Dissertations / Theses: 'Protein structure analysis'

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Pritchard, Leighton. "Evolutionary and structural analysis of protein structure-function relationships." Thesis, University of Strathclyde, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.248316.

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Jonsson, Andreas. "Mass spectrometry in protein structure analysis /." Stockholm, 2001. http://diss.kib.ki.se/2001/91-628-4716-3/.

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Copley, Richard Robertson. "Analysis and prediction of protein structure." Thesis, University of Oxford, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.361954.

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Boscott, Paul Edmond. "Sequence analysis in protein structure prediction." Thesis, University of Oxford, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.386870.

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Hommola, Susan Kerstin. "Categorical data analysis of protein structure." Thesis, University of Leeds, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.578618.

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It has long been known that the amino-acid sequence of a protein determines its 3- dimensional structure, but accurate ab initio prediction of structure from sequence remains elusive. In this thesis, we aim to gain insight into generic principles of protein folding through statistical modelling of protein structure. The first part is concerned with local protein structure. We study the relationship of dihedral angles in short protein segments up to a length of three residues. We adopt a contingency table approach, exploring a targeted set of hypotheses through log-linear modelling to detect patterns of association between the dihedral angles in the segments considered. For segments of length two (dipeptides), our models indicate a substantial association of the side-chain conformation of the first residue with the backbone conformation of the second residue (side-to-back interaction) as well as a weaker, but still significant, associa- tion of the backbone conformation of the first residue with the side-chain conformation of the second residue (back-to-side interaction). Comparison of these interactions across dif- ferent dipeptides through cluster analysis reveals a striking pattern. For the side-to-back term, all dipeptides having the same first residue cluster together, whereas for the back- to-side term we observe a much weaker pattern. This suggests that the conformation of the first residue dictates the conformation of the second. Our categorical approach proves difficult for the analysis of longer segments due to the discrepancy between the increased complexity and the shrinking amount of data available. In the second part, we study non-local interactions represented by contact maps. Our approach focuses entirely on the positions of contacting residues and is completely inde- pendent of protein amino-acid sequence. We investigate and quantify patterns in three specific regions of aggregated contact maps of single-domain proteins belonging to the four major SCOP classes (all-α, all-β, α/β, α+β) using logistic regression models. The first two regions represent contacts of residues aligned to the N-terminus with subsequent residues, and contacts of residues aligned to the C-terminus with previous residues, in a symmetric fashion with respect to the chain termini. The third region contains contacts between terminal residues. The models for each region contain factors for the positions of contacting residues as well as factors describing parallel and anti-parallel β-strand contact patterns. There is an interesting asymmetry between N-aligned and C-aligned contacts for the α/β SCOP class. The region around the -terminus shows a strong propensity towards parallel contacts between the first few residues and residues further along the sequence, whereas the last few residues do not show any strong patterns. This N-terminal dominance could indicate cotranslational folding. The other classes do not exhibit this asymmetry, but reveal predominantly anti-parallel β-strand patterns (all-β class), mixed patterns (α+β class) or no distinct patterns (all-a class). Contact patterns in the terminal regions are generally weak showing no strong preferences towards parallel or anti-parallel f3-strand contacts.

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Michie, Alexander David. "Analysis and classification of protein structure." Thesis, University College London (University of London), 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.267834.

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Elliott, Craig Julian. "Analysis and prediction of protein structure." Thesis, University of York, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.284165.

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Gkolias, Theodoros. "Shape analysis in protein structure alignment." Thesis, University of Kent, 2018. https://kar.kent.ac.uk/66682/.

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In this Thesis we explore the problem of structural alignment of protein molecules using statistical shape analysis techniques. The structural alignment problem can be divided into three smaller ones: the representation of protein structures, the sampling of possible alignments between the molecules and the evaluation of a given alignment. Previous work done in this field, can be divided in two approaches: an adhoc algorithmic approach from the Bioinformatics literature and an approach using statistical methods either in a likelihood or Bayesian framework. Both approaches address the problem from a different scope. For example, the algorithmic approach is easy to implement but lacks an overall modelling framework, and the Bayesian address this issue but sometimes the implementation is not straightforward. We develop a method which is easy to implement and is based on statistical assumptions. In order to asses the quality of a given alignment we use a size and shape likelihood density which is based in the structure information of the molecules. This likelihood density is also extended to include sequence infor- mation and gap penalty parameters so that biologically meaningful solution can be produced. Furthermore, we develop a search algorithm to explore possible alignments from a given starting point. The results suggest that our approach produces better or equal alignments when it is compared to the most recent struc- tural alignment methods. In most of the cases we managed to achieve a higher number of matched atoms combined with a high TMscore. Moreover, we extended our method using Bayesian techniques to perform alignments based on posterior modes. In our approach, we estimate directly the mode of the posterior distribution which provides the final alignment between two molecules. We also, choose a different approach for treating the mean parameter. In previous methods the mean was either integrated out of the likelihood density or considered as fixed. We choose to assign a prior over it and obtain its posterior mode. Finally, we consider an extension of the likelihood model assuming a Normal density for both the matched and unmatched parts of a molecule and diagonal covariance structure. We explore two different variants. In the first we consider a fixed zero mean for the unmatched parts of the molecules and in the second we consider a common mean for both the matched and unmatched parts. Based on simulated and real results, both models seems to perform well in obtaining high number of matched atoms and high TMscore.

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Chivian, Dylan Casey. "Application of information from homologous proteins for the prediction of protein structure /." Thesis, Connect to this title online; UW restricted, 2005. http://hdl.handle.net/1773/9264.

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Betts, Matthew James. "Analysis and prediction of protein-protein recognition." Thesis, University College London (University of London), 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.313795.

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Bliven, Spencer Edward. "Structure-Preserving Rearrangements| Algorithms for Structural Comparison and Protein Analysis." Thesis, University of California, San Diego, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3716489.

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Protein structure is fundamental to a deep understanding of how proteins function. Since structure is highly conserved, structural comparison can provide deep information about the evolution and function of protein families. The Protein Data Bank (PDB) continues to grow rapidly, providing copious opportunities for advancing our understanding of proteins through large-scale searches and structural comparisons. In this work I present several novel structural comparison methods for specific applications, as well as apply structure comparison tools systematically to better understand global properties of protein fold space.

Circular permutation describes a relationship between two proteins where the N-terminal portion of one protein is related to the C-terminal portion of the other. Proteins that are related by a circular permutation generally share the same structure despite the rearrangement of their primary sequence. This non-sequential relationship makes them difficult for many structure alignment tools to detect. Combinatorial Extension for Circular Permutations (CE-CP) was developed to align proteins that may be related by a circular permutation. It is widely available due to its incorporation into the RCSB PDB website.

Symmetry and structural repeats are common in protein structures at many levels. The CE-Symm tool was developed in order to detect internal pseudosymmetry within individual polypeptide chains. Such internal symmetry can arise from duplication events, so aligning the individual symmetry units provides insights about conservation and evolution. In many cases, internal symmetry can be shown to be important for a number of functions, including ligand binding, allostery, folding, stability, and evolution.

Structural comparison tools were applied comprehensively across all PDB structures for systematic analysis. Pairwise structural comparisons of all proteins in the PDB have been computed using the Open Science Grid computing infrastructure, and are kept continually up-to-date with the release of new structures. These provide a network-based view of protein fold space. CE-Symm was also applied to systematically survey the PDB for internally symmetric proteins. It is able to detect symmetry in ~20% of all protein families. Such PDB-wide analyses give insights into the complex evolution of protein folds.

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Cao, Haibo. "Protein Structure Recognition From Eigenvector Analysis to Structural Threading Method." Washington, D.C. : Oak Ridge, Tenn. : United States. Dept. of Energy. Office of Science ; distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy, 2003. http://www.osti.gov/servlets/purl/822060-2L2Xvm/native/.

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Thesis (Ph.D.); Submitted to Iowa State Univ., Ames, IA (US); 12 Dec 2003.
Published through the Information Bridge: DOE Scientific and Technical Information. "IS-T 2028" Haibo Cao. 12/12/2003. Report is also available in paper and microfiche from NTIS.

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Albrecht, Birgit. "Novel representation and analysis of protein structure." Thesis, University of Oxford, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.418641.

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Russell, Robert Bruce. "Computer analysis of protein sequence and structure." Thesis, University of Oxford, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.358736.

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Rufino, Stephen Duarte. "Analysis, comparison and prediction of protein structure." Thesis, Birkbeck (University of London), 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.243648.

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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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Mizuno, Nobuhiro. "Structure-based functional analysis of DsrD protein." 京都大学 (Kyoto University), 2004. http://hdl.handle.net/2433/147845.

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Leonardi, Emanuela. "Bioinformatic Analysis of Protein Mutations." Doctoral thesis, Università degli studi di Padova, 2012. http://hdl.handle.net/11577/3426280.

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Many gene defects have been associated to genetic disorders, but the details of molecular mechanisms by which they contribute to the disease are often unclear. The study of mutation effects at the protein level can help elucidate the biological processes involved in the disease and the role of the protein in it. Bioinformatics can help to address this problem, being the connection between different disciplines including clinical, genetics, structural biology, and biochemistry. By using a computational approach I tackled the analysis of some examples of biomedical interesting proteins integrating various sources of data and addressing experimental and clinical investigations. Experimentally defined structures and molecular modelling were used as a basis to determine the protein structure-function relationship, which is essential to gain insights into disease genotype-phenotype correlation. Proteins have been further analyzed in their context, considering interactions that they take in specific cellular compartments. The results have been used to formulate functional hypotheses, which in some cases have been tested and confirmed by further investigations performed by cooperation groups. Mutations found in genes encoding these proteins have been evaluated for their impact on the protein structure and function by using several available prediction methods. These studies provided the idea for developing novel approaches, using residue interaction networks and an ensemble of methods. A novel strategy has been also designed to evaluate genomic data obtained by next generation sequencing technology. This consists in using available resources and software to prioritize rare functional variants and estimate their contribution to the disease. The novel approaches developed in this thesis have been applied and assessed at the Critical Assessment of Genome Interpretation (CAGI) experiment in 2011, providing in some cases very successful results
Alterazioni genetiche sono state identificate per molte malattie di natura genetica, ma in molti casi i meccanismi molecolari che contribuiscono all’insorgere della malattia non sono ancora chiari. Lo studio degli effetti delle mutazioni a livello della proteina permette di chiarire i processi biologici coinvolti nella malattia e il ruolo della proteina in essa. La bioinformatica può aiutare a affrontare questo problema rappresentando il punto di connessione tra diverse discipline quali la clinica, la genetica, la biologia strutturale e la biochimica. In questa tesi ho impiegato un approccio computazionale per affrontare l’analisi di alcuni esempi di proteine di interesse biomedico, integrando diverse risorse di dati e indirizzando la ricerca sperimentale e clinica. Strutture proteiche determinate sperimentalmente o mediante il modelling molecolare sono state utilizzate come base per determinare la relazione tra struttura e funzione, essenziale per ottenere informazioni sulla correlazione genotipo-fenotipo. Le proteine prese in esame sono state inoltre analizzate nel loro contesto, considerando le interazioni che avvengono con altre proteine o ligandi nei diversi compartimenti cellulari. I risultati dell’analisi bioinformatica sono stati poi utilizzati per formulare ipotesi funzionali che in alcuni casi sono state verificate e confermate sperimentalmente da altri gruppi di ricerca. Le mutazioni identificate nei geni codificanti per le proteine in esame sono state valutate per il loro impatto sulla struttura e funzione della proteina utilizzando numerosi metodi di predizione disponibili online. Le diverse applicazioni descritte in questa tesi hanno fornito l’idea per lo sviluppo di nuovi approcci computazionali per lo caratterizzazione strutturale e funzionale di proteine e dei loro mutanti. Si è visto che la predizione migliora utilizzando un ensemble dei diversi metodi di predizione disponibili. Inoltre, per la predizione degli effetti di mutazioni è stato ideato un nuovo approccio computazionale che utilizza le reti di interazione tra residui per rappresentare la struttura proteica. Questi metodi sono stati utilizzati anche nell’analisi di dati genomici originati da nuove tecnologie di sequenziamento. Questo ambito necessita di nuove strategie di indagine per l’individuazione di poche varianti causative in un’enorme quantità di varianti identificate di dubbio significato. A questo scopo viene proposta una strategia di analisi che utilizza informazioni derivanti dalle reti di interazioni proteiche. I nuovi approcci formulati in questa tesi sono stati applicati e valutati ad un nuovo esperimento internazionale, chiamato Critical Assessment of Genome Interpretation (CAGI), fornendo in alcuni casi ottimi risultati

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Ellis, Jonathan James. "Towards the prediction of protein-RNA interactions through protein structure analysis." Thesis, University of Sussex, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.444117.

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Gillies, Susan Alana. "The structure-function analysis of the patched protein /." [St. Lucia, Qld.], 2004. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe18579.pdf.

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Harrison, Paul Martin. "Analysis and prediction of protein structure : disulphide bridges." Thesis, University College London (University of London), 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.339217.

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Moore, Barbara Kirsten. "An analysis of representations for protein structure prediction." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/32620.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.
Includes bibliographical references (p. 270-279).
by Barbara K. Moore Bryant.
Ph.D.

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Almond, Brian Douglas. "Genetic analysis of delta-endotoxin CrylA protein structure /." The Ohio State University, 1992. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487777170408234.

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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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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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Buckle, Ashley M. "Crystallographic analysis of the structure and function of barnase." Thesis, University of Cambridge, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.321231.

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FANTINI, MARCO. "From in vitro evolution to protein structure." Doctoral thesis, Scuola Normale Superiore, 2020. http://hdl.handle.net/11384/91067.

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In the nanoscale, the machinery of life is mainly composed by macromolecules and macromolecular complexes that through their shapes create a network of interconnected mechanisms of biological processes. The relationship between shape and function of a biological molecule is the foundation of structural biology, that aims at studying the structure of a protein or a macromolecular complex to unveil the molecular mechanism through which it exerts its function. What about the reverse: is it possible by exploiting the function for which a protein was naturally selected to deduce the protein structure? To this aim we developed a method, called CAMELS (Coupling Analysis by Molecular Evolution Library Sequencing), able to obtain the structural features of a protein from an artificial selection based on that protein function. With CAMELS we tried to reconstruct the TEM-1 beta lactamase fold exclusively by generating and sequencing large libraries of mutational variants. Theoretically with this method it is possible to reconstruct the structure of a protein regardless of the species of origin or the phylogenetical time of emergence when a functional phenotypic selection of a protein is available. CAMELS allows us to obtain protein structures without needing to purify the protein beforehand.

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Kamada, Mayumi. "Analysis and Prediction Methods for Protein Structure and Function." 京都大学 (Kyoto University), 2013. http://hdl.handle.net/2433/174836.

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Gilbert, Richard James. "Novel programs for protein sequence analysis and structure prediction." Thesis, University of Oxford, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.305431.

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Cusack, Margaret Rose. "A structure-function analysis of the sweet protein, thaumatin." Thesis, University of Liverpool, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.279662.

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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.
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.
Jeanette Hargbo.

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Morris, Darryl William Seymour. "Low angle protein phasing." Thesis, University of York, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.341631.

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Rhonemus, Troy A. "Reagents for protein analysis and modification." Virtual Press, 1998. http://liblink.bsu.edu/uhtbin/catkey/1115753.

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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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Yang, Yinhua, and 楊銀花. "Application of biomolecular NMR spectroscopy for protein structure determination." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B42182013.

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Zhao, Zhiyu. "Robust and Efficient Algorithms for Protein 3-D Structure Alignment and Genome Sequence Comparison." ScholarWorks@UNO, 2008. http://scholarworks.uno.edu/td/851.

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Sequence analysis and structure analysis are two of the fundamental areas of bioinformatics research. This dissertation discusses, specifically, protein structure related problems including protein structure alignment and query, and genome sequence related problems including haplotype reconstruction and genome rearrangement. It first presents an algorithm for pairwise protein structure alignment that is tested with structures from the Protein Data Bank (PDB). In many cases it outperforms two other well-known algorithms, DaliLite and CE. The preliminary algorithm is a graph-theory based approach, which uses the concept of \stars" to reduce the complexity of clique-finding algorithms. The algorithm is then improved by introducing \double-center stars" in the graph and applying a self-learning strategy. The updated algorithm is tested with a much larger set of protein structures and shown to be an improvement in accuracy, especially in cases of weak similarity. A protein structure query algorithm is designed to search for similar structures in the PDB, using the improved alignment algorithm. It is compared with SSM and shows better performance with lower maximum and average Q-score for missing proteins. An interesting problem dealing with the calculation of the diameter of a 3-D sequence of points arose and its connection to the sublinear time computation is discussed. The diameter calculation of a 3-D sequence is approximated by a series of sublinear time deterministic, zero-error and bounded-error randomized algorithms and we have obtained a series of separations about the power of sublinear time computations. This dissertation also discusses two genome sequence related problems. A probabilistic model is proposed for reconstructing haplotypes from SNP matrices with incomplete and inconsistent errors. The experiments with simulated data show both high accuracy and speed, conforming to the theoretically provable e ciency and accuracy of the algorithm. Finally, a genome rearrangement problem is studied. The concept of non-breaking similarity is introduced. Approximating the exemplar non-breaking similarity to factor n1..f is proven to be NP-hard. Interestingly, for several practical cases, several polynomial time algorithms are presented.

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37

Hannavy, Kevin. "Structure-function analysis of the TonB protein of Salmonella typhimurium." Thesis, University of Oxford, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.306594.

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38

Roznowski, Aaron. "A Structure-Function Analysis of the phiX174 DNA Piloting Protein." Thesis, The University of Arizona, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=13812936.

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In order to initiate an infection, bacteriophages must deliver their large, hydrophilic genomes across their host’s hydrophobic cell wall. Bacteriophage ϕX174 accomplishes this task with a set of identical DNA piloting proteins. The structure of the piloting protein’s central domain was solved to 2.4 Å resolution. In it, ten proteins are oligomerized into an α-helical barrel, or tube, that is long enough to span the host’s cell wall and wide enough for the circular, ssDNA to pass through. This structure was used as a guide to explore the mechanics of ϕX174 genome delivery. In the first study, the H-tube’s highly repetitive primary and quaternary structure made it amenable to a genetic analysis using in-frame insertions and deletions. Length-altered proteins were characterized for the ability to perform the protein’s three known functions: participation in particle assembly, genome translocation, and stimulation of viral protein synthesis.

The tube’s inner surface was altered in the second study. The surface is primarily lined with amide and guanidinium containing amino acid side chains with the exception of four sites near the tube’s C-terminal end. The four sites are conserved across microvirus clades, suggesting that they may play an important role during genome delivery. To test this hypothesis and explore the general role of the amide and guanidinium containing side chains, the amino acids at these sites were changed to glutamine. The resulting mutants had a cold-sensitive phenotype at 22 °C. Viral lifecycle steps were assayed in order to determine which step was disrupted by the mutant glutamine residues. The results support a model in which a balance of forces governs genome delivery: potential energy provided by the densely packaged viral genome and/or an osmotic gradient push the genome into the cell, while the tube’s inward facing residues exert a frictional force on the genome as it passes.

Bacteriophage must first identify a susceptible host prior to genome delivery. In the final study, biochemical and genetic analyses were conducted with two closely related bacteriophages, α3 and ST-1. Despite ~90% amino acid identity, the natural host of α3 is Escherichia coli C, whereas ST-1 is a K-12-specific phage. To determine which structural proteins conferred host range specificity, chimeric virions were generated by individually interchanging the coat, spike, or DNA pilot proteins. Interchanging the coat protein switched host range. However, host range expansion could be conferred by single point mutations in the coat protein. The expansion phenotype was recessive: mutant progeny from co-infected cells did not display the phenotype. Novel virus propagation and selection protocols were developed to isolate host range expansion mutants. The resulting genetic and structural data were consistent enough that host range expansion could be predicted, broadening the classical definition of antireceptors to include interfaces between protein complexes within the capsid.

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39

Nooney, Colleen. "Statistical analysis of coevolution in protein structure and in ecology." Thesis, University of Leeds, 2016. http://etheses.whiterose.ac.uk/16337/.

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In this thesis we explore the theory of coevolution. Yip et al. (2008) define coevolution to be the change in one biological object as a result of the change in one or more associated objects. The process of coevolution has been observed at many biological levels; from microscopic to macroscopic. We explore coevolution at the molecular level by studying protein sequences and their corresponding structures to determine how correlated areas of multiple sequence alignments and structures have coevolved. At the species level, we assess how coevolution drives ecological systems of interacting phylogenetic trees. Determining the three-dimensional structure of proteins is of interest because the structure of a protein is constrained by its function. Proteins carry out vital functions in every cell and are arguably the most important biological molecule found in organisms. Multiple sequence alignments of protein families contain evolutionary information on these functional constraints. In the first part of this thesis, we aim to develop a method to identify correlated mutations within multiple sequence alignments. These correlated positions are used to predict residues that are in close proximity in three-dimensional space. In turn these structural constraints can be used in ab initio protein structure prediction. Currently the most accurate way to determine protein structure is using experimental techniques such as Nuclear Magnetic Resonance (NMR) and X-ray Crystallography. These techniques are expensive and take time. As a result, the proteins that are chosen to have their structures determined may be subject to selection bias. Initially, we focus on a preliminary analysis of the trypsin protein family. We align trypsin structures from a variety of species using a multiple structural alignment algorithm, to determine how the structure of the family has evolved. Basic summary statistics of the aligned distance matrices reveal a set of residues where the distance between these specific residues and every other residue in the structure is highly conserved across all of the structures in the protein family. We label these residues as ‘anchor residues’ because they appear to hold the structure of the trypsin protein family in place like anchors. Following this, we develop a regularised logistic regression model to detect correlated mutations in multiple sequence alignments. We successfully apply our method to a number of small artificial test alignments. When applied to real Pfam datasets, our method has varying success at identifying coevolving columns that are close in physical proximity. In the second part of this thesis we develop a new method to test efficiently for cospeciation in multitrophic ecological systems. Our method can be applied to bitrophic and tritrophic systems, with the potential to generalise to higher order systems and networks. We utilise methods from electrical circuit theory to reduce higher order systems into two vectors of electrically equivalent patristic distances that can be compared using Spearman’s rank correlation coefficient. Compared to existing methods, our method has equal or higher performance at both trophic levels. To test our method, interacting systems of phylogenetic trees were simulated by generating random trees, and separately, their interaction matrices. Simulating the systems in this way does not take into account how the systems might have evolved. We propose a more realistic simulation method that evolves over time. The algorithm starts with one species per lineage, that are assumed to have an ecological interaction. The joint evolution of these species is simulated by sampling the time at which evolutionary events occur from an exponential distribution. We explore speciation events, and gaining and losing ecological interactions. Each of these events are controlled by rate parameters. By experimenting with these parameters, a wide range of systems with different cospeciation properties can be simulated. We show that a wide range of systems that can be produced using our method.

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40

Atkinson, Ian E. "Mass Spectrometric Analysis of Environmental Contaminants, Protein Structure and Expression." Cleveland State University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=csu1231174291.

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41

Caswell, Clayton Christopher. "The SCL1 protein of Streptococcus pyogenes a structure-function analysis /." Morgantown, W. Va. : [West Virginia University Libraries], 2008. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=6026.

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Thesis (Ph. D.)--West Virginia University, 2008.
Title from document title page. Document formatted into pages; contains xi, 190 p. : ill. (some col.). Includes abstract. Includes bibliographical references.

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42

Sodhi, Jaspreet Singh. "Prediction and analysis of functionally important sites in protein structure." Thesis, University College London (University of London), 2005. http://discovery.ucl.ac.uk/1446682/.

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Identifying the molecular role of a target protein is becoming a pressing issue in structural bioinformatics. As the number of uncharacterised proteins accumulate and structural genomics initiatives pick up pace reliable and accurate approaches are required to make sense of the growing structural data in the most effective manner. This study aims to improve the characterisation and cataloguing of functionally important regions in proteins to improve the transfer of information from structure to functional annotation. This is of great importance as effective and accurate annotations are a pre-requisite to unlocking the biological information encoded within the genomes of organisms. A novel and automatic method is presented which identifies residues interacting with metal ions. The aim has been to focus the approach such that it is capable of the prediction of interactions even when reliable side-chain information is unavailable. The results demonstrate that a combination of sequence and structural features can be combined to achieve adequate predictions in both crystal structures as well as low resolution fold recognition models. The findings of the metal binding study are used to extend the approach and predict larger interaction regions found in DNA binding proteins. These important class of proteins are of particular interest given their fundamental control over biological processes. The results highlight effective classification is possible of both residues forming DNA contacts as well as the discrimination of DNA binding from non-DNA binding proteins. Crucially, DNA binding predictions are shown for a genome-wide study of Sacchromyces cerevisiae. The final study of the thesis focuses the attention on the alternative problem of improving the quality of protein structure predictions from fold recognition. The results indicate that site classification provides an effective basis by which protein models can be assessed: this in turn leads to the development and benchmarking of a new method which significantly improves the quality of protein models.

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43

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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44

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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Abstract:

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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45

Maus, Aaron. "Formulation of Hybrid Knowledge-Based/Molecular Mechanics Potentials for Protein Structure Refinement and a Novel Graph Theoretical Protein Structure Comparison and Analysis Technique." ScholarWorks@UNO, 2019. https://scholarworks.uno.edu/td/2673.

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Proteins are the fundamental machinery that enables the functions of life. It is critical to understand them not just for basic biology, but also to enable medical advances. The field of protein structure prediction is concerned with developing computational techniques to predict protein structure and function from a protein’s amino acid sequence, encoded for directly in DNA, alone. Despite much progress since the first computational models in the late 1960’s, techniques for the prediction of protein structure still cannot reliably produce structures of high enough accuracy to enable desired applications such as rational drug design. Protein structure refinement is the process of modifying a predicted model of a protein to bring it closer to its native state. In this dissertation a protein structure refinement technique, that of potential energy minimization using hybrid molecular mechanics/knowledge based potential energy functions is examined in detail. The generation of the knowledge-based component is critically analyzed, and in the end, a potential that is a modest improvement over the original is presented. This dissertation also examines the task of protein structure comparison. In evaluating various protein structure prediction techniques, it is crucial to be able to compare produced models against known structures to understand how well the technique performs. A novel technique is proposed that allows an in-depth yet intuitive evaluation of the local similarities between protein structures. Based on a graph analysis of pairwise atomic distance similarities, multiple regions of structural similarity can be identified between structures independently of relative orientation. Multidomain structures can be evaluated and this technique can be combined with global measures of similarity such as the global distance test. This method of comparison is expected to have broad applications in rational drug design, the evolutionary study of protein structures, and in the analysis of the protein structure prediction effort.

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46

Gane, Paul J. "A sequence, structure and electrostatic analysis of the disulphide oxidoreductases." Thesis, University of Kent, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.242888.

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47

Liu, Xiao-yu. "Structure-function analysis of two Drosophila neuronal cell adhesion proteins fasciclin I and amalgam /." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1199298661.

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48

Dancea, Felician. "New methods for automated NMR data analysis and protein structure determination." [S.l.] : [s.n.], 2005. http://deposit.ddb.de/cgi-bin/dokserv?idn=974442569.

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49

Valkov, Eugene. "Design and analysis of self-assembling protein systems." Thesis, University of Oxford, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.670100.

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50

Hennessy, Fritha. "Characterisation of the J domain aminoacid residues important for the interaction of DNAJ-like proteins with HSP70 chaperones." Thesis, Rhodes University, 2004. http://hdl.handle.net/10962/d1003996.

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The 70 kDa heat shock proteins (Hsp70s) are vital for normal protein folding, as they stabilise the unfolded state of nascent polypeptides, allowing these sufficient time to attain a correct tertiary structure. Hsp70s are aided by the DnaJ-like family of proteins, which interact with Hsp70s in order to enhance the chaperone activity of these proteins. DnaJ-like proteins contain a J domain, a seventy amino acid domain consisting of four α-helices, which is defined by the presence of an invariant tripeptide of histidine, proline and aspartic acid (HPD motif). This motif is key to the interaction between DnaJ-like proteins and Hsp70s. This thesis has focused on determining the presence of other conserved residues in the J domain and their role in mediating the interaction of DnaJ-like proteins with partner Hsp70s. DnaJ-like proteins from Agrobacterium tumefaciens RUOR were isolated and used as a model system. A. tumefaciens DnaJ (Agt DnaJ) was able to replace the lack of E. coli DnaJ in an E. coli null mutant strain, however, additional A. tumefaciens DnaJ-like proteins Agt DjC1/DjlA, Agt DjC2 and Agt DjC5 were unable to complement for the lack of E. coli DnaJ. Replacement of the Agt DnaJ J domain with J domains from these proteins resulted in non-functional chimeric proteins, despite some sequence conservation. The kinetics of the basal specific ATPase activity of Agt DnaK, and its ability to have this activity stimulated by Agt DnaJ and Agt DnaJ-H33Q were also investigated. Stimulation of the ATPase activity by Agt DnaJ ranged between 1.5 to 2 fold, but Agt DnaJ-H33Q was unable to stimulate the basal ATPase activity. Conserved amino acids in the J domain were identified in silico, and these residues were substituted in the J domain of Agt DnaJ. The ability of these derivative proteins to replace E. coli DnaJ was investigated. Alterations in the HPD motif gave rise to proteins unable to complement for lack of E. coli DnaJ, consistent with literature. Agt DnaJ-R26A was unable to replace E. coli DnaJ suggesting that Arg26 could be key to the interaction with partner Hsp70s. Agt DnaJ-D59A was unable to replace E. coli DnaJ; substitutions in Asp59 have not previously been shown to impact on the function of DnaJ. Substituting Arg63 in Agt DnaJ abrogated the levels of complementation. Substitution of several structural residues was also found to disrupt the in vivo function of Agt DnaJ suggesting that the maintenance of the structural integrity of the J domain was important for function. This study has identified a number of residues critical to the structure and function of the J domain of Agt DnaJ, and potentially of general importance as molecular determinants for DnaJ-Hsp70 interaction.

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