. [Gutachter] Bastiaens, and Katja [Gutachter] Ickstadt. "Spatially resolving the dynamics and structure of protein networks in adhesion sites / Rahuman S. Malik Sheriff. Betreuer: Eli Zamir. Gutachter: Philippe I. Bastiaens ; Katja Ickstadt." Dortmund : Universitätsbibliothek Dortmund, 2014. http://d-nb.info/1104947404/34.
Повний текст джерелаVillar, Gabriel. "Aqueous droplet networks for functional tissue-like materials." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:602f9161-368c-48c0-9619-7974f743f2f2.
Повний текст джерелаHellenkamp, Björn Verfasser], Thorsten [Akademischer Betreuer] [Gutachter] [Hugel, Martin [Gutachter] Zacharias, and Ben [Gutachter] Schuler. "Dynamic structure of a multi-domain protein : uncovered using self-consistent FRET networks and time-correlated distance distributions / Björn Hellenkamp ; Gutachter: Thorsten Hugel, Martin Zacharias, Ben Schuler ; Betreuer: Thorsten Hugel." München : Universitätsbibliothek der TU München, 2016. http://d-nb.info/1132773954/34.
Повний текст джерелаToufighi, Kiana 1980. "Integrative study of gene expression and protein complexes." Doctoral thesis, Universitat Pompeu Fabra, 2014. http://hdl.handle.net/10803/380907.
Повний текст джерелаEn las últimas décadas, la emergente vista integrativa de la célula ha triunfado sobre el paradigma histórico: ‘un gene/una proteína/una función’. Esto es ilustrado por los efectos biológicos opuestos de proteínas regulatorias clave en cultivos celulares inmortalizados frente a primarios e in vitro frente a in vivo. El tema persistente en este disertación es la integración de un amplio set de datos para estudiar los distintos contextos celulares. En primer lugar, utilizamos los datos de expresión génica obtenidos de células madre epidérmicas para descubrir las ondas de transcripción expresadas en sintonía con los genes conocidos de los ritmos circadianos. En este estudio demostramos que las respuestas de las células madres a las señales de proliferación/diferenciación dependen de hora del día y el tiempo circadiano es importante para la homeostasis de la piel. Posteriormente, combinamos estos datos de expresión con la información estructural de proteínas y complejos proteicos para describir la regulación temporal de complejos durante el proceso de diferenciación. Por último, mostramos que los complejos de proteínas humanos están compuestos de un ‘núcleo’ estable y una 'periferia' plástica cuya expresión específica de tejido celular permite que los complejos de proteínas funcionen de una manera dependiente del contexto.
Dorn, Márcio. "MOIRAE : a computational strategy to predict 3-D structures of polypeptides." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2012. http://hdl.handle.net/10183/142870.
Повний текст джерелаPereira, José Geraldo de Carvalho. "Redes neurais residuais profundas e autômatos celulares como modelos para predição que fornecem informação sobre a formação de estruturas secundárias proteicas." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/95/95131/tde-03052018-095932/.
Повний текст джерелаThe process of self-organization of the protein structure is known as folding. Although we know the structure of many proteins, for a majority of them, we do not have enough understanding to describe in details how the structure is organized from its amino acid sequence. In this work, we developed two methods for secondary structure prediction using models that have the potential to provide detailed information about the prediction process. One of these models was constructed using cellular automata, a type of dynamic model where it is possible to obtain spatial and temporal information. The other model was developed using deep residual neural networks. With this model it is possible to extract spatial and probabilistic information from its multiple internal layers of convolution. The accuracy of the prediction obtained by this model was ~ 78% for residues that showed consensus in the structure assigned by the DSSP, STRIDE, KAKSI and PROSS methods. Such value is higher than that obtained by other methods which perform the prediction of secondary structures from the amino acid sequence only.
Vijayabaskar, M. S. "Protein-DNA Graphs And Interaction Energy Based Protein Structure Networks." Thesis, 2011. http://etd.iisc.ernet.in/handle/2005/1904.
Повний текст джерелаBhattacharyya, Moitrayee. "Probing Ligand Induced Perturbations In Protien Structure Networks : Physico-Chemical Insights From MD Simulations And Graph Theory." Thesis, 2012. http://etd.iisc.ernet.in/handle/2005/2341.
Повний текст джерелаBrinda, K. V. "Protein Structure Networks : Implications To Protein Stabiltiy And Protein-Protein Interactions." Thesis, 2005. http://etd.iisc.ernet.in/handle/2005/1504.
Повний текст джерелаSykes, JE. "Protein structure and evolution." Thesis, 2021. https://eprints.utas.edu.au/37906/1/Sykes_whole_thesis.pdf.
Повний текст джерелаTsilo, Lipontseng Cecilia. "Protein secondary structure prediction using neural networks and support vector machines /." 2008. http://eprints.ru.ac.za/1675/.
Повний текст джерелаA thesis submitted to Rhodes University in partial fulfillment of the requirements for the degree of Master of Science in Mathematical Statistics.
Ahmed, Hazem Radwan A. "Pattern Discovery in Protein Structures and Interaction Networks." Thesis, 2014. http://hdl.handle.net/1974/12051.
Повний текст джерелаThesis (Ph.D, Computing) -- Queen's University, 2014-04-21 12:54:03.37
Correia, Fernanda Maria dos Reis Brito e. Rodrigues. "Prediction and analysis of biological networks structure and dynamics." Doctoral thesis, 2019. http://hdl.handle.net/10773/29200.
Повний текст джерелаO conhecimento crescente sobre os processos biológicos que regem a dinâmica dos organismos vivos tem potenciado uma melhor compreensão da origem de muitas doenças, assim como a identificação de potenciais alvos terapêuticos. Os sistemas biológicos podem ser modelados através de redes biológicas, permitindo aplicar e explorar métodos da teoria de grafos na sua investigação e caracterização. Este trabalho teve como principal motivação a inferência de padrões e de regras que estão subjacentes à organização de redes biológicas. Através da integração de diferentes tipos de dados, como a expressão de genes, interação entre proteínas e outros conceitos biomédicos, foram desenvolvidos métodos computacionais, para que possam ser usados na previsão e no estudo de doenças. Como primeira contribuição, foi proposto um método de caracterização de um subsistema do interactoma de proteínas humano através das propriedades topológicas das redes que o modelam. Como segunda contribuição, foi utilizado um método não supervisionado que utiliza critérios biológicos e topologia de redes para, através de redes de co-expressão, melhorar a compreensão dos mecanismos genéticos e dos fatores de risco de uma doença. Como terceira contribuição, foi desenvolvida uma metodologia para remover ruído (denoise) em redes de proteínas, para obter modelos mais precisos, utilizando a topologia das redes. Como quarta contribuição, propôs-se uma metodologia supervisionada para modelar a dinâmica do interactoma de proteínas, usando exclusivamente a topologia das redes de interação de proteínas que fazem parte do modelo dinâmico do sistema. As metodologias propostas contribuem para a criação de modelos biológicos, estáticos e dinâmicos, mais precisos, através da identificação e uso de padrões topológicos das redes de interação de proteínas, que podem ser usados na previsão e no estudo doenças.
Programa Doutoral em Engenharia Informática
Lee, Yun, and 李昀. "Prediction of Protein Secondary Structure with Dependency Graphs and Their Expanded Bayesian Networks." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/15829735362099350751.
Повний текст джерела國立清華大學
電機工程學系
93
The completion of Human Genome Project has triggered a wave of investigating various biological problems directly through the string of nucleotides and also its derived amino acid sequence. Therefore, the urgent need of predicting protein three-dimensional structure simply from the amino acid sequence propels us to develop a model-based method to predict the composition of the fundamental structural elements–that is, secondary structures–of any protein chain. To accomplish this goal, we first represent all the eligible secondary structure sequences as specific paths in a secondary structure trellis. Then we employ the method of dependency graphs and their expanded Bayesian networks to quantify the relationship between primary and secondary structures. Following the similar procedure as in the coding theory, we finally assign a secondary structure element to each amino acid through the use of two decoding algorithms: the Viterbi algorithm and the sum-product algorithm. The simulation results reveal that our proposed method achieves an accuracy that is indistinguishable from other existing sequence-only methods, and that a better outcome is reached when the target sequences are confined to a specific protein fold.
Royer, Loic. "Unraveling the Structure and Assessing the Quality of Protein Interaction Networks with Power Graph Analysis." Doctoral thesis, 2010. https://tud.qucosa.de/id/qucosa%3A24399.
Повний текст джерелаHsu, Wei-Lun. "Mechanisms of binding diversity in protein disorder : molecular recognition features mediating protein interaction networks." Thesis, 2014. http://hdl.handle.net/1805/4035.
Повний текст джерелаIntrinsically disordered proteins are proteins characterized by lack of stable tertiary structures under physiological conditions. Evidence shows that disordered proteins are not only highly involved in protein interactions, but also have the capability to associate with more than one partner. Short disordered protein fragments, called “molecular recognition features” (MoRFs), were hypothesized to facilitate the binding diversity of highly-connected proteins termed “hubs”. MoRFs often couple folding with binding while forming interaction complexes. Two protein disorder mechanisms were proposed to facilitate multiple partner binding and enable hub proteins to bind to multiple partners: 1. One region of disorder could bind to many different partners (one-to-many binding), so the hub protein itself uses disorder for multiple partner binding; and 2. Many different regions of disorder could bind to a single partner (many-to-one binding), so the hub protein is structured but binds to many disordered partners via interaction with disorder. Thousands of MoRF-partner protein complexes were collected from Protein Data Bank in this study, including 321 one-to-many binding examples and 514 many-to-one binding examples. The conformational flexibility of MoRFs was observed at atomic resolution to help the MoRFs to adapt themselves to various binding surfaces of partners or to enable different MoRFs with non-identical sequences to associate with one specific binding pocket. Strikingly, in one-to-many binding, post-translational modification, alternative splicing and partner topology were revealed to play key roles for partner selection of these fuzzy complexes. On the other hand, three distinct binding profiles were identified in the collected many-to-one dataset: similar, intersecting and independent. For the similar binding profile, the distinct MoRFs interact with almost identical binding sites on the same partner. The MoRFs can also interact with a partially the same but partially different binding site, giving the intersecting binding profile. Finally, the MoRFs can interact with completely different binding sites, thus giving the independent binding profile. In conclusion, we suggest that protein disorder with post-translational modifications and alternative splicing are all working together to rewire the protein interaction networks.
Dantas, Joana Margarida Franco. "Characterization of extracellular electron transfer networks in Geobacter sulfurreducens, a key bacterium for bioremediation and bioenergy applications." Doctoral thesis, 2017. http://hdl.handle.net/10362/27867.
Повний текст джерела(10137641), Ahmadreza Ghanbarpour Ghouchani. "Applications of Deep Neural Networks in Computer-Aided Drug Design." Thesis, 2021.
Знайти повний текст джерелаSathyapriya, R. "Exploring Protein-Nucleic Acid Interactions Using Graph And Network Approaches." Thesis, 2007. http://hdl.handle.net/2005/624.
Повний текст джерелаPavithra, S. "Functional Role Of Heat Shock Protein 90 From Plasmodium Falciparum." Thesis, 2006. http://hdl.handle.net/2005/433.
Повний текст джерелаFilippi, Michal. "Predikce sekundární struktury proteinu pomocí hlubokých neuronových sítí." Master's thesis, 2017. http://www.nusl.cz/ntk/nusl-365184.
Повний текст джерелаGhosh, Soma. "A Multiscale Modeling Study of Iron Homeostasis in Mycrobacterium Tuberculosis." Thesis, 2014. http://etd.iisc.ernet.in/2005/3519.
Повний текст джерела