Rozprawy doktorskie: „Computational analyses protein”

1

Croft, Edward. "Computational analyses of protein-ligand interactions". Thesis, University of York, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.265562.

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2

Haider, Kamran. "Computational analyses of protein-ligand interactions". Thesis, University of York, 2010. http://etheses.whiterose.ac.uk/1242/.

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Protein-ligand interactions have a central role in all processes in living systems. A comprehensive understanding of protein interactions with small molecules is of great interest as it provides opportunities for understanding protein function and therapeutic intervention. The major aims of this thesis were to characterise protein-ligand interactions from databases of crystal structures and to apply molecular modelling techniques for accurate prediction of binding modes of molecular fragments in protein binding sites. The first aspect of the project was the analysis of hydrogen bond donors and acceptors in 187 protein-ligand complexes of resolution 2.5Å or better. The results showed that an extremely small fraction of them were not explicitly hydrogen bonded, with the hydrogen bond criterion of donor-acceptor distance ≤ 3.5 Å and H-bond angle of ≥ 90°. It was also noticed that a vast majority of such cases were explicable on the basis of weak interactions and weak donor/acceptor strength. The results were consistent with reported observations for buried protein regions. In a series of docking calculations, the fraction of lost hydrogen bonds was evaluated as a discriminator of good versus bad docking poses. Docking and scoring with a standard program, rDock, did not create incorrect poses with missing hydrogen bonds to an extent that would make lost hydrogen bonds a strong discriminator. The second aspect of the research is related to weak (CH-π and XH-π, X=N,O,S) interactions. In a survey of IsoStar, a database of protein-ligand interactions, subtle differences were noticed in geometric parameters of π interactions involving different types of ligand aromatic rings with strong and weak donor groups in binding sites. The results supported the hypothesis that energetically favourable interaction patterns are more frequent when there are electron-donating substituents attached to the aromatic ring. Finally, the applicability of a modelling technique, multiple copy simultaneous search, in terms of predicting energetically favourable poses of solvents and fragments in target binding sites, was explored in detail. Several factors such as re-scoring with a better scoring function, use of multiple receptor structures and good quality prediction of water binding sites led to a robust protocol for high quality predictions of fragment binding in test datasets.

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3

Haberman, N. "Insights into protein-RNA complexes from computational analyses of iCLIP experiments". Thesis, University College London (University of London), 2017. http://discovery.ucl.ac.uk/1568450/.

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RNA-binding proteins (RBPs) are the primary regulators of all aspects of post-transcriptional gene regulation. In order to understand how RBPs perform their function, it is important to identify their binding sites. Recently, new techniques have been developed to employ high-throughput sequencing to study protein-RNA interactions in vivo, including the individual-nucleotide resolution UV crosslinking and immunoprecipitation (iCLIP). iCLIP identifies sites of protein-RNA crosslinking with nucleotide resolution in a transcriptome-wide manner. It is composed of over 60 steps, which can be modified, but it is not clear how variations in the method affect the assignment of RNA binding sites. This is even more pertinent given that several variants of iCLIP have been developed. A central question of my research is how to correctly assign binding sites to RBPs using the data produced by iCLIP and similar techniques. I first focused on the technical analyses and solutions for the iCLIP method. I examined cDNA deletions and crosslink-associated motifs to show that the starts of cDNAs are appropriate to assign the crosslink sites in all variants of CLIP, including iCLIP, eCLIP and irCLIP. I also showed that the non-coinciding cDNA-starts are caused by technical conditions in the iCLIP protocol that may lead to sequence constraints at cDNA-ends in the final cDNA library. I also demonstrated the importance of fully optimizing the RNase and purification conditions in iCLIP to avoid these cDNA-end constraints. Next, I developed CLIPo, a computational framework that assesses various features of iCLIP data to provide quality control standards which reveals how technical variations between experiments affect the specificity of assigned binding sites. I used CLIPo to compare multiple PTBP1 experiments produced by iCLIP, eCLIP and irCLIP, to reveal major effects of sequence constraints at cDNA-ends or starts, cDNA length distribution and non-specific contaminants. Moreover, I assessed how the variations between these methods influence the mechanistic conclusions. Thus, CLIPo presents the quality control standards for transcriptome-wide assignment of protein-RNA binding sites. I continued with analyses of RBP complexes by using data from spliceosome iCLIP. This method simultaneously detects crosslink sites of small nuclear ribonucleo proteins (snRNPs) and auxiliary splicing factors on pre-mRNAs. I demonstrated that the high resolution of spliceosome-iCLIP allows for distinction between multiple proximal RNA binding sites, which can be valuable for transcriptome-wide studies of large ribonucleo protein complexes. Moreover, I showed that spliceosome-iCLIP can experimentally identify over 50,000 human branch points. In summary, I detected technical biases from iCLIP data, and demonstrated how such biases can be avoided, so that cDNA-starts appropriately assign the RNA binding sites. CLIPo analysis proved a useful quality control tool that evaluates data specificity across different methods, and I applied it to iCLIP, irCLIP and ENCODE eCLIP datasets. I presented how spliceosome-iCLIP data can be used to study the splicing machinery on pre-mRNAs and how to use constrained cDNAs from spliceosome-iCLIP data to identify branch points on a genome-wide scale. Taken together, these studies provide new insights into the field of RNA biology and can be used for future studies of iCLIP and related methods.

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4

Henricson, Anna. "Analyses of protein evolution, function, and architecture". Stockholm, 2010. http://diss.kib.ki.se/2010/978-91-7409-753-5/.

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5

Yan, Yongpan. "Computational analyses of microbial genomes operons, protein families and lateral gene transfer /". College Park, Md. : University of Maryland, 2005. http://hdl.handle.net/1903/2596.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2005.
Thesis research directed by: Cell Biology & Molecular Genetics. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

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6

Rajapaksha, Suneth P. "Single Molecule Spectroscopy Studies of Membrane Protein Dynamics and Energetics by Combined Experimental and Computational Analyses". Bowling Green State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1337141955.

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7

Chegancas, Rito Tiago Miguel. "Modelling and comparing protein interaction networks using subgraph counts". Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:dcc0eb0d-1dd8-428d-b2ec-447a806d6aa8.

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The astonishing progress of molecular biology, engineering and computer science has resulted in mature technologies capable of examining multiple cellular components at a genome-wide scale. Protein-protein interactions are one example of such growing data. These data are often organised as networks with proteins as nodes and interactions as edges. Albeit still incomplete, there is now a substantial amount of data available and there is a need for biologically meaningful methods to analyse and interpret these interactions. In this thesis we focus on how to compare protein interaction networks (PINs) and on the rela- tionship between network architecture and the biological characteristics of proteins. The underlying theme throughout the dissertation is the use of small subgraphs – small interaction patterns between 2-5 proteins. We start by examining two popular scores that are used to compare PINs and network models. When comparing networks of the same model type we find that the typical scores are highly unstable and depend on the number of nodes and edges in the networks. This is unsatisfactory and we propose a method based on non-parametric statistics to make more meaningful comparisons. We also employ principal component analysis to judge model fit according to subgraph counts. From these analyses we show that no current model fits to the PINs; this may well reflect our lack of knowledge on the evolution of protein interactions. Thus, we use explanatory variables such as protein age and protein structural class to find patterns in the interactions and subgraphs we observe. We discover that the yeast PIN is highly heterogeneous and therefore no single model is likely to fit the network. Instead, we focus on ego-networks containing an initial protein plus its interacting partners and their interaction partners. In the final chapter we propose a new, alignment-free method for network comparison based on such ego-networks. The method compares subgraph counts in neighbourhoods within PINs in an averaging, many-to-many fashion. It clusters networks of the same model type and is able to successfully reconstruct species phylogenies solely based on PIN data providing exciting new directions for future research.

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8

Jonsson, Pall Freyr. "Computational analysis of protein-protein interaction networks". Thesis, University College London (University of London), 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.439848.

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9

Wang, Kai. "Novel computational methods for accurate quantitative and qualitative protein function prediction /". Thesis, Connect to this title online; UW restricted, 2005. http://hdl.handle.net/1773/11488.

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10

Ansari, Sam. "Analysis of protein-protein interactions : a computational approach /". Saarbrücken : VDM Verl. Dr. Müller, 2007. http://deposit.d-nb.de/cgi-bin/dokserv?id=2992987&prov=M&dok_var=1&dok_ext=htm.

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11

Taroni, Chiara. "Computational analysis of protein-carbohydrate interactions". Thesis, University College London (University of London), 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.300932.

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12

Lavallée-Adam, Mathieu. "Protein-protein interaction confidence assessment and network clustering computational analysis". Thesis, McGill University, 2014. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=121237.

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Protein-protein interactions represent a crucial source of information for the understanding of the biological mechanisms of the cell. In order to be useful, high quality protein-protein interactions must be computationally extracted from the noisy datasets produced by high-throughput experiments such as affinity purification. Even when filtered protein-protein interaction datasets are obtained, the task of analyzing the network formed by these numerous interactions remains tremendous. Protein-protein interaction networks are large, intricate, and require computational approaches to provide meaningful biological insights. The overall objective of this thesis is to explore algorithms assessing the quality of protein-protein interactions and facilitating the analysis of their networks. This work is divided into four results: 1) a novel Bayesian approach to model contaminants originating from affinity purifications, 2) a new method to identify and evaluate the quality of protein-protein interactions independently in different cell compartments, 3) an algorithm computing the statistical significance of clusterings of proteins sharing the same functional annotation in protein-protein interaction networks, and 4) a computational tool performing sequence motif discovery in 5' untranslated regions as well as evaluating the clustering of such motifs in protein-protein interaction networks.
Les interactions protéine-protéine représentent une source d'information essentielle à la compréhension des divers méchanismes biologiques de la cellule. Cependant, les expériences à haut débit qui identifient ces interactions, comme la purification par affinité, produisent un très grand nombre de faux-positifs. Des méthodes computationelles sont donc requises afin d'extraire de ces ensembles de données les interactions protéine-protéine de grande qualité. Toutefois, même lorsque filtrés, ces ensembles de données forment des réseaux très complexes à analyser. Ces réseaux d'interactions protéine-protéine sont d'une taille importante, d'une grande complexité et requièrent des approches computationelles sophistiquées afin d'en retirer des informations possédant une réelle portée biologique. L'objectif de cette thèse est d'explorer des algorithmes évaluant la qualité d'interactions protéine-protéine et de faciliter l'analyse des réseaux qu'elles composent. Ce travail de recherche est divisé en quatre principaux résultats: 1) une nouvelle approche bayésienne permettant la modélisation des contaminants provenant de la purification par affinité, 2) une nouvelle méthode servant à la découverte et l'évaluation de la qualité d'interactions protéine-protéine à l'intérieur de différents compartiments de la cellule, 3) un algorithme détectant les regroupements statistiquement significatifs de protéines partageant une même annotation fonctionnelle dans un réseau d'interactions protéine-protéine et 4) un outil computationel qui a pour but la découverte de motifs de séquences dans les régions 5' non traduites tout en évaluant le regroupement de ces motifs dans les réseaux d'interactions protéine-protéine.

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13

Maccallum, Robert Matthew. "Computational analysis of protein sequence and structure". Thesis, University College London (University of London), 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.285202.

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Kim, Wan Kyu. "Computational analysis of protein interactions and interfaces". Thesis, University of Cambridge, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.612782.

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Hayashida, Morihiro. "Computational Analysis and Inference of Protein-Protein Interactions from Domain Information". 京都大学 (Kyoto University), 2005. http://hdl.handle.net/2433/68888.

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16

Enright, A. J. "Computational analysis of protein function within complete genomes". Thesis, University of Cambridge, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.598854.

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Since the advent of complete genome sequencing, vast amounts of nucleotide and amino acid sequence data have been produced. These data need to be effectively analysed and verified so that they may be used for biological discovery. A significant proportion of predicted protein sequences from these complete genomes have poorly characterised or unknown functional annotations. This thesis describes a number of approaches which detail the computational analysis of amino acid sequences for the prediction and analysis of protein function within complete genomes. The first chapter is a short introduction to computational genome analysis while the second and third chapters describe how groups of related protein sequences (termed protein families) may be characterised using sequence clustering algorithms. Two novel and complementary sequence clustering algorithms will be presented together with details of how protein family information can be used to detect and describe the functions of proteins in complete genomes. Further research is described which uses this protein family information to analyse the molecular evolution of proteins within complete genomes. Recent developments in genome analysis have shown that the computational prediction of protein function in complete genomes is not limited to pure sequence homology methods. So called genome context method use other information from complete genome sequences to predict protein function. Examples of this include gene location or neighbourhood analysis and the analysis of the phyletic distribution of protein sequences. In chapter four a novel method for predicting whether two proteins are functionally associated or physically interact is described. This method is based on the detection of gene-fusion events within complete genomes. During this research many novel tools and methods for genome analysis, data-visualisation, data-mining and high-performance biological computing were developed. Many of these tools represent interesting research projects in their own right, and formed the basis from which the research carried out in this thesis was conducted. Within the final chapter a selection of these novel algorithms developed during this research is described.

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17

Terribilini, Michael Joseph. "Computational analysis and prediction of protein-RNA interactions". [Ames, Iowa : Iowa State University], 2008.

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18

Di, 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.

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With the advent of modern sequencing technologies, the amount of biological data available has begun to challenge our ability to process it. The development of new tools and methods has become essential for the production of results based on such a vast amount of information. This thesis focuses on the development of such computational tools and method for the study of protein data. I first present the work done towards the understanding of intrinsic protein disorder. Through the development of novel disorder predictors, we were able to expand the available data sources to cover any protein of known sequence. By storing these predicted annotations, together with data from other sources, we created MobiDB, a resource that provides a comprehensive view of available disorder annotations for a protein of interest, covering all sequences in the UniProt database. Based on observations obtained from this resource, we proceeded to create a data analysis workflow with the goal of furthering our understanding of intrinsic protein disorder. The second part focuses on tandem repeat proteins. The RAPHAEL method was developed to assist in the identification of tandem repeat protein structures from PDB files. Identified repeat structures were then manually classified into a formal classification schema, and published as part of the RepeatsDB database. Finally, I describe the development of network-based tools for the analysis of protein data. RING allows the user to visualise and study the structure of a protein as a network of nodes, linked by physico-chemical properties. The second method, PANADA, enables the user to create protein similarity networks and to assess the transferability of functional annotations between clusters of proteins.
Con 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.

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19

McDonald, Ian Kevin. "Computational analysis of intramolecular interactions in proteins". Thesis, University College London (University of London), 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.338865.

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20

Zotenko, 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.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2007.
Thesis 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.

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21

Fink, Florian [Verfasser], i Elmar [Akademischer Betreuer] Lang. "Computational analysis of docked protein-protein complexes / Florian Fink. Betreuer: Elmar Lang". Regensburg : Universitätsbibliothek Regensburg, 2011. http://d-nb.info/1022819976/34.

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Wróblewska, Liliana. "Refinement of reduced protein models with all-atom force fields". Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/26606.

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The goal of the following thesis research was to develop a systematic approach for the refinement of low-resolution protein models, as a part of the protein structure prediction procedure. Significant progress has been made in the field of protein structure prediction and the contemporary methods are able to assemble correct topology for a large fraction of protein domains. But such approximate models are often not detailed enough for some important applications, including studies of reaction mechanisms, functional annotation, drug design or virtual ligand screening. The development of a method that could bring those structures closer to the native is then of great importance. The minimal requirements for a potential that can refine protein structures is the existence of a correlation between the energy with native similarity and the scoring of the native structure as being lowest in energy. Extensive tests of the contemporary all-atom physics-based force fields were conducted to assess their applicability for refinement. The tests revealed flatness of such potentials and enabled the identification of the key problems in the current approaches. Guided by these results, the optimization of the AMBER (ff03) force field was performed that aimed at creating a funnel shape of the potential, with the native structure at the global minimum. Such shape should facilitate the conformational search during refinement and drive it towards the native conformation. Adjusting the relative weights of particular energy components, and adding an explicit hydrogen bond potential significantly improved the average correlation coefficient of the energy with native similarity (from 0.25 for the original ff03 potential to 0.65 for the optimized force field). The fraction of proteins for which the native structure had lowest energy increased from 0.22 to 0.90. The new, optimized potential was subsequently used to refine protein models of various native-similarity. The test employed 47 proteins and 100 decoy structures per protein. When the lowest energy structure from each trajectory was compared with the starting decoy, we observed structural improvement for 70% of the models on average. Such an unprecedented result of a systematic refinement is extremely promising in the context of high-resolution structure prediction.

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SCARDONI, Giovanni. "Computational Analysis of Biological networks". Doctoral thesis, Università degli Studi di Verona, 2010. http://hdl.handle.net/11562/343983.

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Caratterizzare, descrivere ed estrarre informazioni da un network, è sicuramente uno dei principali obbiettivi della scienza, dato che lo studio dei network interessa differenti campi della ricerca, come la biologia, l'economia, le scienze sociali, l'informatica e così via. Ciò che si vuole è riuscire ad estrarre le proprietà fondamentali dei network e comprenderne la funzionalità. Questa tesi riguarda sia l'analisi topologica che l' analisi dinamica dei network biologici, anche se i risultati possono essere applicati a diversi campi. Per quanto riguarda l'analisi topologica viene utilizzato un approccio orientato ai nodi, utilizzando le centralità per individuare i nodi più rilevanti e integrando tali risultati con dati da laboratorio. Viene inoltre descritto CentiScaPe, un software implementato per effettuare tale tipo di analisi. Vengono inoltre introdotti i concetti di "interference" e "robustness" che permettono di comprendere come un network si riarrangia in seguito alla rimozione o all'aggiunta di nodi. Per quanto riguarda l'analisi dinamica, si mostra come l'abstract interpretation può essere utilizzata nella simulazione di pathways per ottenere i risultati di migliaia di simulazioni in breve tempo e come possibile soluzione del problema della stima dei parametri mancanti.
This thesis, treating both topological and dynamic points of view, concerns several aspects of biological networks analysis. Regarding the topological analysis of biological networks, the main contribution is the node-oriented point of view of the analysis. It means that instead of concentrating on global properties of the networks, we analyze them in order to extract properties of single nodes. An excellent method to face this problem is to use node centralities. Node centralities allow to identify nodes in a network having a relevant role in the network structure. This can not be enough if we are dealing with a biological network, since the role of a protein depends also on its biological activity that can be detected with lab experiments. Our approach is to integrate centralities analysis and data from biological experiments. A protocol of analysis have been produced, and the CentiScaPe tool for computing network centralities and integrating topological analysis with biological data have been designed and implemented. CentiScaPe have been applied to a human kino-phosphatome network and according to our protocol, kinases and phosphatases with highest centralities values have been extracted creating a new subnetwork of most central kinases and phosphatases. A lab experiment established which of this proteins presented high activation level and through CentiScaPe the proteins with both high centrality values and high activation level have been easily identified. The notion of node centralities interference have also been introduced to deal with central role of nodes in a biological network. It allow to identify which are the nodes that are more affected by the remotion of a particular node measuring the variation on their centralities values when such a node is removed from the network. The application of node centralities interference to the human kino-phosphatome revealed that different proteins affect centralities values of different nodes. Similarly to node centralities interference, the notion of centrality robustness of a node is introduced. This notion reveals if the central role of a node depends on other particular nodes in the network or if the node is ``robust'' in the sense that even if we remove or add other nodes the central role of the node remains almost unchanged. The dynamic aspects of biological networks analysis have been treated from an abstract interpretation point of view. Abstract interpretation is a powerful framework for the analysis of software and is excellent in deriving numerical properties of programs. Dealing with pathways, abstract interpretation have been adapted to the analysis of pathways simulation. Intervals domain and constants domain have been succesfully used to automatically extract information about reactants concentration. The intervals domain allow to determine the range of concentration of the proteins, and the constants domain have been used to know if a protein concentration become constant after a certain time. The other domain of analysis used is the congruences domain that, if applied to pathways simulation can easily identify regular oscillating behaviour in reactants concentration. The use of abstract interpretation allows to execute thousands of simulation and to completely and automatically characterize the behaviour of the pathways. In such a way it can be used also to solve the problem of parameters estimation where missing parameters can be detected with a brute force algorithm combined with the abstract interpretation analysis. The abstract interpretation approach have been succesfully applied to the mitotic oscillator pathway, characterizing the behaviour of the pathway depending on some reactants. To help the analysis of relation between reactants in the network, the notions of variables interference and variables abstract interference have been introduced and adapted to biological pathways simulation. They allow to find relations between properties of different reactants of the pathway. Using the abstract interference techniques we can say, for instance, which range of concentration of a protein can induce an oscillating behaviour of the pathway.

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Zhan, Bill Shili. "Computational mutagenesis models for protein activity and stability analysis". Fairfax, VA : George Mason University, 2007. http://hdl.handle.net/1920/2989.

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Thesis (Ph. D.)--George Mason University, 2007.
Title from PDF t.p. (viewed Jan. 22, 2008). Thesis director: Iosif I. Vaisman. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Bioinformatics. Vita: p. 140. Includes bibliographical references (p. 133-139). Also available in print.

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Dobbins, Sara E. "Computational Studies of Protein Flexibility using Normal Mode Analysis". Thesis, Imperial College London, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.508940.

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Na, Insung. "Computational Analysis of Protein Intrinsic Disorder in Human Diseases". Thesis, University of South Florida, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10603424.

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There are different conformational states of proteins characterized by different Gibbs free energy levels, manifested in folding-unfolding dynamics, for example. Recently, a set of protein states, which require relatively small amount of folding energies, emerged as subjects of intensive research, and proteins or regions characterized by the presence of these states have been termed as ‘Intrinsically Disordered Proteins’ (IDP) and ‘Intrinsically Disordered Protein Regions’ (IDPR), respectively. Predisposition for intrinsic disorder of a query protein is encoded in its amino acid sequence and composition, and can be rather accurately predicted using several intrinsic disorder algorithms. Since pathology of many human diseases can be driven by proteins characterized by high intrinsic disorder scores, research on various disease-associated proteins is often started with the analysis of their intrinsic disorder propensities. In this work, I utilized computational approaches based on the concept of intrinsic disorder to address three health-related issues. To this end, I developed a novel computational platform for disorder-based drug discovery and applied this tool for finding inhibitors of the cancer-related MBD2-NuRD complex, utilized molecular dynamic simulations to explain the effects of mutations on the functionality of the X-linked protoporphyria-related protein ALAS, and used bioinformatics tools to examine the effects of cardiomyopathy-related mutations in cardiac troponin.

Since the complex between the Methyl-CpG-binding domain protein 2 (MBD2) and the Nucleosome Remodeling Deacetylase complex (NuRD) specifically binds to the mCpG-island and blocks tumor suppressor gene expression, finding an inhibitor of this MBD2-NuRD complex is hypothesized to be important for the development of novel anti-cancer drugs. I found that the site, which is responsible for the MBD2 interaction with the transcriptional repressor p66-α (p66α, which is a part of the NuRD complex), is characterized by a specific disorder-to-order transition pattern, this pattern showed a remarkable similarity to the disorder-to-order pattern of the Myc transcription factor binding site for the Max transcription factor. Importantly, several inhibitors of the Myc-Max interaction targeting the disorder-to-order transition site of Myc were previously described. By applying molecular docking at the disorder-to-order transition site of MBD2, two compounds were identified and further evaluated through molecular dynamics simulations. Anti-leukemia and anti-metastasis effectiveness of these compounds was demonstrated in dedicated in vitro and in vivo experiments conducted by our collaborators.

In relation to the defective protein associated with the X-linked protoporphyria (XLPP), the hepta-variant of mouse erythroid 5-aminolevulinate synthase (mALAS2), previously shown to be characterized by a remarkable acceleration of the reaction rate, was investigated through molecular dynamics simulations. In this study, a loop to β-strand transition was observed, and this observation was crucial for a better understanding of the previously described rate-enhancing effects of seven simultaneous variations in the active loop site of this protein.

Finally, a wide spectrum of bioinformatics tools was applied to carefully analyze a potential role of intrinsic disorder in a set of cardiomyopathy-related mutations in the components of human cardiac troponin. This analysis revealed that, in comparison with the wild type troponin, chains containing the disease-associated mutations were typically characterized by a local decrease in intrinsic disorder propensity. These mutations affected some disorder-based protein-protein interaction sites and caused remarkable rearrangements of the complex pattern of post-translational modifications.

Therefore, this work illustrates that inclusion of the protein intrinsic disorder analysis into the arsenal of techniques used by the biomedical researchers represents an important and promising approach that provides novel inputs for the better understanding of protein behavior in relation to human disease at the molecular level. Techniques and methods developed and utilized in this study will significantly contribute to future biomedical research.

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27

Zhang, Wei. "Computational simulation of biological systems studies on protein folding and protein structure prediction /". Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file 2.84Mb, 184 p, 2005. http://wwwlib.umi.com/dissertations/fullcit/3181881.

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28

Milburn, Duncan. "Computational analysis of the functional sites in proteins". Thesis, University College London (University of London), 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.325850.

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29

Siu, Wing-yan, i 蕭穎欣. "Multiple structural alignment for proteins". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B4068748X.

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30

Baldan, Nikita <1996&gt. "Computational analysis of NaV1.7 protein variants and tool for 3D visualization of protein structures". Master's Degree Thesis, Università Ca' Foscari Venezia, 2020. http://hdl.handle.net/10579/17572.

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This thesis is composed of two parts. The first part explores the possibility to use Graph Kernels to discriminate pathogenic versus non-pathogenic variants of a specific protein. All variants are represented as Residue Interaction Networks (RIN), where nodes are amino acids and edges represent non-covalent bonds between atoms of the two involved amino acids. This part is guided by a previous Master degree thesis that considered protein NaV1.7, which is responsible for the transmission of the pain signal from the peripheral nervous system to the brain. The thesis considered 85 genetic variants of the human NaV1.7, among which 30 are known to cause neuropathic diseases and 55 are instead neutral. The results of the first part highlight that some Graph Kernels are actually able to discriminate between pathogenic and neutral variants. This prompted the idea of realizing a 3D viewer able to show the three-dimensional structure of a protein and also its non-covalent bonds. The second part of the thesis describes Spheremole, an application for the visualization of the three-dimensional structure of a protein. In particular, Spheremole allows the visualization of two proteins structures and their visual comparison, also based on their non-covalent bonds.

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31

Ganapathy, Ashwin. "Computational analysis of protein identification using peptide mass fingerprinting approach /". free to MU campus, to others for purchase, 2004. http://wwwlib.umi.com/cr/mo/fullcit?p1426056.

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32

Bartoli, Lisa <1980&gt. "Computational methods for the analysis of protein structure and function". Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2009. http://amsdottorato.unibo.it/1225/1/Bartoli_Lisa_tesi.pdf.

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The vast majority of known proteins have not yet been experimentally characterized and little is known about their function. The design and implementation of computational tools can provide insight into the function of proteins based on their sequence, their structure, their evolutionary history and their association with other proteins. Knowledge of the three-dimensional (3D) structure of a protein can lead to a deep understanding of its mode of action and interaction, but currently the structures of <1% of sequences have been experimentally solved. For this reason, it became urgent to develop new methods that are able to computationally extract relevant information from protein sequence and structure. The starting point of my work has been the study of the properties of contacts between protein residues, since they constrain protein folding and characterize different protein structures. Prediction of residue contacts in proteins is an interesting problem whose solution may be useful in protein folding recognition and de novo design. The prediction of these contacts requires the study of the protein inter-residue distances related to the specific type of amino acid pair that are encoded in the so-called contact map. An interesting new way of analyzing those structures came out when network studies were introduced, with pivotal papers demonstrating that protein contact networks also exhibit small-world behavior. In order to highlight constraints for the prediction of protein contact maps and for applications in the field of protein structure prediction and/or reconstruction from experimentally determined contact maps, I studied to which extent the characteristic path length and clustering coefficient of the protein contacts network are values that reveal characteristic features of protein contact maps. Provided that residue contacts are known for a protein sequence, the major features of its 3D structure could be deduced by combining this knowledge with correctly predicted motifs of secondary structure. In the second part of my work I focused on a particular protein structural motif, the coiled-coil, known to mediate a variety of fundamental biological interactions. Coiled-coils are found in a variety of structural forms and in a wide range of proteins including, for example, small units such as leucine zippers that drive the dimerization of many transcription factors or more complex structures such as the family of viral proteins responsible for virus-host membrane fusion. The coiled-coil structural motif is estimated to account for 5-10% of the protein sequences in the various genomes. Given their biological importance, in my work I introduced a Hidden Markov Model (HMM) that exploits the evolutionary information derived from multiple sequence alignments, to predict coiled-coil regions and to discriminate coiled-coil sequences. The results indicate that the new HMM outperforms all the existing programs and can be adopted for the coiled-coil prediction and for large-scale genome annotation. Genome annotation is a key issue in modern computational biology, being the starting point towards the understanding of the complex processes involved in biological networks. The rapid growth in the number of protein sequences and structures available poses new fundamental problems that still deserve an interpretation. Nevertheless, these data are at the basis of the design of new strategies for tackling problems such as the prediction of protein structure and function. Experimental determination of the functions of all these proteins would be a hugely time-consuming and costly task and, in most instances, has not been carried out. As an example, currently, approximately only 20% of annotated proteins in the Homo sapiens genome have been experimentally characterized. A commonly adopted procedure for annotating protein sequences relies on the "inheritance through homology" based on the notion that similar sequences share similar functions and structures. This procedure consists in the assignment of sequences to a specific group of functionally related sequences which had been grouped through clustering techniques. The clustering procedure is based on suitable similarity rules, since predicting protein structure and function from sequence largely depends on the value of sequence identity. However, additional levels of complexity are due to multi-domain proteins, to proteins that share common domains but that do not necessarily share the same function, to the finding that different combinations of shared domains can lead to different biological roles. In the last part of this study I developed and validate a system that contributes to sequence annotation by taking advantage of a validated transfer through inheritance procedure of the molecular functions and of the structural templates. After a cross-genome comparison with the BLAST program, clusters were built on the basis of two stringent constraints on sequence identity and coverage of the alignment. The adopted measure explicity answers to the problem of multi-domain proteins annotation and allows a fine grain division of the whole set of proteomes used, that ensures cluster homogeneity in terms of sequence length. A high level of coverage of structure templates on the length of protein sequences within clusters ensures that multi-domain proteins when present can be templates for sequences of similar length. This annotation procedure includes the possibility of reliably transferring statistically validated functions and structures to sequences considering information available in the present data bases of molecular functions and structures.

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33

Bartoli, Lisa <1980&gt. "Computational methods for the analysis of protein structure and function". Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2009. http://amsdottorato.unibo.it/1225/.

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The vast majority of known proteins have not yet been experimentally characterized and little is known about their function. The design and implementation of computational tools can provide insight into the function of proteins based on their sequence, their structure, their evolutionary history and their association with other proteins. Knowledge of the three-dimensional (3D) structure of a protein can lead to a deep understanding of its mode of action and interaction, but currently the structures of <1% of sequences have been experimentally solved. For this reason, it became urgent to develop new methods that are able to computationally extract relevant information from protein sequence and structure. The starting point of my work has been the study of the properties of contacts between protein residues, since they constrain protein folding and characterize different protein structures. Prediction of residue contacts in proteins is an interesting problem whose solution may be useful in protein folding recognition and de novo design. The prediction of these contacts requires the study of the protein inter-residue distances related to the specific type of amino acid pair that are encoded in the so-called contact map. An interesting new way of analyzing those structures came out when network studies were introduced, with pivotal papers demonstrating that protein contact networks also exhibit small-world behavior. In order to highlight constraints for the prediction of protein contact maps and for applications in the field of protein structure prediction and/or reconstruction from experimentally determined contact maps, I studied to which extent the characteristic path length and clustering coefficient of the protein contacts network are values that reveal characteristic features of protein contact maps. Provided that residue contacts are known for a protein sequence, the major features of its 3D structure could be deduced by combining this knowledge with correctly predicted motifs of secondary structure. In the second part of my work I focused on a particular protein structural motif, the coiled-coil, known to mediate a variety of fundamental biological interactions. Coiled-coils are found in a variety of structural forms and in a wide range of proteins including, for example, small units such as leucine zippers that drive the dimerization of many transcription factors or more complex structures such as the family of viral proteins responsible for virus-host membrane fusion. The coiled-coil structural motif is estimated to account for 5-10% of the protein sequences in the various genomes. Given their biological importance, in my work I introduced a Hidden Markov Model (HMM) that exploits the evolutionary information derived from multiple sequence alignments, to predict coiled-coil regions and to discriminate coiled-coil sequences. The results indicate that the new HMM outperforms all the existing programs and can be adopted for the coiled-coil prediction and for large-scale genome annotation. Genome annotation is a key issue in modern computational biology, being the starting point towards the understanding of the complex processes involved in biological networks. The rapid growth in the number of protein sequences and structures available poses new fundamental problems that still deserve an interpretation. Nevertheless, these data are at the basis of the design of new strategies for tackling problems such as the prediction of protein structure and function. Experimental determination of the functions of all these proteins would be a hugely time-consuming and costly task and, in most instances, has not been carried out. As an example, currently, approximately only 20% of annotated proteins in the Homo sapiens genome have been experimentally characterized. A commonly adopted procedure for annotating protein sequences relies on the "inheritance through homology" based on the notion that similar sequences share similar functions and structures. This procedure consists in the assignment of sequences to a specific group of functionally related sequences which had been grouped through clustering techniques. The clustering procedure is based on suitable similarity rules, since predicting protein structure and function from sequence largely depends on the value of sequence identity. However, additional levels of complexity are due to multi-domain proteins, to proteins that share common domains but that do not necessarily share the same function, to the finding that different combinations of shared domains can lead to different biological roles. In the last part of this study I developed and validate a system that contributes to sequence annotation by taking advantage of a validated transfer through inheritance procedure of the molecular functions and of the structural templates. After a cross-genome comparison with the BLAST program, clusters were built on the basis of two stringent constraints on sequence identity and coverage of the alignment. The adopted measure explicity answers to the problem of multi-domain proteins annotation and allows a fine grain division of the whole set of proteomes used, that ensures cluster homogeneity in terms of sequence length. A high level of coverage of structure templates on the length of protein sequences within clusters ensures that multi-domain proteins when present can be templates for sequences of similar length. This annotation procedure includes the possibility of reliably transferring statistically validated functions and structures to sequences considering information available in the present data bases of molecular functions and structures.

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34

Allan, Robert Douglas. "Computational analysis and experimental characterisation of natural antiparallel coiled-coil motifs". Thesis, University of Sussex, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343369.

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35

Joughin, Brian Alan. "Novel methods in computational analysis and design of protein-protein interactions : applications to phosphoregulated interactions". Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/38630.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biology, 2007.
Includes bibliographical references (p. 107-130).
This thesis presents a number of novel computational methods for the analysis and design of protein-protein complexes, and their application to the study of the interactions of phosphopeptides with phosphopeptide-binding domain interactions. A novel protein-protein interaction type, the action-at-a-distance interaction, is described in the complex of the TEM1 P-lactamase with the 3-lactamase inhibitor protein (BLIP). New action-at-a-distance interactions were designed on the surface of BLIP and computed to enhance the affinity of that complex. A new method is described for the characterization and prediction of protein ligand-binding sites. This method was used to analyze the phosphoresidue-contacting sites of known phosphopeptide-binding domains, and to predict the sites of phosphoresidue-contact on some protein domains for which the correct site was not known. The design of a library of variant WW domains that is predicted to be enriched in domains that might have specificity for "pS/pT-Q" peptide ligands is detailed. General methods for designing libraries of degenerate oligonucleotides for expressing protein libraries as accurately as possible are given, and applied to the described WW domain variant library.
by Brian Alan Joughin.
Ph.D.

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36

Mazandu, Gaston Kuzamunu. "Data integration for the analysis of uncharacterized proteins in Mycobacterium tuberculosis". Doctoral thesis, University of Cape Town, 2010. http://hdl.handle.net/11427/11590.

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Includes abstract.
Includes bibliographical references (leaves 126-150).
Mycobacterium tuberculosis is a bacterial pathogen that causes tuberculosis, a leading cause of human death worldwide from infectious diseases, especially in Africa. Despite enormous advances achieved in recent years in controlling the disease, tuberculosis remains a public health challenge. The contribution of existing drugs is of immense value, but the deadly synergy of the disease with Human Immunodeficiency Virus (HIV) or Acquired Immunodeficiency Syndrome (AIDS) and the emergence of drug resistant strains are threatening to compromise gains in tuberculosis control. In fact, the development of active tuberculosis is the outcome of the delicate balance between bacterial virulence and host resistance, which constitute two distinct and independent components. Significant progress has been made in understanding the evolution of the bacterial pathogen and its interaction with the host. The end point of these efforts is the identification of virulence factors and drug targets within the bacterium in order to develop new drugs and vaccines for the eradication of the disease.

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37

Wróblewska, Liliana. "Refinement of reduced protein models with all-atom force fields". Atlanta, Ga. : Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/26606.

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Thesis (Ph.D)--Biology, Georgia Institute of Technology, 2008.
Committee Chair: Skolnick, Jeffrey; Committee Member: Fernandez, Facundo; Committee Member: Jordan, King; Committee Member: McDonald, John; Committee Member: Sherrill, David. Part of the SMARTech Electronic Thesis and Dissertation Collection.

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38

Zeng, Bo Verfasser], Dmitrij [Akademischer Betreuer] Frishman, Dmitrij [Gutachter] Frishman i Dieter [Gutachter] [Langosch. "Computational analysis and prediction of protein interaction sites in transmembrane proteins / Bo Zeng ; Gutachter: Dmitrij Frishman, Dieter Langosch ; Betreuer: Dmitrij Frishman". München : Universitätsbibliothek der TU München, 2019. http://d-nb.info/118563794X/34.

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39

Dyer, Matthew David. "Pathosystems Biology: Computational Prediction and Analysis of Host-Pathogen Protein Interaction Networks". Diss., Virginia Tech, 2003. http://hdl.handle.net/10919/28141.

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An important aspect of systems biology is the elucidation of the protein-protein interactions (PPIs) that control important biological processes within a cell and between organisms. In particular, at the cellular and molecular level, interactions between a pathogen and its host play a vital role in initiating infection and a successful pathogenesis. Despite recent successes in the advancement of the systems biology of model organisms to understand complex diseases, the analysis of infectious diseases at the systems-level has not received as much attention. Since pathogen related disease is responsible for millions of deaths and billions of dollars in damage to crops and livestock, understanding the mechanisms employed by pathogens to infect their hosts is critical in the development of new and effective therapeutic strategies. The research presented here is one of the first computational approaches to studying host-pathogen PPI networks. This dissertation has two main aims. First, we discuss analytical tools for studying host-pathogen networks to identify common pathways perturbed and manipulated by pathogens. We present the first global comparison of the host-pathogen PPI networks of 190 different pathogens and their interactions with human proteins. We also present the construction and analysis of three highly infectious human-bacterial PPI networks: Bacillus anthracis, Francislla tularensis, and Yersinia pestis. The second aim of the research presented here is the development of predictive models for identifying PPIs between host and pathogen proteins. We present two methods: (i) a domain-based approach that uses frequency of domain-pairs in intra-species PPIs, and (ii) a supervised machine learning method that is trained on known inter-species PPIs. The techniques developed in this dissertation, along with the informative datasets presented, will serve as a foundation for the field of computational pathosystems biology.
Ph. D.

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40

Dyer, Matthew D. "Pathosystems Biology: Computational Prediction and Analysis of Host-Pathogen Protein Interaction Networks". Diss., Virginia Tech, 2008. http://hdl.handle.net/10919/28141.

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An important aspect of systems biology is the elucidation of the protein-protein interactions (PPIs) that control important biological processes within a cell and between organisms. In particular, at the cellular and molecular level, interactions between a pathogen and its host play a vital role in initiating infection and a successful pathogenesis. Despite recent successes in the advancement of the systems biology of model organisms to understand complex diseases, the analysis of infectious diseases at the systems-level has not received as much attention. Since pathogen related disease is responsible for millions of deaths and billions of dollars in damage to crops and livestock, understanding the mechanisms employed by pathogens to infect their hosts is critical in the development of new and effective therapeutic strategies. The research presented here is one of the first computational approaches to studying host-pathogen PPI networks. This dissertation has two main aims. First, we discuss analytical tools for studying host-pathogen networks to identify common pathways perturbed and manipulated by pathogens. We present the first global comparison of the host-pathogen PPI networks of 190 different pathogens and their interactions with human proteins. We also present the construction and analysis of three highly infectious human-bacterial PPI networks: Bacillus anthracis, Francislla tularensis, and Yersinia pestis. The second aim of the research presented here is the development of predictive models for identifying PPIs between host and pathogen proteins. We present two methods: (i) a domain-based approach that uses frequency of domain-pairs in intra-species PPIs, and (ii) a supervised machine learning method that is trained on known inter-species PPIs. The techniques developed in this dissertation, along with the informative datasets presented, will serve as a foundation for the field of computational pathosystems biology.
Ph. D.

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41

Dahl, Anna Caroline E. "Membrane protein mechanotransduction : computational studies and analytics development". Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:67798647-8ed5-46e0-bde9-c71235fe70ba.

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Membrane protein mechanotransduction is the altered function of an integral membrane protein in response to mechanical force. Such mechanosensors are found in all kingdoms of life, and increasing numbers of membrane proteins have been found to exhibit mechanosensitivity. How they mechanotransduce is an active research area and the topic of this thesis. The methodology employed is classical molecular dynamics (MD) simulations. MD systems are complex, and two programs were developed to reduce this apparent complexity in terms of both visual abstraction and statistical analysis. Bendix detects and visualises helices as cylinders that follow the helix axis, and quantifies helix distortion. The functionality of Bendix is demonstrated on the symporter Mhp1, where a state is identified that had hitherto only been proposed. InterQuant tracks, categorises and orders proximity between parts of an MD system. Results from multiple systems are statistically interrogated for reproducibility and significant differences at the resolution of protein chains, residues or atoms. Using these tools, the interaction between membrane and the Escherichia coli mechanosensitive channel of small conductance, MscS, is investigated. Results are presented for crystal structures captured in different states, one of which features electron density proposed to be lipid. MD results supports this hypothesis, and identify differential lipid interaction between closed and open states. It is concluded that propensity for lipid to leave for membrane bulk drives MscS state stability. In a subsequent study, MscS is opened by membrane surface tension for the first time in an MD setup. The gating mechanism of MscS is explored in terms of both membrane and protein deformation in response to membrane stretch. Using novel tension methodology and the longest MD simulations of MscS performed to date, a molecular basis for the Dashpot gating mechanism is proposed. Lipid emerges as an active structural element with the capacity to augment protein structure in the protein structure-function paradigm.

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42

Park, Daniel K. (Daniel Kyu). "Web servers, databases, and algorithms for the analysis of protein interaction networks". Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/79146.

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Thesis (S.M.)--Massachusetts Institute of Technology, Computational and Systems Biology Program, 2013.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (p. 41-44).
Understanding the cell as a system has become one of the foremost challenges in the post-genomic era. As a result of advances in high-throughput (HTP) methodologies, we have seen a rapid growth in new types of data at the whole-genome scale. Over the last decade, HTP experimental techniques such as yeast two-hybrid assays and co-affinity purification couple with mass spectrometry have generated large amounts of data on protein-protein interactions (PPI) for many organisms. We focus on the sub-domain of systems biology related to understanding the interactions between proteins that ultimately drive all cellular processes. Representing PPIs as a protein interaction network has proved to be a powerful tool for understanding PPIs at the systems level. In this representation, each node represents a protein and each edge between two nodes represents a physical interaction between the corresponding two proteins. With this abstraction, we present algorithms for the prediction and analysis of such PPI networks as well as web servers and databases for their public availability: 1. In many organisms, the coverage of experimental determined PPI data remains relatively noisy and limited. Given two protein sequences, we describe an algorithm, called Struct2Net, to predict if two proteins physically interact, using insights from structural biology and logistic regression. Furthermore, we create a community-wide web-resource that predicts interactions between any protein sequence pair and provides proteome-wide pre-computed PPI predictions for Homo sapiens, Drosophila melanogaster, and Saccharomyces cerevisiae. 2. Comparative analysis of PPI networks across organisms can provide valuable insights into evolutionary conservation. We describe an algorithm, called IsoRank, for global alignment of multiple PPI networks. The algorithm first constructs an eigenvalue problem that models the network and sequence similarity constraints. The solution of the problem describes a k partite graph that is further processed to find the alignments. Furthermore, we create a communitywide web database, called IsoBase, that provides network alignments and orthology mappings for the most commonly studied eukaryotic model organisms: Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, and Saccharomyces cerevisiae.
by Daniel K. Park.
S.M.

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43

Sweeney, Deacon John. "A Computational Tool for Biomolecular Structure Analysis Based On Chemical and Enzymatic Modification of Native Proteins". Wright State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=wright1316440232.

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44

Gawalapu, Ravi Kumar Root Douglas. "Fluorescence labeling and computational analysis of the strut of myosin's 50 kDa cleft". [Denton, Tex.] : University of North Texas, 2007. http://digital.library.unt.edu/permalink/meta-dc-3974.

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45

Dubey, Anshul. "Search and Analysis of the Sequence Space of a Protein Using Computational Tools". Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/14115.

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A new approach to the process of Directed Evolution is proposed, which utilizes different machine learning algorithms. Directed Evolution is a process of improving a protein for catalytic purposes by introducing random mutations in its sequence to create variants. Through these mutations, Directed Evolution explores the sequence space, which is defined as all the possible sequences for a given number of amino acids. Each variant sequence is divided into one of two classes, positive or negative, according to their activity or stability. By employing machine learning algorithms for feature selection on the sequence of these variants of the protein, attributes or amino acids in its sequence important for the classification into positive or negative, can be identified. Support Vector Machines (SVMs) were utilized to identify the important individual amino acids for any protein, which have to be preserved to maintain its activity. The results for the case of beta-lactamase show that such residues can be identified with high accuracy while using a small number of variant sequences. Another class of machine learning problems, Boolean Learning, was used to extend this approach to identifying interactions between the different amino acids in a proteins sequence using the variant sequences. It was shown through simulations that such interactions can be identified for any protein with a reasonable number of variant sequences. For experimental verification of this approach, two fluorescent proteins, mRFP and DsRed, were used to generate variants, which were screened for fluorescence. Using Boolean Learning, an interacting pair was identified, which was shown to be important for the fluorescence. It was also shown through experiments and simulations that knowing such pairs can increase the fraction active variants in the library. A Boolean Learning algorithm was also developed for this application, which can learn Boolean functions from data in the presence of classification noise.

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46

Hurley, M. G. "Analysis and prediction of the protein folding nucleus using computational and experimental techniques". Thesis, University of Cambridge, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.604825.

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Two novel algorithms were developed to align pairs of domains according to the conservation of the patterns of physical contacts each residue forms with one another. DPCONTACTS uses only residue contact information while DPCONSTRUCT incorporates secondary structure contact types and environment-specific gap penalities. These algorithms were tested against established structure alignment algorithms based on alignment quality compared to hand-curated family and superfamily alignments. When compared to more established algorithms, both DPCONTACTS and DPCONSTRUCT performed comparatively well. A multiple structure-based sequence alignment algorithm, called MDPCONS, was developed to align and score equivalent positions based on similarities of residue contact patterns. Hand-curated fibronectin type III (FnIII) and I-set family alignments were annotated with experimentally derived key folding positions. When compared with other multiple structure and sequence based alignment algorithms, MDPCONS aligned the majority of β-strands and key folding positions correctly. MDPCONS also scored most key folding positions, highly. An FnIII domain was biophysically characterised and identified to use a novel folding nucleus. Integration of this domain into an FNIII alignment allowed the identification of key folding positions to be tested by MDPCONS. MDPCONS aligned three of the four key folding positions correctly. However, there is no evidence to suggest that residues aligned due to similar patterns of contacts are more likely to be involved in folding than for stability. Finally, statistical coupling analysis was performed for the FnIII and I-set domains to identify if key folding positions are conserved through evolution. The clusters identified were found to be important for establishing and maintaining the structure of the Ig fold and were not involved in conserving the folding nucleus.

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47

Gunnarsson, Ida. "Deriving Protein Networks by Combining Gene Expression and Protein Chip Analysis". Thesis, University of Skövde, Department of Computer Science, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-706.

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In order to derive reliable protein networks it has recently been suggested that the combination of information from both gene and protein level is required. In this thesis a combination of gene expression and protein chip analysis was performed when constructing protein networks. Proteins with high affinity to the same substrates and encoded by genes with high correlation is here thought to constitute reliable protein networks. The protein networks derived are unfortunately not as reliable as were hoped for. According to the tests performed, the method derived in this thesis does not perform more than slightly better than chance. However, the poor results can depend on the data used, since mismatching and shortage of data has been evident.

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48

Guo, Weihua. "Computational Modeling of Planktonic and Biofilm Metabolism". Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/79669.

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Most of microorganisms are ubiquitously able to live in both planktonic and biofilm states, which can be applied to dissolve the energy and environmental issues (e.g., producing biofuels and purifying waste water), but can also lead to serious public health problems. To better harness microorganisms, plenty of studies have been implemented to investigate the metabolism of planktonic and/or biofilm cells via multi-omics approaches (e.g., transcriptomics and proteomics analysis). However, these approaches are limited to provide the direct description of intracellular metabolism (e.g., metabolic fluxes) of microorganisms. Therefore, in this study, I have applied computational modeling approaches (i.e., 13C assisted pathway and flux analysis, flux balance analysis, and machine learning) to both planktonic and biofilm cells for better understanding intracellular metabolisms and providing valuable biological insights. First, I have summarized recent advances in synergizing 13C assisted pathway and flux analysis and metabolic engineering. Second, I have applied 13C assisted pathway and flux analysis to investigate the intracellular metabolisms of planktonic and biofilm cells. Various biological insights have been elucidated, including the metabolic responses under mixed stresses in the planktonic states, the metabolic rewiring in homogenous and heterologous chemical biosynthesis, key pathways of biofilm cells for electricity generation, and mechanisms behind the electricity generation. Third, I have developed a novel platform (i.e., omFBA) to integrate multi-omics data with flux balance analysis for accurate prediction of biological insights (e.g., key flux ratios) of both planktonic and biofilm cells. Fourth, I have designed a computational tool (i.e., CRISTINES) for the advanced genome editing tool (i.e., CRISPR-dCas9 system) to facilitate the sequence designs of guide RNA for programmable control of metabolic fluxes. Lastly, I have also accomplished several outreaches in metabolic engineering. In summary, during my Ph.D. training, I have systematically applied computational modeling approaches to investigate the microbial metabolisms in both planktonic and biofilm states. The biological findings and computational tools can be utilized to guide the scientists and engineers to derive more productive microorganisms via metabolic engineering and synthetic biology. In the future, I will apply 13C assisted pathway analysis to investigate the metabolism of pathogenic biofilm cells for reducing their antibiotic resistance.
Ph. D.

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49

Sirota, Leite Fernanda. "Role of the amino acid sequences in domain swapping of the B1 domain of protein G by computation analysis". Doctoral thesis, Universite Libre de Bruxelles, 2007. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210657.

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Domain swapping is a wide spread phenomenon which involves the association between two or more protein subunits such that intra-molecular interactions between domains in each subunit are replaced by equivalent inter-molecular interactions between the same domains in different subunits. This thesis is devoted to the analysis of the factors that drive proteins to undergo such association modes. The specific system analyzed is the monomer to swapped dimer formation of the B1 domain of the immunoglobulin G binding protein (GB1). The formation of this dimer was shown to be fostered by 4 amino acid substitutions (L5V, F30V, Y33F, A34F) (Byeon et al. 2003). In this work, computational protein design and molecular dynamics simulations, both with detailed atomic models, were used to gain insight into how these 4 mutations may promote the domain swapping reaction.

The stability of the wt and quadruple mutant GB1 monomers was assessed using the software DESIGNER, a fully automatic procedure that selects amino acid sequences likely to stabilize a given backbone structure (Wernisch et al. 2000). Results suggest that 3 of the mutations (L5V, F30V, A34F) have a destabilizing effect. The first mutation (L5V) forms destabilizing interactions with surrounding residues, while the second (F30V) is engaged in unfavorable interactions with the protein backbone, consequently causing local strain. Although the A34F substitution itself is found to contribute favorably to the stability of the monomer, this is achieved only at the expense of forcing the wild type W43 into a highly strained conformation concomitant with the formation of unfavorable interactions with both W43 and V54.

Finally, we also provide evidence that A34F mutation stabilizes the swapped dimer structure. Although we were unable to perform detailed protein design calculations on the dimer, due to the lower accuracy of the model, inspection of its 3D structure reveals that the 34F side chains pack against one another in the core of the swapped structure, thereby forming extensive non-native interactions that have no counterparts in the individual monomers. Their replacement by the much smaller Ala residue is suggested to be significantly destabilizing by creating a large internal cavity, a phenomenon, well known to be destabilizing in other proteins. Our analysis hence proposes that the A34F mutation plays a dual role, that of destabilizing the GB1 monomer structure while stabilizing the swapped dimer conformation.

In addition to the above study, molecular dynamics simulations of the wild type and modeled quadruple mutant GB1 structures were carried out at room and elevated temperatures (450 K) in order to sample the conformational landscape of the protein near its native monomeric state, and to characterize the deformations that occur during early unfolding. This part of the study was aimed at investigating the influence of the amino acid sequence on the conformational properties of the GB1 monomer and the possible link between these properties and the swapping process. Analysis of the room temperature simulations indicates that the mutant GB1 monomer fluctuates more than its wild type counter part. In addition, we find that the C-terminal beta-hairpin is pushed away from the remainder of the structure, in agreement with the fact that this hairpin is the structural element that is exchanged upon domain swapping. The simulations at 450 K reveal that the mutant protein unfolds more readily than the wt, in agreement with its decreased stability. Also, among the regions that unfold early is the alpha-helix C-terminus, where 2 out of the 4 mutations reside. NMR experiments by our collaborators have shown this region to display increased flexibility in the monomeric state of the quadruple mutant.

Our atomic scale investigation has thus provided insights into how sequence modifications can foster domain swapping of GB1. Our findings indicate that the role of the amino acid substitutions is to decrease the stability of individual monomers while at the same time increase the stability of the swapped dimer, through the formation of non-native interactions. Both roles cooperate to foster swapping.
Doctorat en sciences, Spécialisation biologie moléculaire
info:eu-repo/semantics/nonPublished

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50

Gawalapu, Ravi Kumar. "Fluorescence labeling and computational analysis of the strut of myosin's 50 kDa cleft". Thesis, University of North Texas, 2007. https://digital.library.unt.edu/ark:/67531/metadc3974/.

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In order to understand the structural changes in myosin S1, fluorescence polarization and computational dynamics simulations were used. Dynamics simulations on the S1 motor domain indicated that significant flexibility was present throughout the molecular model. The constrained opening versus closing of the 50 kDa cleft appeared to induce opposite directions of movement in the lever arm. A sequence called the "strut" which traverses the 50 kDa cleft and may play an important role in positioning the actomyosin binding interface during actin binding is thought to be intimately linked to distant structural changes in the myosin's nucleotide cleft and neck regions. To study the dynamics of the strut region, a method of fluorescent labeling of the strut was discovered using the dye CY3. CY3 served as a hydrophobic tag for purification by hydrophobic interaction chromatography which enabled the separation of labeled and unlabeled species of S1 including a fraction labeled specifically at the strut sequence. The high specificity of labeling was verified by proteolytic digestions, gel electrophoresis, and mass spectroscopy. Analysis of the labeled S1 by collisional quenching, fluorescence polarization, and actin-activated ATPase activity were consistent with predictions from structural models of the probe's location. Although the fluorescent intensity of the CY3 was insensitive to actin binding, its fluorescence polarization was notably affected. Intriguingly, the mobility of the probe increases upon S1 binding to actin suggesting that the CY3 becomes displaced from interactions with the surface of S1 and is consistent with a structural change in the strut due to cleft motions. Labeling the strut reduced the affinity of S1 for actin but did not prevent actin-activated ATPase activity which makes it a potentially useful probe of the actomyosin interface. The different conformations of myosin S1 indicated that the strut is not as flexible as several other key regions of myosin as determined by the application of force constraints to elastic portions of the myosin structure.

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