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Simons, Kim T. "Deciphering the protein folding code : ab initio prediction of protein structure /." Thesis, Connect to this title online; UW restricted, 1998. http://hdl.handle.net/1773/9234.
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Griffiths-Jones, Samuel R. "Peptide models for protein beta-sheets." Thesis, University of Nottingham, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364650.
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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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Gamalielsson, Jonas. "Models for Protein Structure Prediction by Evolutionary Algorithms." Thesis, University of Skövde, Department of Computer Science, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-623.
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<p>Evolutionary algorithms (EAs) have been shown to be competent at solving complex, multimodal optimisation problems in applications where the search space is large and badly understood. EAs are therefore among the most promising classes of algorithms for solving the Protein Structure Prediction Problem (PSPP). The PSPP is how to derive the 3D-structure of a protein given only its sequence of amino acids. This dissertation defines, evaluates and shows limitations of simplified models for solving the PSPP. These simplified models are off-lattice extensions to the lattice HP model which has been proposed and is claimed to possess some of the properties of real protein folding such as the formation of a hydrophobic core. Lattice models usually model a protein at the amino acid level of detail, use simple energy calculations and are used mainly for search algorithm development. Off-lattice models usually model the protein at the atomic level of detail, use more complex energy calculations and may be used for comparison with real proteins. The idea is to combine the fast energy calculations of lattice models with the increased spatial possibilities of an off-lattice environment allowing for comparison with real protein structures. A hypothesis is presented which claims that a simplified off-lattice model which considers other amino acid properties apart from hydrophobicity will yield simulated structures with lower Root Mean Square Deviation (RMSD) to the native fold than a model only considering hydrophobicity. The hypothesis holds for four of five tested short proteins with a maximum of 46 residues. Best average RMSD for any model tested is above 6Å, i.e. too high for useful structure prediction and excludes significant resemblance between native and simulated structure. Hence, the tested models do not contain the necessary biological information to capture the complex interactions of real protein folding. It is also shown that the EA itself is competent and can produce near-native structures if given a suitable evaluation function. Hence, EAs are useful for eventually solving the PSPP.</p>
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Käll, Lukas. "Predicting transmembrane topology and signal peptides with hidden Markov models /." Stockholm, 2006. http://diss.kib.ki.se/2006/91-7140-719-7/.
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Tångrot, Jeanette. "Structural Information and Hidden Markov Models for Biological Sequence Analysis." Doctoral thesis, Umeå universitet, Institutionen för datavetenskap, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-1629.
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Bioinformatics is a fast-developing field, which makes use of computational methods to analyse and structure biological data. An important branch of bioinformatics is structure and function prediction of proteins, which is often based on finding relationships to already characterized proteins. It is known that two proteins with very similar sequences also share the same 3D structure. However, there are many proteins with similar structures that have no clear sequence similarity, which make it difficult to find these relationships. In this thesis, two methods for annotating protein domains are presented, one aiming at assigning the correct domain family or families to a protein sequence, and the other aiming at fold recognition. Both methods use hidden Markov models (HMMs) to find related proteins, and they both exploit the fact that structure is more conserved than sequence, but in two different ways. Most of the research presented in the thesis focuses on the structure-anchored HMMs, saHMMs. For each domain family, an saHMM is constructed from a multiple structure alignment of carefully selected representative domains, the saHMM-members. These saHMM-members are collected in the so called "midnight ASTRAL set", and are chosen so that all saHMM-members within the same family have mutual sequence identities below a threshold of about 20%. In order to construct the midnight ASTRAL set and the saHMMs, a pipe-line of software tools are developed. The saHMMs are shown to be able to detect the correct family relationships at very high accuracy, and perform better than the standard tool Pfam in assigning the correct domain families to new domain sequences. We also introduce the FI-score, which is used to measure the performance of the saHMMs, in order to select the optimal model for each domain family. The saHMMs are made available for searching through the FISH server, and can be used for assigning family relationships to protein sequences. The other approach presented in the thesis is secondary structure HMMs (ssHMMs). These HMMs are designed to use both the sequence and the predicted secondary structure of a query protein when scoring it against the model. A rigorous benchmark is used, which shows that HMMs made from multiple sequences result in better fold recognition than those based on single sequences. Adding secondary structure information to the HMMs improves the ability of fold recognition further, both when using true and predicted secondary structures for the query sequence.<br>Bioinformatik är ett område där datavetenskapliga och statistiska metoder används för att analysera och strukturera biologiska data. Ett viktigt område inom bioinformatiken försöker förutsäga vilken tredimensionell struktur och funktion ett protein har, utifrån dess aminosyrasekvens och/eller likheter med andra, redan karaktäriserade, proteiner. Det är känt att två proteiner med likande aminosyrasekvenser också har liknande tredimensionella strukturer. Att två proteiner har liknande strukturer behöver dock inte betyda att deras sekvenser är lika, vilket kan göra det svårt att hitta strukturella likheter utifrån ett proteins aminosyrasekvens. Den här avhandlingen beskriver två metoder för att hitta likheter mellan proteiner, den ena med fokus på att bestämma vilken familj av proteindomäner, med känd 3D-struktur, en given sekvens tillhör, medan den andra försöker förutsäga ett proteins veckning, d.v.s. ge en grov bild av proteinets struktur. Båda metoderna använder s.k. dolda Markov modeller (hidden Markov models, HMMer), en statistisk metod som bland annat kan användas för att beskriva proteinfamiljer. Med hjälp en HMM kan man förutsäga om en viss proteinsekvens tillhör den familj modellen representerar. Båda metoderna använder också strukturinformation för att öka modellernas förmåga att känna igen besläktade sekvenser, men på olika sätt. Det mesta av arbetet i avhandlingen handlar om strukturellt förankrade HMMer (structure-anchored HMMs, saHMMer). För att bygga saHMMerna används strukturbaserade sekvensöverlagringar, vilka genereras utifrån hur proteindomänerna kan läggas på varandra i rymden, snarare än utifrån vilka aminosyror som ingår i deras sekvenser. I varje proteinfamilj används bara ett särskilt, representativt urval av domäner. Dessa är valda så att då sekvenserna jämförs parvis, finns det inget par inom familjen med högre sekvensidentitet än ca 20%. Detta urval görs för att få så stor spridning som möjligt på sekvenserna inom familjen. En programvaruserie har utvecklats för att välja ut representanter för varje familj och sedan bygga saHMMer baserade på dessa. Det visar sig att saHMMerna kan hitta rätt familj till en hög andel av de testade sekvenserna, med nästan inga fel. De är också bättre än den ofta använda metoden Pfam på att hitta rätt familj till helt nya proteinsekvenser. saHMMerna finns tillgängliga genom FISH-servern, vilken alla kan använda via Internet för att hitta vilken familj ett intressant protein kan tillhöra. Den andra metoden som presenteras i avhandlingen är sekundärstruktur-HMMer, ssHMMer, vilka är byggda från vanliga multipla sekvensöverlagringar, men också från information om vilka sekundärstrukturer proteinsekvenserna i familjen har. När en proteinsekvens jämförs med ssHMMen används en förutsägelse om sekundärstrukturen, och den beräknade sannolikheten att sekvensen tillhör familjen kommer att baseras både på sekvensen av aminosyror och på sekundärstrukturen. Vid en jämförelse visar det sig att HMMer baserade på flera sekvenser är bättre än sådana baserade på endast en sekvens, när det gäller att hitta rätt veckning för en proteinsekvens. HMMerna blir ännu bättre om man också tar hänsyn till sekundärstrukturen, både då den riktiga sekundärstrukturen används och då man använder en teoretiskt förutsagd.<br>Jeanette Hargbo.
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Pettitt, Christopher Steven. "Refinement of protein structure models with multi-objective genetic algorithms." Thesis, University College London (University of London), 2007. http://discovery.ucl.ac.uk/1446043/.
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Here I investigate the protein structure refinement problem for homology-based protein structure models. The refinement problem has been identified as a major bottleneck in the structure prediction process and inhibits the goal of producing high-resolution experimental quality structures for target protein sequences. This thesis is composed of three investigations into aspects of template-based modelling and refinement. In the primary investigation, empirical evidence is provided to support the hypothesis that using multiple template-based structures to model a target sequence can improve the quality of the prediction over that obtained solely by using the single best prediction. A multi-objective genetic algorithm is used to optimize protein structure models by using the structural information from a set of predictions, guided by various objective functions. The effect of multi-objective optimization on model quality is examined. A benchmark of energy functions and model quality assessment methods is performed in the context of automated homology modelling to assess the ability of these methods at discriminating nearer-native structures from a set of predictions. These model quality assessment methods were unable to significantly improve the ranking of threading- based prediction methods though some model quality assessment methods improved model selection for methods which use sequence information alone. The results suggest that structural informational can provide valuable information for distinguishing better models where only sequence information has been used for modelling. The suitability of these energy functions for high-resolution refinement is discussed. Finally, a stochastic optimization algorithm is developed for refining homology-based protein structure models using evolutionary algorithms. This approach uses multiple structural model inputs, conformational sampling operators, and objective functions for guiding a search through conformational space. Single- and multi-objective genetic variants are applied to homology model predictions for 35 target proteins. The refinement results are discussed and the performance of both algorithmic variants compared and contrasted.
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Hayward, Steven John. "Studies in protein secondary structure prediction with neural network models." Thesis, University of Edinburgh, 1991. http://hdl.handle.net/1842/14034.
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The aim of this work was to predict protein secondary structure using neural network models. Initially a Hopfield network was used but abandoned in favour of a layered network trained using the back propagation algorithm. In the early stages of this work an exploration of the many different approaches to this problem was undertaken. These included attempts to predict boundaries between secondary structures, the secondary structures of individual residues, and the secondary structures of sequences wholly within a particular secondary structure. Results indicated the latter to be the best approach to continue with. In addition two coding schemes were investigated: a coding scheme based on occurrences of pairs of residues and one based on the positions of residues. It was found that this positional coding scheme was the natural coding scheme for this problem. On segments of whole alpha-helix and whole non-alpha-helix 10 residues in length a prediction success of around 80% with a correlation coefficient of 0.52 was achieved with the positional coding scheme. On whole proteins, where predictions are made for individual residues, alpha-helix prediction drops to 73% with a correlation coefficient of 0.34. The relative predictability of alpha-helices of above and below average accessibility was also investigated showing that those of above average accessibility are more predictable than those with below average accessibility. The main body of this work concerns the apparent limit of predictability of alpha-helices. It was found that test set prediction did not depend on the number of hidden nodes. In fact, a single layer network performed as well as those with hidden nodes showing that the probolem is basically linearly separable. In addition, prediction success plateaus well below that of perfect prediction success. During training, test set prediction is seen to peak. The decrease in prediction success was found to be due to non-alpha-helix sequences that the network was unable to distinguish from real alpha-helix sequences. These regions of non-alpha-helix were shown to occur adjacent to actual alpha-helices with statistical significance. It is proposed that potential alpha-helices are disrupted by global constraints during the formation of tertiary structure. The effect of window size was also investigated as was beta-sheet prediction, but this was found to be limited by the small number of examples available with our approach. However, its distribution in the input space in relation to alpha-helix and coil was determined.
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Gregor, Craig Robert. "Epitopes, aggregation and membrane binding : investigating the protein structure-function relationship." Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/5833.
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The three-dimensional structure of a protein, formed as a result of amino-acid sequences folding into compact domains, is regarded as a key factor in its biological function. How and why proteins fold into specific topologies, remain the key focus of scientific research in the field of biophysics. By stripping down complex reactions down to the most basic elements, biophysicists aim to develop simplified models for biological phenomena such as antibody discrimination, viral fusion or self-assembly. Focusing on small model peptide systems, rather than the full proteins from which they were derived, was hoped to result in accurate structural measurements and provide a more transparent comparison between simulation and experiment. The aim of this research was therefore to investigate how accurate these models were when compared against experiment. Furthermore, while breaking down the complex biological phenomena into simple models, there was also a conscious effort to ensure that the models were representative of real biological systems, and a major focus was therefore aimed at determining whether any meaningful biomedical insight may be extrapolated from such models. Peptides found in hormones (human chorionic gonadotropin, luteinizing hormone), viruses (HIV) and amyloid diseases (transthyretin) were selected in order to probe a variety of questions in relation to the aforementioned biological phenomena. Namely, how the primary sequence influenced the three-dimensional structure (and thus its biological function), how its environment could influence such a confirmation, and how these systems aggregated. This doctoral study has made use of a combination of computer simulations and experimental techniques to investigate a selection of biologically relevant peptides; utilising classical atomistic molecular dynamics (MD) simulations to characterise the free-energy landscapes of the chosen peptides, and compare these findings with the secondary structure content predicted by spectroscopic methods such as circular dichroism and infrared spectroscopy. The peptide systems studied within, were found to be characterised by rugged free-energy landscapes unlike their protein counterparts (defined by singular, deep minima). Furthermore, these landscapes were found to be highly plastic and sensitive to changes in the local environment.
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Chippington-Derrick, T. C. "Models, methods and algorithms for constraint dynamics simulations of long chain molecules." Thesis, University of Reading, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.234776.
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Sidiqi, Mahjooba. "The structure and RNA-binding of poly (C) protein 1." University of Western Australia. School of Biomedical, Biomolecular and Chemical Sciences, 2008. http://theses.library.uwa.edu.au/adt-WU2008.0077.
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[Truncated abstract] Regulation of mRNA stability is an important posttranscriptional mechanism involved in the control of gene expression. The rate of mRNA decay can differ greatly from one mRNA to another and may be regulated by RNA-protein interactions. A key determinant of mRNA decay are sequence instability (cis) elements often located in the 3' untranslated region (UTR) of many mRNAs. For example, the AU rich elements (AREs), are such well characterized elements, and most commonly involved in promoting mRNA degradation, and specific binding of proteins to these elements leading to the stabilization of some mRNAs. Other cis-elements have been described for mRNA in which mRNA stability is a critical component of gene regulation. This includes the androgen receptor (AR) UC-rich cis element in its 3'UTR. The AR is a key target for therapeutics in human prostate cancer and thus understanding the mechanism involved in regulating its expression is an important goal. The [alpha]CP1 protein, a KH-domain containing RNA-binding protein has been found to bind this UC-rich region of the AR and is thought to play an important role in regulating AR mRNA expression. [alpha]CP1 protein is a triple KH (hnRNP K homology) domain protein with specificity for Crich tracts of RNA and ssDNA (single stranded DNA). Relatively little is known about the structural interaction of [alpha]CP1 with target RNA cis elements, thus the present study aimed to better understand the nature of interaction between 30 nt 3'UTR UC-rich AR mRNA and [alpha]CP1 protein using various biophysical techniques, in an attempt to determine which [alpha]CP1 domain or combination of domains is involved in RNA-binding. These studies could ultimately provide novel targets for drugs aimed to regulate AR mRNA expression in prostate cancer cells. At the commencement of this study little was known about the structure of the [alpha]CP1- KH domains and their basis for poly (C) binding specificity. ... Additional studies addressed the significance of the four core recognition nucleotides (TCCC) using a series of cytosine to thymine mutants. The findings verified some of the results predicted from structural studies, especially the need for maximum KH binding to a core tetranucleotide recognition sequence. Our mutational studies of the four core bases confirmed the importance of cytosine in positions two and three as no binding was observed, while some binding was observed when the fourth base was mutated. In summary, the work presented in this thesis provides new detailed insight into the molecular interactions between the [alpha]CP1-KH domain and AR mRNA. Furthermore, these studies shed light on the nature of protein/mRNA interactions in general, as well as the specific complex that forms on AR mRNA. These studies have provided new understanding into the mode of [alpha]CP1 binding at a target oligonucleotide binding site and, provide a foundation for future studies to define structure of multiprotein/oligonucleotide complexes involved in AR mRNA gene regulation. Understanding the detailed interaction between the AR mRNA and [alpha]CP1 could provide possible targets for drug development at reducing AR expression in prostate cancer cells by interfering with the interaction of [alpha]CP1 and AR-mRNA.
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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.<br>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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Aydin, Zafer. "Bayesian models and algoritms for protein secondary structure and beta-sheet prediction." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/26471.
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Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2009.<br>Committee Chair: Yucel Altunbasak; Committee Co-Chair: Mark Borodovsky; Committee Member: Brani Vidakovic; Committee Member: Ghassan Alregib; Committee Member: James McClellan; Committee Member: Russel Mersereau. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Pontiggia, Francesco. "Protein structure and functionally-oriented dynamics: from atomistic to Coarse-grained models." Doctoral thesis, SISSA, 2008. http://hdl.handle.net/20.500.11767/3979.
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The overwhelming majority of biological processes relies on the capability of proteins to sustain conformational changes so to selectively recognise, bind and process other molecules, being them proteins, nucleic acids or other chemical compounds. The paradigmatic tripartite characterization of proteins in terms of sequence→structure→function has served to interpret the above-mentioned capability as being encoded in proteins' native structures, which is in turn determined by the amino acidic sequence...
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Tsilo, Lipontseng Cecilia. "Protein secondary structure prediction using neural networks and support vector machines." Thesis, Rhodes University, 2009. http://hdl.handle.net/10962/d1002809.
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Predicting the secondary structure of proteins is important in biochemistry because the 3D structure can be determined from the local folds that are found in secondary structures. Moreover, knowing the tertiary structure of proteins can assist in determining their functions. The objective of this thesis is to compare the performance of Neural Networks (NN) and Support Vector Machines (SVM) in predicting the secondary structure of 62 globular proteins from their primary sequence. For each NN and SVM, we created six binary classifiers to distinguish between the classes’ helices (H) strand (E), and coil (C). For NN we use Resilient Backpropagation training with and without early stopping. We use NN with either no hidden layer or with one hidden layer with 1,2,...,40 hidden neurons. For SVM we use a Gaussian kernel with parameter fixed at = 0.1 and varying cost parameters C in the range [0.1,5]. 10- fold cross-validation is used to obtain overall estimates for the probability of making a correct prediction. Our experiments indicate for NN and SVM that the different binary classifiers have varying accuracies: from 69% correct predictions for coils vs. non-coil up to 80% correct predictions for stand vs. non-strand. It is further demonstrated that NN with no hidden layer or not more than 2 hidden neurons in the hidden layer are sufficient for better predictions. For SVM we show that the estimated accuracies do not depend on the value of the cost parameter. As a major result, we will demonstrate that the accuracy estimates of NN and SVM binary classifiers cannot distinguish. This contradicts a modern belief in bioinformatics that SVM outperforms other predictors.
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Brown, Jennifer Louise. "Investigation of the molecular interactions between an anti-peptide antibody and its ligand." Thesis, University of Southampton, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.318221.
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TUBB, MATTHEW ROBERT. "Apolipoprotein A-IV Structural Models and Functional Implications." University of Cincinnati / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1218826062.
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Kurtz, Katryn Lucille. "Structure of chromatin, protein transitions, and post-translational histone modifications in several sperm models." Doctoral thesis, Universitat de Barcelona, 2008. http://hdl.handle.net/10803/1158.
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The study of chromatin structure in several simple sperm models of increasing complexity was performed. Species demonstrating different types of sperm nuclear protein transitions and structural changes in spermatic chromatin during spermiogenesis were selected as models for comparison: "H" (non-histone proteins are removed), "H->P" (protamine displaces histones), and "H->Pp->P" (precursor protamine displaces histones, and subsequently is converted into the mature protamine). This study has an evolutionary focus, in which a primitive sperm model is identified, from which more complex models may have risen during evolution. The final sperm characteristics achieved are considered to be caused by the changes the immature sperm cell undergoes during the process of spermiogenesis, and are correlated to an adaptation to the fertilization biology of each species. A broader understanding of the variety of sperm shapes, their chemical variability, and the spermatic chromatin condensation patterns pertaining to species of these simple spermiogenic models has been achieved. In this study, the diversity in sperm characteristics is extrapolated to the function the sperm cell has to pass on its genetic material to achieve fertilization of the egg of its own species.<br/><br/>For three different models using four marine species, protein transitions, chromatin condensation, and acetylation patterns were described during spermiogeneis. Specifically, changes in chromatin architecture and its protein complement was extensively studied using mainly transmission electron microscopy, inmunocytochemistry using anti-histone, anti-precursor protamine, and anti-acetyl group antibodies, as well as high resolution polyacrylamide gel electrophoresis (PAGE) and western blotting. <br/><br/>A model of specialized sperm chromatin (crustacean type) has been included in this study, since for decades this type of chromatin has remained poorly understood. Crustacean type sperm, once believed to have nuclei void of basic DNA-associated proteins, was found to contain histones, and is considered a derivation of the "H" model. Three species of brachyuran crabs from two different families were used to compositionally and ultrastructurally study this unusually decondensed mature sperm chromatin. Characterization of the histones from these sperm using HPLC and amino acid analysis confirm that the basic proteins extracted from sperm of these crabs are indeed typical and canonical histones, though some appear modified by post-translational modifications such as acetylation, which has never before been described in mature sperm. Additionally, in Maja brachydactyla, histones H3 and H2B appear in stoichiometric amounts different to what would be found in somatic chromatin. By performing micrococcal nuclease digestions, the presence of nucleosomes (or nucleosome-like particles) in the sperm of these species was confirmed, and demonstrated that histones are found interacting with the sperm DNA. Further, the histone/DNA ratio was evaluated in two Cancer species, and it was determined that these sperm only contain slightly over half the amount of basic protein per DNA unit compared to other sperm types. These results concerning the composition of the crustacean-type sperm chromatin help to explain its decondensed nature.
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Gough, J. "Hidden Markov models and their application to genome analysis in the context of protein structure." Thesis, University of Cambridge, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.599547.
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The bulk of the thesis is concerned with the application of hidden Markov models (HMMs) to remote protein homology detection. The thesis both addresses how best to utilise HMMs, and then uses them to analyse all completely sequenced genomes. There is a structural perspective to the work, and a section on three-dimensional protein structure analysis is included. The Structural Classification of Proteins (SCOP) database forms the basis of the structural perspective. SCOP is a hierarchical database of protein domains classified by their structure, sequence and function. The main aim of the work is to use HMMs to classify all protein sequences produced by the genome projects (including human) into their constituent structural domains. To do this HMMs were built to represent all proteins of known structure; the thesis examines ways in which to best do this and describes the construction of a library of models. Structural domain assignments to the genome sequences were generated by scoring the model library against the genomes, and then selecting the most probable domain architecture for each sequence. The genome assignments (within the framework of the evolutionary-based SCOP classification) provide the capability to study evolution at a molecular level, as well as at the level of the whole organism. The model library and genome assignments has been made public via the SUPERFAMILY database. The data and services provided have been used for a growing number of projects several of which have already led to publication.
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Murray, Ian V. J. "Acylation stimulating protein (ASP) structure & function studies : in vitro and in vivo in mouse models." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp02/NQ55363.pdf.
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Kim, Seoung Bum. "Data Mining in Tree-Based Models and Large-Scale Contingency Tables." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/6825.
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This thesis is composed of two parts. The first part pertains to tree-based models. The second part deals with multiple testing in large-scale contingency tables. Tree-based models have gained enormous popularity in statistical modeling and data mining. We propose a novel tree-pruning algorithm called frontier-based tree-pruning algorithm (FBP). The new method has an order of computational complexity comparable to cost-complexity pruning (CCP). Regarding tree pruning, it provides a full spectrum of information. Numerical study on real data sets reveals a surprise: in the complexity-penalization approach, most of the tree sizes are inadmissible. FBP facilitates a more faithful implementation of cross validation, which is favored by simulations.
One of the most common test procedures using two-way contingency tables is the test of independence between two categorizations. Current test procedures such as chi-square or likelihood ratio tests provide overall independency but bring limited information about the nature of the association in contingency tables. We propose an approach of testing independence of categories in individual cells of contingency tables based on a multiple testing framework. We then employ the proposed method to identify the patterns of pair-wise associations between amino acids involved in beta-sheet bridges of proteins. We identify a number of amino acid pairs that exhibit either strong or weak association. These patterns provide useful information for algorithms that predict secondary and tertiary structures of proteins.
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Planas, 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.
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Proteins are indispensable players in virtually all biological events. The functions of proteins are determined by their three dimensional (3D) structure and coordinated through intricate networks of protein-protein interactions (PPIs). Hence, a deep comprehension of such networks turns out to be crucial for understanding the cellular biology. Computational approaches have become critical tools for analysing PPI networks. In silico methods take advantage of the existing PPI knowledge to both predict new interactions and predict the function of proteins. Regarding the task of predicting PPIs, several methods have been already developed. However, recent findings demonstrate that such methods could take advantage of the knowledge on non-interacting protein pairs (NIPs). On the task of predicting the function of proteins,the Guilt-by-Association (GBA) principle can be exploited to extend the functional annotation of proteins over PPI networks. In this thesis, a new algorithm for PPI prediction and a protocol to complete cell signalling networks are presented. iLoops is a method that uses NIP data and structural information of proteins to predict the binding fate of protein pairs. A novel protocol for completing signalling networks –a task related to predicting the function of a protein, has also been developed. The protocol is based on the application of GBA principle in PPI networks.<br>Les 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.
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Mishra, Avdesh. "Effective Statistical Energy Function Based Protein Un/Structure Prediction." ScholarWorks@UNO, 2019. https://scholarworks.uno.edu/td/2674.
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Proteins are an important component of living organisms, composed of one or more polypeptide chains, each containing hundreds or even thousands of amino acids of 20 standard types. The structure of a protein from the sequence determines crucial functions of proteins such as initiating metabolic reactions, DNA replication, cell signaling, and transporting molecules. In the past, proteins were considered to always have a well-defined stable shape (structured proteins), however, it has recently been shown that there exist intrinsically disordered proteins (IDPs), which lack a fixed or ordered 3D structure, have dynamic characteristics and therefore, exist in multiple states. Based on this, we extend the mapping of protein sequence not only to a fixed stable structure but also to an ensemble of protein conformations, which help us explain the complex interaction within a cell that was otherwise obscured. The objective of this dissertation is to develop effective ab initio methods and tools for protein un/structure prediction by developing effective statistical energy function, conformational search method, and disulfide connectivity patterns predictor.
The key outcomes of this dissertation research are: i) a sequence and structure-based energy function for structured proteins that includes energetic terms extracted from hydrophobic-hydrophilic properties, accessible surface area, torsion angles, and ubiquitously computed dihedral angles uPhi and uPsi, ii) an ab initio protein structure predictor that combines optimal energy function derived from sequence and structure-based properties of proteins and an effective conformational search method which includes angular rotation and segment translation strategies, iii) an SVM with RBF kernel-based framework to predict disulfide connectivity pattern, iv) a hydrophobic-hydrophilic property based energy function for unstructured proteins, and v) an ab initio conformational ensemble generator that combines energy function and conformational search method for unstructured proteins which can help understand the biological systems involving IDPs and assist in rational drugs design to cure critical diseases such as cancer or cardiovascular diseases caused by challenging states of IDPs.
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Glidden, Michael D. II. "Single-chain insulin analogs as ultra-stable therapeutics and as models of protein (mis)folding: stability, structure, dynamics, and function of novel analogs." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1522270994798884.
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Hudson, Cody Landon. "Protein structure analysis and prediction utilizing the Fuzzy Greedy K-means Decision Forest model and Hierarchically-Clustered Hidden Markov Models method." Thesis, University of Central Arkansas, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=1549796.
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<p>Structural genomics is a field of study that strives to derive and analyze the structural characteristics of proteins through means of experimentation and prediction using software and other automatic processes. Alongside implications for more effective drug design, the main motivation for structural genomics concerns the elucidation of each protein’s function, given that the structure of a protein almost completely governs its function. Historically, the approach to derive the structure of a protein has been through exceedingly expensive, complex, and time consuming methods such as x-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy. </p><p> In response to the inadequacies of these methods, three families of approaches developed in a relatively new branch of computer science known as bioinformatics. The aforementioned families include threading, homology-modeling, and the de novo approach. However, even these methods fail either due to impracticalities, the inability to produce novel folds, rampant complexity, inherent limitations, etc. In their stead, this work proposes the Fuzzy Greedy K-means Decision Forest model, which utilizes sequence motifs that transcend protein family boundaries to predict local tertiary structure, such that the method is cheap, effective, and can produce semi-novel folds due to its local (rather than global) prediction mechanism. This work further extends the FGK-DF model with a new algorithm, the Hierarchically Clustered-Hidden Markov Models (HC-HMM) method to extract protein primary sequence motifs in a more accurate and adequate manner than currently exhibited by the FGK-DF model, allowing for more accurate and powerful local tertiary structure predictions. Both algorithms are critically examined, their methodology thoroughly explained and tested against a consistent data set, the results thereof discussed at length. </p>
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Robertson, Timothy Allen. "Development and validation of statistical potential functions for the prediction of protein/nucleic-acid interactions from structure /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/9268.
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Cheng, Haitao. "Protein structure prediction and conformational transitions I. Improvement of protein secondary structure prediction : II. Pathways of conformational transition originating in phosphorylation : a study of CDK2 using targeted molecular dynamics and coarse grained models /." [Ames, Iowa : Iowa State University], 2009. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3360333.
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Hägglöf, Peter. "Plasminogen activator inhibitor type-1 : structure-function studies and its use as a reference for intramolecular distance measurements /." Umeå : Umeå University, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-177.
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Brewer, Allan Ronald. "Computational protein structure prediction using physico-chemical force fields : assessing reduced-atom and torsional models for accelerating the phase-space search process." Thesis, University of Bristol, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.427889.
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Tetley, Romain. "Analyse mixte de protéines basée sur la séquence et la structure - applications à l'annotation fonctionnelle." Thesis, Université Côte d'Azur (ComUE), 2018. http://www.theses.fr/2018AZUR4111/document.
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Dans cette thèse, l'emphase est mise sur la réconciliation de l'analyse de structure et de séquence pour les protéines. L'analyse de séquence brille lorsqu'il s'agit de comparer des protéines présentant une forte identité de séquence (≤ 30\%) mais laisse à désirer pour identifier des homologues lointains. L'analyse de structure est une alternative intéressante. Cependant, les méthodes de résolution de structures sont coûteuses et complexes - lorsque toutefois elles produisent des résultats. Ces observations rendent évident la nécessité de développer des méthodes hybrides, exploitant l'information extraite des structures disponibles pour l'injecter dans des modèles de séquence. Cette thèse produit quatre contributions principales dans ce domaine. Premièrement, nous présentons une nouvelle distance structurale, le RMSDcomb, basée sur des patterns de conservation structurale locale, les motifs structuraux. Deuxièmement, nous avons développé une méthode pour identifier des motifs structuraux entre deux structures exploitant un bootstrap dépendant de filtrations. Notre approche n'est pas un compétiteur direct des aligneurs flexibles mais permet plutôt de produire des analyses multi-échelles de similarités structurales. Troisièmement, nous exploitons les méthodes suscitées pour construire des modèles de Markov cachés hybrides biaisés vers des régions mieux conservées structurellement. Nous utilisons un tel modèle pour caractériser les protéines de fusion virales de classe II, une tâche particulièrement ardue du fait de leur faible identité de séquence et leur conservation structurale moyenne. Ce faisant, nous parvenons à trouver un certain nombre d'homologues distants connues des protéines virales, notamment chez la Drosophile. Enfin, en formalisant un sous-problème rencontré lors de la comparaison de filtrations, nous présentons un nouveau problème théorique - le D-family matching - sur lequel nous démontrons des résultats algorithmiques variés. Nous montrons - d'une façon analogue à la comparaison de régions de deux conformations d'une protéine - comment exploiter ce modèle théorique pour comparer deux clusterings d'un même jeu de données<br>In this thesis, the focus is set on reconciling the realms of structure and sequence for protein analysis. Sequence analysis tools shine when faced with proteins presenting high sequence identity (≤ 30\%), but are lack - luster when it comes to remote homolog detection. Structural analysis tools present an interesting alternative, but solving structures - when at all possible- is a tedious and expensive process. These observations make the need for hybrid methods - which inject information obtained from available structures in a sequence model - quite clear. This thesis makes four main contributions toward this goal. First we present a novel structural measure, the RMSDcomb, based on local structural conservation patterns - the so called structural motifs. Second, we developed a method to identify structural motifs between two structures using a bootstrap method which relies on filtrations. Our approach is not a direct competitor to flexible aligners but can provide useful to perform a multiscale analysis of structural similarities. Third, we build upon the previous methods to design hybrid Hidden Markov Models which are biased towards regions of increased structural conservation between sets of proteins. We test this tool on the class II fusion viral proteins - particularly challenging because of their low sequence identity and mild structural homology. We find that we are able to recover known remote homologs of the viral proteins in the Drosophila and other organisms. Finally, formalizing a sub - problem encountered when comparing filtrations, we present a new theoretical problem - the D-family matching - on which we present various algorithmic results. We show - in a manner that is analogous to comparing parts of two protein conformations - how it is possible to compare two clusterings of the same data set using such a theoretical model
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Dressel, Frank. "Sequenz, Energie, Struktur - Untersuchungen zur Beziehung zwischen Primär- und Tertiärstruktur in globulären und Membran-Proteinen." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2008. http://nbn-resolving.de/urn:nbn:de:bsz:14-ds-1222781322751-68621.
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Proteine spielen auf der zellulären Ebene eines Organismus eine fundamentale Rolle. Sie sind quasi die „Maschinen“ der Zelle. Ihre Bedeutung wird nicht zuletzt in ihrem Namen deutlich, welcher 1838 erstmals von J. Berzelius verwendet wurde und „das Erste“, „das Wichtigste“ bedeutet. Proteine sind aus Aminosäuren aufgebaute Moleküle. Unter physiologischen Bedingungen besitzen sie eine definierte dreidimensionale Gestalt, welche für ihre biologische Funktion bestimmend ist. Es wird heutzutage davon ausgegangen, dass diese dreidimensionale, stabile Struktur von Proteinen eindeutig durch die Abfolge der einzelnen Aminosäuren, der Sequenz, bestimmt ist. Diese Abfolge ist für jedes Protein in der Desoxyribonukleinsäure (DNS) gespeichert. Es ist allerdings eines der größten ungelösten Probleme der letzten Jahrzehnte, wie die Beziehung zwischen Sequenz und 3D-Struktur tatsächlich aussieht. Die Beantwortung dieser Fragestellung erfordert interdisziplinäre Ansätze aus Biologie, Informatik und Physik. In dieser Arbeit werden mit Hilfe von Methoden der theoretischen (Bio-) Physik einige der damit verbundenen Aspekte untersucht. Das Hauptaugenmerk liegt dabei auf Wechselwirkungen der einzelnen Aminosäuren eines Proteins untereinander, wofür in dieser Arbeit ein entsprechendes Energiemodell entwickelt wurde. Es werden Grundzustände sowie Energielandschaften untersucht und mit experimentellen Daten verglichen. Die Stärke der Wechselwirkung einzelner Aminosäuren erlaubt zusätzlich Aussagen über die Stabilität von Proteinen bezüglich mechanischer Kräfte. Die vorliegende Arbeit unterteilt sich wie folgt: Kapitel 2 dient der Einleitung und stellt Proteine und ihre Funktionen dar. Kapitel 3 stellt die Modellierung der Proteinstrukturen in zwei verschiedenen Modellen vor, welche in dieser Arbeit entwickelt wurden, um 3D-Strukturen von Proteinen zu beschreiben. Anschließend wird in Kapitel 4 ein Algorithmus zum Auffinden des exakten Energieminimums dargestellt. Kapitel 5 beschäftigt sich mit der Frage, wie eine geeignete diskrete Energiefunktion aus experimentellen Daten gewonnen werden kann. In Kapitel 6 werden erste Ergebnisse dieses Modells dargestellt. Der Frage, ob der experimentell bestimmte Zustand dem energetischen Grundzustand eines Proteins entspricht, wird in Kapitel 7 nachgegangen. Die beiden Kapitel 8 und 9 zeigen die Anwendung des Modells an zwei Proteinen, dem Tryptophan cage protein als dem kleinsten, stabilen Protein und Kinesin, einem Motorprotein, für welches 2007 aufschlussreiche Experimente zur mechanischen Stabilität durchgeführt wurden. Kapitel 10 bis 12 widmen sich Membranproteinen. Dabei beschäftigt sich Kapitel 10 mit der Vorhersage von stabilen Bereichen (sog. Entfaltungsbarrieren) unter externer Krafteinwirkung. Zu Beginn wird eine kurze Einleitung zu Membranproteinen gegeben. Im folgenden Kapitel 11 wird die Entfaltung mit Hilfe des Modells und Monte-Carlo-Techniken simuliert. Mit dem an Membranproteine angepassten Wechselwirkungsmodell ist es möglich, den Einfluss von Mutationen auch ohne explizite strukturelle Informationen vorherzusagen. Dieses Thema wird in Kapitel 12 diskutiert. Die Beziehung zwischen Primär- und Tertiärstruktur eines Proteins wird in Kapitel 13 behandelt. Es wird ein Ansatz skizziert, welcher in der Lage ist, Strukturbeziehungen zwischen Proteinen zu detektieren, die mit herkömmlichen Methoden der Bioinformatik nicht gefunden werden können. Die letzten beiden Kapitel schließlich geben eine Zusammenfassung bzw. einen Ausblick auf künftige Entwicklungen und Anwendungen des Modells.
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Frouws, Timothy Duncan. "Iterative helical real-space reconstruction of histone octamer tubular crystals and implications for the 30 nm chromatin fiber." Thesis, University of the Western Cape, 2006. http://etd.uwc.ac.za/index.php?module=etd&.
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Lubbe, Lizel. "Cloning and Expression of the M-Gene from the Human Coronavirus NL-63 in Different Expression Systems." Thesis, University of the Western Cape, 2008. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_2721_1266364969.
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<p>In this study, the HCoV-NL63 genome was transcribed from RNA to DNA from which the M gene was amplified with various primers designed for use in specific expression systems. The various genes were cloned into the pGEM vector and confirmed by sequencing. The genes were now expressed in cloning vectors suited for each expression system (pFastBac for baculovirus expression, pFlexi for bacterial expression and pCMV for mammalian expression). Clones were sequenced for a second time. The recombinant clone in pFlexi was expressed in KRX cells and a 36hr time course was performed. The recombinant pFastBac clone was used to infect Sf9 insect cells and P1 and P2 viral stocks were obtained. The recombinant pCMV clone was used to transfect Cos1 mammalian cells.</p>
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Nordling, Erik. "Biocomputational studies on protein structures /." Stockholm, 2002.
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Chen, Yiwen Superfine Richard. "Probing protein structural dynamics using simplified models." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2007. http://dc.lib.unc.edu/u?/etd,1093.
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Thesis (Ph. D.)--University of North Carolina at Chapel Hill, 2007.<br>Title from electronic title page (viewed Mar. 27, 2008). "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Physics and Astronomy." Discipline: Physics and Astronomy; Department/School: Physics and Astronomy.
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Uziela, Karolis. "Protein Model Quality Assessment : A Machine Learning Approach." Doctoral thesis, Stockholms universitet, Institutionen för biokemi och biofysik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-137695.
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Many protein structure prediction programs exist and they can efficiently generate a number of protein models of a varying quality. One of the problems is that it is difficult to know which model is the best one for a given target sequence. Selecting the best model is one of the major tasks of Model Quality Assessment Programs (MQAPs). These programs are able to predict model accuracy before the native structure is determined. The accuracy estimation can be divided into two parts: global (the whole model accuracy) and local (the accuracy of each residue). ProQ2 is one of the most successful MQAPs for prediction of both local and global model accuracy and is based on a Machine Learning approach. In this thesis, I present my own contribution to Model Quality Assessment (MQA) and the newest developments of ProQ program series. Firstly, I describe a new ProQ2 implementation in the protein modelling software package Rosetta. This new implementation allows use of ProQ2 as a scoring function for conformational sampling inside Rosetta, which was not possible before. Moreover, I present two new methods, ProQ3 and ProQ3D that both outperform their predecessor. ProQ3 introduces new training features that are calculated from Rosetta energy functions and ProQ3D introduces a new machine learning approach based on deep learning. ProQ3 program participated in the 12th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP12) and was one of the best methods in the MQA category. Finally, an important issue in model quality assessment is how to select a target function that the predictor is trying to learn. In the fourth manuscript, I show that MQA results can be improved by selecting a contact-based target function instead of more conventional superposition based functions.<br><p>At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 3: Manuscript.</p>
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Silva, Aparecido Rodrigues da. "Desenvolvimento e avaliação de modelos representativos para construção de aminoácidos e de estruturas de proteínas." Universidade de São Paulo, 2010. http://www.teses.usp.br/teses/disponiveis/76/76132/tde-16022011-091415/.
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Foi desenvolvido um conjunto de peças plásticas que permitem a montagem e representação dos aminoácidos mais comuns, bem como a construção de estruturas protéicas. Durante e após o desenvolvimento o material foi submetido a várias etapas de avaliação por professores (do ensino básico e universitário), alunos de pós-graduação e de graduação. A primeira etapa foi o desenvolvimento dos modelos em ambiente computacional, seguida da prototipagem das peças. Após discussão com a comunidade científica (apresentados na XXXVI Reunião Anual da SBBq em 2007) as sugestões foram implementadas nos modelos computacionais. Quatro moldes para injeção termoplástica foram projetados, detalhados e construídos, sob nossa orientação. As peças representando as estruturas que compõe os aminoácidos e ligações foram produzidas em grande escala e iniciou-se o processo final de avaliação. As peças apresentaram boas relações geométricas com as fórmulas estruturais dos aminoácidos obtidas de bancos de dados e livros didáticos. As conexões Cα-amina e Cα-carboxila permitem verificar a liberdade de rotação característica das cadeias polipeptídicas e as possibilidades dos ângulos de torção Ψ e Φ, visualizando a restrição de rotação da ligação peptídica. Montando um conjunto de aminoácidos é possível construir uma cadeia polipeptídica e, através das ligações de hidrogênio, montar as estruturas secundárias principais (hélice-α e estruturas β). Duas avaliações preliminares foram realizadas e a avaliação final ocorreu em uma oficina de atividades com 256 professores das áreas de ciências da natureza da rede publica do Estado de SP. Os resultados da avaliação foram extremamente positivos, sendo importante destacar a quantidade e o teor dos comentários elogiosos ao potencial de utilização do material, notadamente, dos professores de biologia e química. O material poderá inclusive auxiliar no preenchimento de lacunas conceituais que existem na formação dos professores e que foram observadas durante as atividades de avaliação. Este conjunto de peças, organizado na forma de um kit: Construindo Estruturas de Aminoácidos e Proteínas, foi submetido à avaliação do MEC e certificado por este órgão, passando a integrar o Guia de Tecnologias Educacionais 2008.<b/><br>It was developed a set of plastic pieces that allow the assembly and representation of the most common amino acids, as well as the construction of protein structures. During and after development the material was submitted to several stages of evaluation by teachers (primary and university), graduate and undergraduate students. The first step was the development of models in the computing environment, followed by prototyping of parts. After discussion with the scientific community (presented at the XXXVI Annual Meeting of SBBq in 2007) suggestions were implemented in the computational models. Four thermoplastic injection molds were designed, detailed and constructed under our supervision. Parts representing the structures of amino acids and bonds were produced in large scale and it was started the final process of evaluation. The pieces had good geometric relationships with the structural formulas of amino acids obtained from databases and textbooks. The connections Cα-amine and Cα-arboxyl permit to check the freedom of rotation of the polypeptide chains and the possibility of torsion angles Φ and Ψ, visualizing the restriction of rotation of the peptide bond. Assembling a set of amino acids is possible to build a polypeptide chain and, through hydrogen bonding, to assemble the main secondary structures (α-helix and β-structures). Two preliminary evaluations were conducted and the final evaluation took place in a workshop with 256 teachers of the fields of natural sciences from public schools of the São Paulo State. The results of the evaluation were extremely positive and it is important to highlight the amount and content of approving comments for the potential of use of the material, especially from biology and chemistry teachers. The material may even assist in filling in conceptual gaps that exist in teacher instruction and that were observed during the evaluation activities. This set of pieces, arranged in the form of a kit: Building Structures of Amino Acids and Proteins, was submitted to MEC and certified by this organization, starting to integrate the Guide of Educational Technology 2008<b/>.
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Wallner, Björn. "Protein Structure Prediction : Model Building and Quality Assessment." Doctoral thesis, Stockholm University, Department of Biochemistry and Biophysics, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-649.
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<p>Proteins play a crucial roll in all biological processes. The wide range of protein functions is made possible through the many different conformations that the protein chain can adopt. The structure of a protein is extremely important for its function, but to determine the structure of protein experimentally is both difficult and time consuming. In fact with the current methods it is not possible to study all the billions of proteins in the world by experiments. Hence, for the vast majority of proteins the only way to get structural information is through the use of a method that predicts the structure of a protein based on the amino acid sequence.</p><p>This thesis focuses on improving the current protein structure prediction methods by combining different prediction approaches together with machine-learning techniques. This work has resulted in some of the best automatic servers in world – Pcons and Pmodeller. As a part of the improvement of our automatic servers, I have also developed one of the best methods for predicting the quality of a protein model – ProQ. In addition, I have also developed methods to predict the local quality of a protein, based on the structure – ProQres and based on evolutionary information – ProQprof. Finally, I have also performed the first large-scale benchmark of publicly available homology modeling programs.</p>
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Wallner, Björn. "Protein structure prediction : model building and quality assessment /." Stockholm : Stockholm Bioinformatics Center, Department of Biochemistry and Biophysics, Stockholm University, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-649.
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SUBRAMANIAN, SUCHITHA. "PROTEIN STRUCTURE ALIGNMENT USING A GENERALIZED ALIGNMENT MODEL." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1191966691.
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Trovato, Antonio. "A Geometric perspective on protein structures and heteropolymer models." Doctoral thesis, SISSA, 2000. http://hdl.handle.net/20.500.11767/3962.
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Polozov, Ivan V. "Interactions of class A and class L amphipathic helical peptides with model membranes." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape16/PQDD_0006/NQ30110.pdf.
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Davies, Lisa Joy. "Structural models of protein evolution in database searching." Thesis, University of Cambridge, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.620508.
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Blackburne, Benjamin P. "Functional model proteins : structure, function and evolution." Thesis, University of Nottingham, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.405144.
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Le, Treut Guillaume. "Models of chromosome architecture and connection with the regulation of genetic expression." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLS411/document.
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Plusieurs indices suggèrent que le repliement du chromosome et la régulation de l’expression génétique sont étroitement liés. Par exemple, la co-expression d’un grand nombre de gènes est favorisée par leur rapprochement dans l’espace cellulaire. En outre, le repliement du chromosome permet de faire émerger des structures fonctionnelles. Celles-ci peuvent être des amas condensés et fibrillaires, interdisant l’accès à l’ADN, ou au contraire des configurations plus ouvertes de l’ADN avec quelques amas globulaires, comme c’est le cas avec les usines de transcription. Bien que dissemblables au premier abord, de telles structures sont rendues possibles par l’existence de protéines bivalentes, capable d’apparier des régions parfois très éloignées sur la séquence d’ADN. Le système physique ainsi constitué du chromosome et de protéines bivalentes peut être très complexe. C’est pourquoi les mécanismes régissant le repliement du chromosome sont restés majoritairement incompris.Nous avons étudié des modèles d’architecture du chromosome en utilisant le formalisme de la physique statistique. Notre point de départ est la représentation du chromosome sous la forme d’un polymère rigide, pouvant interagir avec une solution de protéines liantes. Les structures résultant de ces interactions ont été caractérisées à l’équilibre thermodynamique. De plus, nous avons utilisé des simulations de dynamique Brownienne en complément des méthodes théoriques, car elles permettent de prendre en considération une plus grande complexité dans les phénomènes biologiques étudiés.Les principaux aboutissements de cette thèse ont été : (i) de fournir un modèle pour l’existence des usines de transcriptions caractérisées in vivo à l’aide de microscopie par fluorescence ; (ii) de proposer une explication physique pour une conjecture portant sur un mécanisme de régulation de la transcription impliquant la formation de boucles d’ADN en tête d’épingle sous l’effet de la protéine H-NS, qui a été émise suite à l’observation de ces boucles au microscope à force atomique ; (iii) de proposer un modèle du chromosome qui reproduise les contacts mesurés à l’aide des techniques Hi-C. Les conséquences de ces mécanismes sur la régulation de la transcription ont été systématiquement discutées<br>Increasing evidences suggest that chromosome folding and genetic expression are intimately connected. For example, the co-expression of a large number of genes can benefit from their spatial co-localization in the cellular space. Furthermore, functional structures can result from the particular folding of the chromosome. These can be rather compact bundle-like aggregates that prevent the access to DNA, or in contrast, open coil configurations with several (presumably) globular clusters like transcription factories. Such phenomena have in common to result from the binding of divalent proteins that can bridge regions sometimes far away on the DNA sequence. The physical system consisting of the chromosome interacting with divalent proteins can be very complex. As such, most of the mechanisms responsible for chromosome folding and for the formation of functional structures have remained elusive.Using methods from statistical physics, we investigated models of chromosome architecture. A common denominator of our approach has been to represent the chromosome as a polymer with bending rigidity and consider its interaction with a solution of DNA-binding proteins. Structures entailed by the binding of such proteins were then characterized at the thermodynamical equilibrium. Furthermore, we complemented theoretical results with Brownian dynamics simulations, allowing to reproduce more of the biological complexity.The main contributions of this thesis have been: (i) to provide a model for the existence of transcrip- tion factories characterized in vivo with fluorescence microscopy; (ii) to propose a physical basis for a conjectured regulatory mechanism of the transcription involving the formation of DNA hairpin loops by the H-NS protein as characterized with atomic-force microscopy experiments; (iii) to propose a physical model of the chromosome that reproduces contacts measured in chromosome conformation capture (CCC) experiments. Consequences on the regulation of transcription are discussed in each of these studies
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Lutya, Portia Thandokazi. "Expression and purification of the novel protein domain DWNN." Thesis, University of the Western Cape, 2002. http://etd.uwc.ac.za/index.php?module=etd&.
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Proteins play an important role in cells, as the morphology, function and activities of the cell depend on the proteins they express. The key to understanding how different proteins function lies in an understanding of the molecular structure. The overall aim of this thesis was the determination of the structure of DWNN domains. This thesis described the preparation of samples of human DWNN suitable for structural analysis by nuclear magnetic resonance spectroscopy (NMR), as well as NMR analysis.
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Fraga, Keith Jeffrey. "Explorations into protein structure with the knob-socket model." Scholarly Commons, 2016. https://scholarlycommons.pacific.edu/uop_etds/264.
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Protein sequences contain the information in order for a protein to fold to a unique compact, three-dimensional native structure. The forces that drive protein structures to form compact folds are largely dominated by burial of hydrophobic amino acids, which results in non-specific packing of amino acid side-chains. The knob-socket model attempts to organize side-chain packing into tetrahedral packing motifs. This tetrahedral motif is characterized with a three residues on the same secondary structure forming the base of the tetrahedron packing with a side-chain from a separate secondary structure. The base of the motif is termed the socket, and the other side-chain is called the knob. Here, we focus on extending the knob-socket model to understand tertiary and quaternary structure. First, single knobs sometimes pack into more than one socket in real structures. We focus on understanding the topology and amino acid preferences of these tertiary packing surfaces. The main results from the study of tertiary packing surfaces is that they have a preferred handedness, some interactions are ancillary to the packing interaction, there are specific amino preferences for specific positions in packing surfaces, and there is no relationship between side-chain rotamer of the knob packing into the tertiary packing surface. Next, we examine the application of the knob-socket to irregular and mixed packing in protein structure. The main conclusions from these efforts show canonical packing modes between secondary structures and highlight the important of coil secondary structure in providing many of the knobs for packing. Third, we investigate protein quaternary structure with a clique analysis of side-chain interactions. We identify a possible pseudo knob-socket interaction, and compare knob-socket interactions between tertiary and quaternary structure. Lastly, we discuss the workflow used in CASP12 to predict side-chain contacts and atomic coordinates of proteins.
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Tanley, Simon. "Structural chemistry and structural biology of anti-cancer agents binding to proteins with reference to a model protein and to heparanase." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/structural-chemistry-and-structural-biology-of-anticancer-agents-binding-to-proteins-with-reference-to-a-model-protein-and-to-heparanase(b14a04ea-d7db-4846-a717-84d013478698).html.
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The binding of cisplatin and carboplatin to hen egg white lysozyme, a model protein, has been studied using X-ray crystallography under many different crystallisation conditions (Tanley et al, 2012a; 2012b; 2013a; 2013b and Helliwell & Tanley, 2013). From this work, many new results have been obtained; (1) Two molecules of cisplatin and carboplatin are bound to HEWL in DMSO media using the co-crystallisation method. (2) Two molecules of cisplatin are bound to HEWL in aqueous media after a prolonged chemical exposure of 13months. (3) Cisplatin is stable up to 1.78MGy of X-ray radiation when bound to a protein. (4) We are the first academic group to make our raw diffraction data images freely available. (5) Carboplatin partially converts to cisplatin due to the high Cl concentration used in the co-crystallisation conditions. (6) We have also seen binding of just one molecule of carboplatin to the His-15 residue as a function of chemistry, pH and elapsed time. (7) Using NaBr crystallisation conditions, we see partial conversion of carboplatin to the trans Br form with a portion of the CBDC moiety still present. (8) Using triclinic HEWL co-crystallisation with cisplatin studied at 3 different data collection temperatures showed a more versatile binding and an overall larger summed occupancy at the Nε binding site. The human protein, heparanase is over-expressed in many different cancers and its experimental three-dimensional structure is yet to be elucidated. The work presented here includes; (1) Production of a homology model of heparanase and completion of virtual screening in order to identify potential novel small molecule inhibitors. (2) Use of this homology model as a molecular replacement search model (crystals of heparanase and their diffraction data was obtained before my PhD). Due to disorder in the crystal and resulting diffraction pattern as well as heavy metal soaks failing, the crystal structure has been difficult to obtain with this particular crystal system and molecular replacement procedure. (3) Over-expression of heparanase in insect cells and purification has been undertaken in order to produce new crystals.
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Davies, L. "Sequence database searching using structural models of protein evolution." Thesis, University of Cambridge, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.598371.
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Commonly used programs to search sequence databases such as BLAST, FASTA and SSEARCH identify sequence homology through pairwise alignment techniques. These programs are good at detecting closely related sequences but have problems accurately detecting homologous sequences with low sequence identity. This thesis describes a new approach that attempts to improve the detection of distantly related sequences by rejecting the assumption that all sites in a protein behave in an identical manner. This is done without the use of profile techniques, which require the preliminary collection of a set of homologs. Existing programs use general properties of proteins to generate alignment scores, which simplify calculations but may also result in a decrease in accuracy. In reality, amino acid replacement probabilities and rates, amino acid frequencies and gap probabilities all vary according to where a residue lies in a protein structure. Typical patterns of these structure-specific variations in evolutionary dynamics can be incorporated into a database search program through the use of hidden Markov models (HMMs), and hence potentially improve the detection of more distantly related sequences. In this thesis, the utility of including structure-specific evolutionary information in a database search program has been assessed. I have developed a general methodology permitting structure-based evolutionary models to be used for database searching, and specific algorithms that incorporate either solvent accessibility distinctions or protein secondary structure distinctions for globular proteins. In addition I have developed a database search algorithm for transmembrane proteins. The improvement afforded by adding the extra information has then been evaluated through the use of both simulated sequences, which exactly fit the models, and real sequences from the SCOP database. The success rate of each of these programs has been compared to a simplified model that contains the general properties of proteins but with no structural distinctions.
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Carlsson, Jonas. "Mutational effects on protein structure and function." Doctoral thesis, Linköpings universitet, Bioinformatik, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-50491.
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In this thesis several important proteins are investigated from a structural perspective. Some of the proteins are disease related while other have important but not completely characterised functions. The techniques used are general as demonstrated by applications on metabolic proteins (CYP21, CYP11B1, IAPP, ADH3), regulatory proteins (p53, GDNF) and a transporter protein (ANTR1). When the protein CYP21 (steroid 21-hydroxylase) is deficient it causes CAH (congenital adrenal hyperplasia). For this protein, there are about 60 known mutations with characterised clinical phenotypes. Using manual structural analysis we managed to explain the severity of all but one of the mutations. By observing the properties of these mutations we could perform good predictions on, at the time, not classified mutations. For the cancer suppressor protein p53, there are over thousand mutations with known activity. To be able to analyse such a large number of mutations we developed an automated method for evaluation of the mutation effect called PREDMUT. In this method we include twelve different prediction parameters including two energy parameters calculated using an energy minimization procedure. The method manages to differentiate severe mutations from non-severe mutations with 77% accuracy on all possible single base substitutions and with 88% on mutations found in breast cancer patients. The automated prediction was further applied to CYP11B1 (steroid 11-beta-hydroxylase), which in a similar way as CYP21 causes CAH when deficient. A generalized method applicable to any kind of globular protein was developed. The method was subsequently evaluated on nine additional proteins for which mutants were known with annotated disease phenotypes. This prediction achieved 84% accuracy on CYP11B1 and 81% accuracy in total on the evaluation proteins while leaving 8% as unclassified. By increasing the number of unclassified mutations the accuracy of the remaining mutations could be increased on the evaluation proteins and substantially increase the classification quality as measured by the Matthews correlation coefficient. Servers with predictions for all possible single based substitutions are provided for p53, CYP21 and CYP11B1. The amyloid formation of IAPP (islet amyloid polypeptide) is strongly connected to diabetes and has been studied using both molecular dynamics and Monte Carlo energy minimization. The effects of mutations on the amount and speed of amyloid formation were investigated using three approaches. Applying a consensus of the three methods on a number of interesting mutations, 94% of the mutations could be correctly classified as amyloid forming or not, evaluated with in vitro measurements. In the brain there are many proteins whose functions and interactions are largely unknown. GDNF (glial cell line-derived neurotrophic factor) and NCAM (neural cell adhesion molecule) are two such neuron connected proteins that are known to interact. The form of interaction was studied using protein--protein docking where a docking interface was found mediated by four oppositely charged residues in respective protein. This interface was subsequently confirmed by mutagenesis experiments. The NCAM dimer interface upon binding to the GDNF dimer was also mapped as well as an additional interacting protein, GFRα1, which was successfully added to the protein complex without any clashes. A large and well studied protein family is the alcohol dehydrogenase family, ADH. A class of this family is ADH3 (alcohol dehydrogenase class III) that has several known substrates and inhibitors. By using virtual screening we tried to characterize new ligands. As some ligands were already known we could incorporate this knowledge when the compound docking simulations were scored and thereby find two new substrates and two new inhibitors which were subsequently successfully tested in vitro. ANTR1 (anion transporter 1) is a membrane bound transporter important in the photosynthesis in plants. To be able to study the amino acid residues involved in inorganic phosphate transportation a homology model of the protein was created. Important residues were then mapped onto the structure using conservation analysis and we were in this way able to propose roles of amino acid residues involved in the transportation of inorganic phosphate. Key residues were subsequently mutated in vitro and a transportation process could be postulated. To conclude, we have used several molecular modelling techniques to find functional clues, interaction sites and new ligands. Furthermore, we have investigated the effect of muations on the function and structure of a multitude of disease related proteins.