Dissertations / Theses: 'Protein structure prediction'

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Cuff, James Andrew. "Protein structure prediction." Thesis, University of Oxford, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.365685.

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Wood, Matthew J. "Protein secondary structure prediction." Thesis, University of Nottingham, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.430525.

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Choi, Yoonjoo. "Protein loop structure prediction." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:bd5c1b9b-89ba-4225-bc17-85d3f5067e58.

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This dissertation concerns the study and prediction of loops in protein structures. Proteins perform crucial functions in living organisms. Despite their importance, we are currently unable to predict their three dimensional structure accurately. Loops are segments that connect regular secondary structures of proteins. They tend to be located on the surface of proteins and often interact with other biological agents. As loops are generally subject to more frequent mutations than the rest of the protein, their sequences and structural conformations can vary significantly even within the same protein family. Although homology modelling is the most accurate computational method for protein structure prediction, difficulties still arise in predicting protein loops. Protein loop structure prediction is therefore a bottleneck in solving the protein structure prediction problem. Reflecting on the success of homology modelling, I implement an improved version of a database search method, FREAD. I show how sequence similarity as quantified by environment specific substitution scores can be used to significantly improve loop prediction. FREAD performs appreciably better for an identifiable subset of loops (two thirds of shorter loops and half of the longer loops tested) than ab initio methods; FREAD's predictive ability is length independent. In general, it produces results within 2Å root mean square deviation (RMSD) from the native conformations, compared to an average of over 10Å for loop length 20 for any of the other tested ab initio methods. I then examine FREAD’s predictive ability on a specific type of loops called complementarity determining regions (CDRs) in antibodies. CDRs consist of six hypervariable loops and form the majority of the antigen binding site. I examine CDR loop structure prediction as a general case of loop structure prediction problem. FREAD achieves accuracy similar to specific CDR predictors. However, it fails to accurately predict CDR-H3, which is known to be the most challenging CDR. Various FREAD versions including FREAD with contact information (ConFREAD) are examined. The FREAD variants improve predictions for CDR-H3 on homology models and docked structures. Lastly, I focus on the local properties of protein loops and demonstrate that the protein loop structure prediction problem is a local protein folding problem. The end-to-end distance of loops (loop span) follows a distinctive frequency distribution, regardless of secondary structure elements connected or the number of residues in the loop. I show that the loop span distribution follows a Maxwell-Boltzmann distribution. Based on my research, I propose future directions in protein loop structure prediction including estimating experimentally undetermined local structures using FREAD, multiple loop structure prediction using contact information and a novel ab initio method which makes use of loop stretch.

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Son, Hyeon S. "Prediction of membrane protein structure." Thesis, University of Oxford, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.337775.

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5

Offman, Marc Nathan. "Protein structure prediction and refinement." Thesis, University College London (University of London), 2008. http://discovery.ucl.ac.uk/16775/.

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Over the last few years it has been shown that protein modelling techniques, especially template based modelling, are now accurate enough for qualitative analysis and decision-making in support of a wide range of experimental work. Automatic protein modelling pipelines are becoming ever more accurate; however, this has come hand in hand with an increasingly complicated interplay between all components involved. Despite all progress, still important problems remain and so far computational methods cannot routinely meet the accuracy of experimentally determined protein structures. In protein modelling pipelines, several important steps dictate a model's quality. Selecting a good template and aligning the query sequence correctly, backbone completion, model refinement and final model selection are considered the main steps. As a first step to approach protein refinement, a genetic algorithm (GA) for protein model recombination and optimization is presented in this work. This algorithm has the potential, to drive models away from the template towards the native structure. Furthermore, a complete and novel modelling pipeline, incorporating this GA is presented. In this context, a new scoring scheme, backbone repair algorithm and several other findings are reported and presented: We introduce the novel concept of Alternating Evolutionary Pressure, i.e. intermediate rounds within the GA simulation, where unrestrained linear growth of the model population is allowed. This approach improves the structural sampling and thereby facilitates energy-based model selection. Finally, the GA in combination with molecular dynamics simulations is used in the context of protein engineering. Several mutants were identified to stabilise and increase the activity of the cancer drug L-Asparaginase, a complex enzyme. The successful prediction of these mutations stresses the importance of protein molecular modelling for cell biology and in a clinical context.

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Munro, Robin Edward James. "Protein structure prediction and modelling." Thesis, University College London (University of London), 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.313827.

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7

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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8

Hosur, Raghavendra. "Structure-based algorithms for protein-protein interaction prediction." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/75843.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Materials Science and Engineering, 2012.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student submitted PDF version of thesis.
Includes bibliographical references (p. 109-124).
Protein-protein interactions (PPIs) play a central role in all biological processes. Akin to the complete sequencing of genomes, complete descriptions of interactomes is a fundamental step towards a deeper understanding of biological processes, and has a vast potential to impact systems biology, genomics, molecular biology and therapeutics. PPIs are critical in maintenance of cellular integrity, metabolism, transcription/ translation, and cell-cell communication. This thesis develops new methods that significantly advance our efforts at structure- based approaches to predict PPIs and boost confidence in emerging high-throughput (HTP) data. The aims of this thesis are, 1) to utilize physicochemical properties of protein interfaces to better predict the putative interacting regions and increase coverage of PPI prediction, 2) increase confidence in HTP datasets by identifying likely experimental errors, and 3) provide residue-level information that gives us insights into structure-function relationships in PPIs. Taken together, these methods will vastly expand our understanding of macromolecular networks. In this thesis, I introduce two computational approaches for structure-based proteinprotein interaction prediction: iWRAP and Coev2Net. iWRAP is an interface threading approach that utilizes biophysical properties specific to protein interfaces to improve PPI prediction. Unlike previous structure-based approaches that use single structures to make predictions, iWRAP first builds profiles that characterize the hydrophobic, electrostatic and structural properties specific to protein interfaces from multiple interface alignments. Compatibility with these profiles is used to predict the putative interface region between the two proteins. In addition to improved interface prediction, iWRAP provides better accuracy and close to 50% increase in coverage on genome-scale PPI prediction tasks. As an application, we effectively combine iWRAP with genomic data to identify novel cancer related genes involved in chromatin remodeling, nucleosome organization and ribonuclear complex assembly - processes known to be critical in cancer. Coev2Net addresses some of the limitations of iWRAP, and provides techniques to increase coverage and accuracy even further. Unlike earlier sequence and structure profiles, Coev2Net explicitly models long-distance correlations at protein interfaces. By formulating interface co-evolution as a high-dimensional sampling problem, we enrich sequence/structure profiles with artificial interacting homologus sequences for families which do not have known multiple interacting homologs. We build a spanning-tree based graphical model induced by the simulated sequences as our interface profile. Cross-validation results indicate that this approach is as good as previous methods at PPI prediction. We show that Coev2Net's predictions correlate with experimental observations and experimentally validate some of the high-confidence predictions. Furthermore, we demonstrate how analysis of the predicted interfaces together with human genomic variation data can help us understand the role of these mutations in disease and normal cells.
by Raghavendra Hosur.
Ph.D.

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9

Shatabda, Swakkhar. "Local Search Heuristics for Protein Structure Prediction." Thesis, Griffith University, 2014. http://hdl.handle.net/10072/365446.

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This thesis presents our research on protein structure prediction on discrete lattices. Given a protein’s amino acid sequence, the protein structure prediction problem is to find its three dimensional native structure that has the minimum free energy. Knowledge about the native protein structures and their respective folding process is a key to understand protein functionalities and consequently the basics of life. Protein structure prediction problem is one of the most challenging problems in molecular biology. In-vitro laboratory methods applied to this problem are very time-consuming, cost- expensive and failure-prone. Also, the search based optimization methods used are com- putationally very expensive. To tackle these, researchers have used various simplified models, such as low resolution energy functions and lattice-based structures, and applied incomplete local search methods on them. The simplified models help obtain back-bone structures first and then hierarchically work out the details. Local search methods can normally quickly find solutions although they suffer from re-visitation and stagnancy, and require good heuristics. In the literature, researchers have mostly used primitive ap- proaches based on random decisions at various choice points. Consequently, these methods are applicable to small-sized proteins only. In this thesis, we present a number of techniques to improve the performance of lo- cal search methods applied to protein structure prediction problem using discrete lattices. Firstly, we propose a memory based local search framework that maintains a set of already explored solutions for avoiding re-visitation and stores previously unexplored but promi- nent solutions for restarting to handle stagnation. A novel encoding scheme for protein structures is proposed to handle symmetry present in the search space. We also propose an approximate matching strategy that results in reducing redundancy in the search space.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
Institute for Integrated and Intelligent Systems
Science, Environment, Engineering and Technology
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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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Jain, Pooja. "Protein Structure Similarity, Classification and Prediction." Thesis, University of Nottingham, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.523727.

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13

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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14

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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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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Bazzoli, A. "Protein structure prediction and protein design with evolutionary algorithms." Doctoral thesis, Università degli Studi di Milano, 2009. http://hdl.handle.net/2434/64478.

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17

Montalvão, Rinaldo Wander. "Protein structure prediction : differential geometry of proteins and comparative modelling." Thesis, University of Cambridge, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.613753.

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18

Senekal, Frederick Petrus. "Protein secondary structure prediction using amino acid regularities." Diss., Pretoria : [s.n.], 2008. http://upetd.up.ac.za/thesis/available/etd-01232009-120040/.

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Rashid, Mahmood Abdur. "Heuristic Based Search for Protein Structure Prediction." Thesis, Griffith University, 2014. http://hdl.handle.net/10072/367134.

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Proteins that are essentially sequences of amino acids, adopt specific folded 3-dimensional (3D) structures to perform their specific tasks. However, misfolded proteins cause fatal diseases. Hence, protein structure prediction (PSP) has emerged as an important multi-disciplinary research problem. Given a protein sequence, the PSP problem is to find a 3D structure of the protein such that the total free energy amongst the amino acids in the sequence is minimised. In-vitro laboratory methods are time-consuming, expensive, and failure-prone. Conversely, computational methods are NP-hard even when the models are simplified by using low-resolution energy functions and lattice-based structures.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
Institute for Integrated and Intelligent Systems
Science, Environment, Engineering and Technology
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20

Larsson, Per. "Prediction, modeling, and refinement of protein structure /." Stockholm : Department of Biochemistry and Biophysics, Stockholm University, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-38253.

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Diss. (sammanfattning) Stockholm : Stockholms universitet, 2010.
At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 4: In press. Paper 5: Manuscript. Härtill 5 uppsatser.

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Dunlavy, Daniel Michael. "Homotopy optimization methods and protein structure prediction." College Park, Md. : University of Maryland, 2005. http://hdl.handle.net/1903/2882.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2005.
Thesis research directed by: Applied Mathematics and Scientific Computation Program. 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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22

C, Dukka Bahadur K. "Clique-based algorithms for protein structure prediction." 京都大学 (Kyoto University), 2006. http://hdl.handle.net/2433/143887.

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Steeg, Evan W. "Automated motif discovery in protein structure prediction." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq27733.pdf.

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24

Zhao, Jing. "Protein Structure Prediction Based on Neural Networks." Thèse, Université d'Ottawa / University of Ottawa, 2013. http://hdl.handle.net/10393/23636.

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Proteins are the basic building blocks of biological organisms, and are responsible for a variety of functions within them. Proteins are composed of unique amino acid sequences. Some has only one sequence, while others contain several sequences that are combined together. These combined amino acid sequences fold to form a unique three-dimensional (3D) shape. Although the sequences may fold proteins into different 3D shapes in diverse environments, proteins with similar amino acid sequences typically have similar 3D shapes and functions. Knowledge of the 3D shape of a protein is important in both protein function analysis and drug design, for example when assessing the toxicity reduction associated with a given drug. Due to the complexity of protein 3D shapes and the close relationship between shapes and functions, the prediction of protein 3D shapes has become an important topic in bioinformatics. This research introduces a new approach to predict proteins’ 3D shapes, utilizing a multilayer artificial neural network. Our novel solution allows one to learn and predict the representations of the 3D shape associated with a protein by starting directly from its amino acid sequence descriptors. The input of the artificial neural network is a set of amino acid sequence descriptors we created based on a set of probability density functions. In our algorithm, the probability density functions are calculated by the correlation between the constituent amino acids, according to the substitution matrix. The output layer of the network is formed by 3D shape descriptors provided by an information retrieval system, called CAPRI. This system contains the pose invariant 3D shape descriptors, and retrieves proteins having the closest structures. The network is trained by proteins with known amino acid sequences and 3D shapes. Once the network has been trained, it is able to predict the 3D shape descriptors of the query protein. Based on the predicted 3D shape descriptors, the CAPRI system allows the retrieval of known proteins with 3D shapes closest to the query protein. These retrieved proteins may be verified as to whether they are in the same family as the query protein, since proteins in the same family generally have similar 3D shapes. The search for similar 3D shapes is done against a database of more than 45,000 known proteins. We present the results when evaluating our approach against a number of protein families of various sizes. Further, we consider a number of different neural network architectures and optimization algorithms. When the neural network is trained with proteins that are from large families where the proteins in the same family have similar amino acid sequences, the accuracy for finding proteins from the same family is 100%. When we employ proteins whose family members have dissimilar amino acid sequences, or those from a small protein family, in which case, neural networks with one hidden layer produce more promising results than networks with two hidden layers, and the performance may be improved by increasing the number of hidden nodes when the networks have one hidden layer.

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Akkaladevi, Somasheker. "Decision Fusion for Protein Secondary Structure Prediction." Digital Archive @ GSU, 2006. http://digitalarchive.gsu.edu/cs_diss/9.

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Prediction of protein secondary structure from primary sequence of amino acids is a very challenging task, and the problem has been approached from several angles. Proteins have many different biological functions; they may act as enzymes or as building blocks (muscle fibers) or may have transport function (e.g., transport of oxygen). The three-dimensional protein structure determines the functional properties of the protein. A lot of interesting work has been done on this problem, and over the last 10 to 20 years the methods have gradually improved in accuracy. In this dissertation we investigate several techniques for predicting the protein secondary structure. The prediction is carried out mainly using pattern classification techniques such as neural networks, genetic algorithms, simulated annealing. Each individual algorithm may work well in certain situations but fails in others. Capitalizing on the positive decisions can be achieved by forcing the various methods to collaborate to reach a unified consensus based on their previous performances. The process of combining classifiers is called decision fusion. The various decision fusion techniques such as the committee method, correlation method and the Bayesian inference methods to fuse the solutions from various approaches and to get better prediction accuracy are thoroughly explored in this dissertation. The RS126 data set was used for training and testing purposes. The results of applying pattern classification algorithms along with decision fusion techniques showed improvement in the prediction accuracy compared to that of prediction by neural networks or pattern classification algorithms individually or combined with neural networks. This research has shown that decision fusion techniques can be used to obtain better protein secondary structure prediction accuracy.

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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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Nugent, T. C. O. "Transmembrane protein structure prediction using machine learning." Thesis, University College London (University of London), 2010. http://discovery.ucl.ac.uk/792008/.

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This thesis describes the development and application of machine learning-based methods for the prediction of alpha-helical transmembrane protein structure from sequence alone. It is divided into six chapters. Chapter 1 provides an introduction to membrane structure and dynamics, membrane protein classes and families, and membrane protein structure prediction. Chapter 2 describes a topological study of the transmembrane protein CLN3 using a consensus of bioinformatic approaches constrained by experimental data. Mutations in CLN3 can cause juvenile neuronal ceroid lipofuscinosis, or Batten disease, an inherited neurodegenerative lysosomal storage disease affecting children, therefore such studies are important for directing further experimental work into this incurable illness. Chapter 3 explores the possibility of using biologically meaningful signatures described as regular expressions to influence the assignment of inside and outside loop locations during transmembrane topology prediction. Using this approach, it was possilbe to modify a recent topology prediction method leading to an improvement of 6% prediction accuracy using a standard data set. Chapter 4 describes the development of a novel support vector machine-based topology predictor that integrates both signal peptide and re-entrant helix prediction, benchmarked with full cross-validation on a novel data set of sequences with known crystal structures. The method achieves state-of-the-art performance in predicting topology and discriminating between globular and transmembrane proteins. We also present the results of applying these tools to a number of complete genomes. Chapter 5 describes a novel approach to predict lipid exposure, residue contacts, helix-helix interactions and finally the optimal helical packing arrangement of transmembrane proteins. It is based on two support vector machine classifiers that predict per residue lipid exposure and residue contacts, which are used to determine helix-helix interaction with up to 65% accuracy. The method is also able to discriminate native from decoy helical packing arrangements with up to 70% accuracy. Finally, a force-directed algorithm is employed to construct the optimal helical packing arrangement which demonstrates success for proteins containing up to 13 transmembrane helices. The final chapter summarises the major contributions of this thesis to biology, before future perspectives for TM protein structure prediction are discussed.

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Sturrock, Shane Steven. "Improved tools for protein tertiary structure prediction." Thesis, University of Edinburgh, 1997. http://hdl.handle.net/1842/14506.

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The most successful method to date for predicting protein tertiary structure from primary sequence data is homology modelling based on alignment with similar sequences of known structure. The use of a variety of computing methods to identify the best similarities is discussed. Model building based on alignments and the construction of libraries of side chain conformations is described. The application of sequence alignment modelling to the structure prediction of EcoKI type I DNA methyltransferase is shown in the context of corroborative laboratory experiments. Finally, a method is presented which incorporates sequence alignment with secondary structure prediction. A program - sss_align - which incorporates this method, was used to make blind fold recognition predictions as part of an international collaborative exercise in the critical assessment of methods of protein structure prediction ('CASP2'). It was shown by this and other assessment methods that sss_align will detect similarities between sequences which exhibit as little as 15% identity.

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Pires, de Oliveira Saulo Henrique. "Biologically inspired de novo protein structure prediction." Thesis, University of Oxford, 2015. https://ora.ox.ac.uk/objects/uuid:cec2378e-2ae6-48c5-b735-f55aea0c59dc.

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Fragment-based approaches are the current standard for de novo protein structure prediction. These approaches rely on accurate and reliable fragment libraries to generate good structural models. We demonstrate that fragments presenting different predominant predicted secondary structures should be treated differently during fragment library generation. Using this information, we developed Flib and shown that it generates fragment libraries with higher precision and coverage than two other methods. We explored co-evolution to identify pairs of residues that are in contact, which were then used to improve model generation. We performed a comparative analysis of nine methods in terms of their precision and their usefulness to de novo structure prediction. Our results show that metaPSICOV stage 2 produces the most accurate predictions and that metaPSICOV stage 1 generates the best modelling results. In general, contact predictors are good at identifying contacts between β-strands and bad at identifying contacts between a-helices. We also show that the ratio of satisfied predicted contacts can be used to assess whether correct models were generated for a given target. We also investigated whether the biological process of cotranslational protein folding, the notion that proteins fold as they are being synthesized, can be used to improve de novo protein structure prediction. Our tool for this investigation is SAINT2. SAINT2 differs from conventional fragment-assembly approaches as it is able to perform predictions sequentially from N to C-terminus, starting with a small peptide that is extended as the simulation progresses (SAINT2 Cotranslational). SAINT2 is also able to generate decoys in a standard non-sequential fashion (SAINT2 In Vitro). We compared SAINT2 Cotranslational to SAINT2 In Vitro and shown that SAINT2 Cotranslational generally produces better answers, generating an individual decoy between 1.5 to 2.5 times faster than SAINT2 In Vitro. Our results suggest that biologically inspired structure prediction can improve search heuristics and final model quality.

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Zhang, Fan. "Improving protein structure prediction through data purification." Thesis, University of Surrey, 2007. http://epubs.surrey.ac.uk/844077/.

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In this thesis, the author pursues the target of improving accuracy of protein structural prediction through the procedure of data purification. A Protein Attributes Microtuning System (PAMS) is developed to prepare a variety of new datasets as and when required. Furthermore, a Protein Structural Accuracy Reckoner (PSAR) framework is used to recommend procedures that might lead to high prediction accuracy. By using the PSAR, it is shown that using a refined dataset generated by the PAMS, and implementing an appropriate window mechanism considerably improves the accuracy of protein structure prediction by 12%, giving a best accuracy of 90.97%. On average, almost all classifiers that are applied in the experiments result in accuracy increases of 10%-15%. A list of classifiers is categorized according to their prediction performances and classification efficiencies. A few refined datasets are proposed as benchmark datasets. Apart from the aforementioned achievements, examination of a total of 3,135,393 predictions tasks, which carried out by the PSAR framework, yielded 139 'best' and 73 'worst' combinations of amino acid features descriptors. In this analysis, the 'best' prediction gave 82.34%, and the 'worst' prediction gave 73.65%. To achieve a greater computational capacity the PSAR infrastructure is hosted on the Condor platform in the Department of Computing, University of Surrey.

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Bondugula, Rajkumar. "A novel framework for protein structure prediction." Diss., Columbia, Mo. : University of Missouri-Columbia, 2007. http://hdl.handle.net/10355/4855.

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Thesis (Ph.D.)--University of Missouri-Columbia, 2007.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on March 23, 2009) Vita. Includes bibliographical references.

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Ahmed, Mostafa H. "Hydropathic Interactions and Protein Structure: Utilizing the HINT Force Field in Structure Prediction and Protein‐Protein Docking." VCU Scholars Compass, 2014. http://scholarscompass.vcu.edu/etd/3581.

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Protein structure predication is a field of computational molecular modeling with an enormous potential for improvement. Side-chain geometry prediction is a critical component of this process that is crucial for computational protein structure predication as well as crystallographers in refining experimentally determined protein crystal structures. The cornerstone of side-chain geometry prediction are side-chain rotamer libraries, usually obtained through exhaustive statistical analysis of existing protein structures. Little is known, however, about the driving forces leading to the preference or suitability of one rotamer over another. Construction of 3D hydropathic interaction maps for nearly 30,000 tyrosines extracted from the PDB reveals their environments, in terms of hydrophobic and polar (collectively “hydropathic”) interactions. Using a unique 3D similarity metric, these environments were clustered with k-means. In the ϕ, ψ region (–200° < ϕ < –155°; –205° < ψ < –160°) representing 631 tyrosines, clustering reduced the set to 14 unique hydropathic environments, with most diversity arising from favorable hydrophobic interactions. Polar interactions for tyrosine include ubiquitous hydrogen bonding with the phenolic OH and a handful of unique environments surrounding the backbone. The memberships of all but one of the 14 environments are dominated by a single χ1/χ2 rotamer. Each tyrosine residue attempts to fulfill its hydropathic valence. Structural water molecules are thus used in a variety of roles throughout protein structure. A second project involves elucidating the 3D structure of CRIP1a, a cannabinoid 1 receptor (CB1R) binding protein that could provide information for designing small molecules targeting the CRIP1a-CB1R interaction. The CRIP1a protein was produced in high purity. Crystallization experiments failed, both with and without the last 9 or 12 amino acid peptide of the CB1R C-terminus. Attempts were made to use NMR for structure determination; however, the protein precipitated out during data acquisition. A model was thus built computationally to which the CB1R C-terminus peptide was docked. HINT was used in selecting optimum models and analyzing interactions involved in the CRIP1a-CB1R complex. The final model demonstrated key putative interactions between CRIP1a and CB1R while also predicting highly flexible areas of the CRIP1a possibly contributing to the difficulties faced during crystallization.

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CITROLO, ANDREA GAETANO. "Novel Computational Approaches for Protein Structure Prediction and Optimization." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2015. http://hdl.handle.net/10281/88167.

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La determinazione della struttura tridimensionale delle biomolecole su scala genomica è uno dei più importanti obbiettivi della biologia moderna con potenziali ricadute in differenti contesti applicativi che spaziano dalla farmacologia, alla biologia sintetica. Il ruolo dei metodi computazionali ed in particolare dei metodi di ottimizzazione in quest'ambito è fondamentale per l'interpretazione dei dati sperimentali. Inoltre nell'ultimo decennio la predizione computazionale della struttura di importanti classi di biomolecole come RNA e proteine è diventata una prospettiva concreta. Questa tesi presenta due nuovi metodi di ottimizzazione stocastica progettati rispettivamente per il problema della predizione della struttura delle proteine nel modello idrofobico polare e per il problema della ricostruzione della struttura da dati NMR. Il primo problema consiste nel trovare un assegnamento in un reticolo di una stringa binaria tale da minimizzare una data funzione di costo e senza violare un insieme di vincoli. Il secondo, consiste nel identificare una disposizione di atomi nello spazio tridimensionale che rispetti un insieme di vincoli di distanza. Entrambi questi problemi sono rilevanti dal punto di vista computazionale in quanto è stata dimostrata la NP-completezza del problema di decisione associato. Pertanto essi rappresentano un ottimo banco di prova per le euristiche di ottimizzazione stocastica. Nel caso della predizione della struttura nel modello idrofobico polare, i risultati ottenuti su una serie di istanze di benchmark mostrano che la strategia proposta può essere adattata a differenti modelli di rappresentazione migliorando in alcuni casi la performance rispetto allo stato dell'arte. Per quanto riguarda la ricostruzione di strutture da dati NMR, i risultati suggeriscono che il metodo proposto sia in grado di raggiungere un'accuratezza comparabile a quella dello stato dell'arte offrendo altresì numerosi vantaggi in termini di applicabilità rispetto agli approcci esistenti.
For many important classes of biomolecules such as RNA and proteins, a direct relationship exists between structure and function. On the contrary the relationships between genomic sequences and molecular structures are still poorly understood. The determination of the three dimensional structure of biomolecules on a genome-scale is hence one of the major challenges in modern biology. Indeed, today genomic data are easily achievable, thanks to next generation sequencing technology, while structural data are still obtained through complex experimental protocols. As a result, the disproportion between the available amount of genomic and structural data limits the progress in several fields such as drug discovery and synthetic biology. The use of computational methods and mathematical optimization in structural biology is fundamental to reduce the amount of data required from experiments speeding up experimental protocols and to define in silico protocols for the prediction of three dimensional structures. This thesis introduces novel heuristic approaches to tackle two important problems in structural biology: the protein structure prediction (PSP) and the molecular distance geometry (MDG) problem. %The MDG problem consists in reconstructing a three dimensional structure using a set of distance restraints obtained through a nuclear magnetic resonance (NMR) experiment. Both these problems are known to have a complex combinatorial structure and are classified as NP-hard. Therefore the proposed approaches are based on stochastic optimization heuristics (SOH), which provide a powerful framework to tackle complex combinatorial problems that do not allow for exact approaches. The PSP problem have been treated in the simplified representation provided by the hydrophobic polar (HP) model; a new perturbation strategy has been introduced to mimic off-lattice approaches and to provide a complementary benchmark to the existing move sets. Two heuristics, based on the principle of \emph{local landscape mapping}, have been tested on several benchmark instances both in combination with the new perturbation strategy and with standard move sets. The results show that one of the proposed heuristics outperforms state of the art methods on the majority of the considered instances. In the case of the MDG problem, results show that the proposed methodology is able to achieve a performance comparable to the state of the art and to overcome most limitations of the existing approaches.

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Tang, Thomas Cheuk Kai. "Discovering Protein Sequence-Structure Motifs and Two Applications to Structural Prediction." Thesis, University of Waterloo, 2004. http://hdl.handle.net/10012/1188.

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This thesis investigates the correlations between short protein peptide sequences and local tertiary structures. In particular, it introduces a novel algorithm for partitioning short protein segments into clusters of local sequence-structure motifs, and demonstrates that these motif clusters contain useful structural information via two applications to structural prediction. The first application utilizes motif clusters to predict local protein tertiary structures. A novel dynamic programming algorithm that performs comparably with some of the best existing algorithms is described. The second application exploits the capability of motif clusters in recognizing regular secondary structures to improve the performance of secondary structure prediction based on Support Vector Machines. Empirical results show significant improvement in overall prediction accuracy with no performance degradation in any specific aspect being measured. The encouraging results obtained illustrate the great potential of using local sequence-structure motifs to tackle protein structure predictions and possibly other important problems in computational biology.

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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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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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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.

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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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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.

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.

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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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Panaganti, Shilpa. "Parallel SVM with Application to Protein Structure Prediction." Digital Archive @ GSU, 2004. http://digitalarchive.gsu.edu/cs_theses/3.

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A learning task with thousands of training examples in Support Vector Machine (SVM) demands large amounts of memory and time requirements. SVMlight by Dr. Thorsten Joachims has been implemented in C using a fast optimizing algorithm for handling thousands of such support vectors. SVMlight solves the problem of classification, pattern recognition, regression and learning ranking function. The C code also provides methods for XiAlpha estimation of error rate and precision. Implementing these two methods leads to generalized performance of Support Vector Machine even for computation intensive text classification functions. SVMlight code allows users to define their own kernel functions. The SVMlight software employs an efficient algorithm and minimizes the cost, but it still takes considerable amount of time for computing thousands of support vectors and training examples. This time can be still reduced by parallelizing the code. In our work we refined the SVMlight code by removing unnecessary iterations and rewriting it as cost efficient. Then we parallelized the code individually using two different types, OpenMP and POSIX Threads shared memory parallelism. The code is parallelized for these two methods on Intel’s C compiler for Linux 7.1 using hyper threading technology. The parallelized code is tested for protein structure prediction. Different types of Protein Sequences are tested on these methods by varying the number of training examples and support vectors. The time consumption and speedup are calculated for both OpenMP and Pthreads. Implementation of OpenMP and Pthreads together showed good increase in speedup.

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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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Clark, Henry S. X. "Protein structure / function prediction within the twilight zone." Thesis, University of Essex, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.412342.

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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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Gweon, Hyun Soon. "Protein structure prediction : homology recognition and alignment refinement." Thesis, University of Cambridge, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.611864.

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Khatib, Firas. "Topological filters for use with protein structure prediction /." Diss., Digital Dissertations Database. Restricted to UC campuses, 2008. http://uclibs.org/PID/11984.

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45

Feng, Yaping. "New statistical potentials for improved protein structure prediction." [Ames, Iowa : Iowa State University], 2008.

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46

Higgs, Trent. "Protein Structure Prediction using Feature-Based Resampling Techniques." Thesis, Griffith University, 2013. http://hdl.handle.net/10072/365543.

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A protein is formed by a string of amino acids folding into a specic three-dimensional shape. Experimental approaches used to determine a protein's three-dimensional structure are time consuming and resource demanding. Therefore, computational methods that predict protein structure have been introduced. Computational protein structure predition (PSP) methods can be grouped into three main categories: comparative modelling, threading, and ab initio. Ab initio methods try to predict a protein's three-dimensional structure from its sequence alone. This is based on `Ansen's Thermodynamic Hypothesis' that a protein's native conformation is at its free-energy minimum. However, the biggest problem that the ab initio eld faces is that the free-energy landscape is very irregular and high-dimensional. To minimise this problem the concept of fragments was introduced to limit the number of conformations considered for a particular segment of the protein chain. Fragments can be applied to most PSP search techniques. A promising search method in PSP is the Genetic Algorithm (GA), as they allow for a generic search strategy. GAs provide a way of recombining good genetic traits from generation to generation, which allows them to easily be applied to feature-based resampling. Feature-based resampling focuses on taking an already sampled search space and recombining features from it, in order to improve the nal solution. In the literature feature-based resampling using GAs has been mentioned, primarily concerning the use of strings of torsion angles, and low-resolution PSP models to represent the protein structure. The biggest problem that can be seen from this is that torsion angles are unable to capture larger-scale elements of protein structure.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Information and Communication Technology
Science, Environment, Engineering and Technology
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47

Abbass, Jad. "Secondary structure-based template selection for fragment-assembly protein structure prediction." Thesis, Kingston University, 2018. http://eprints.kingston.ac.uk/42106/.

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Proteins play critical biochemical roles in all living organisms; in human beings, they are the targets of 50% of all drugs. Although the first protein structure was determined 60 years ago, experimental techniques are still time and cost consuming. Consequently, in silico protein structure prediction, which is considered a main challenge in computational biology, is fundamental to decipher conformations of protein targets. This thesis contributes to the state of the art of fragment-assembly protein structure prediction. This category has been widely and thoroughly studied due to its application to any type of targets. While the majority of research focuses on enhancing the functions that are used to score fragments by incorporating new terms and optimising their weights, another important issue is how to pick appropriate fragments from a large pool of candidate structures. Since prediction of the main structural classes, i.e. mainly-alpha, mainly-beta and alpha-beta, has recently reached quite a high level of accuracy, we have introduced a novel approach by decreasing the size of the pool of candidate structures to comprise only proteins that share the same structural class a target is likely to adopt. Picking fragments from this customised set of known structures not only has contributed in generating decoys with higher level of accuracy but also has eliminated irrelevant parts of the search space which makes the selection of first models a less complicated process, addressing the inaccuracies of energy functions. In addition to the challenge of adopting a unique template structure for all targets, another one arises whenever relying on the same amount of corrections and fine tunings; such a phase may be damaging to "easy' targets, i.e. those that comprise a relatively significant percentage of alpha helices. Owing to the sequence-structure correlation based on which fragment-based protein structure prediction was born, we have also proposed a customised phase of correction based on the structural class prediction of the target in question. After using secondary structure prediction as a "global feature" of a target, i.e. structural classes, we have also investigated its usage as a "local feature" to customise the number of candidate fragments, which is currently the same at all positions. Relying on the known facts regarding diversity of short fragments of helices, sheets and loops, the fragment insertion process has been adjusted to make "changes" relative to the expected complexity of each region. We have proved in this thesis the extent to which secondary structure features can be used implicitly or explicitly to enhance fragment assembly protein structure prediction.

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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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Kountouris, Petros. "Prediction of local protein structure using machine learning techniques." Thesis, University of Nottingham, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.546251.

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Dissertations / Theses on the topic 'Protein structure prediction'

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