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Dissertations / Theses on the topic 'Prediction of secondary structure of proteins'

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Published: 4 June 2021
Last updated: 14 February 2022

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1

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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2

Imai, Kenichiro, and Shigeki Mitaku. "Mechanisms of Secondary Structure Breakers in Soluble Proteins." THE BIOPHYSICAL SOCIETY OF JAPAN, 2005. http://hdl.handle.net/2237/9269.

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3

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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4

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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5

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 investi
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6

Tsilo, Lipontseng Cecilia. "Protein secondary structure prediction using neural networks and support vector machines." Thesis, Rhodes University, 2009. http://hdl.handle.net/10962/d1002809.

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Predicting the secondary structure of proteins is important in biochemistry because the 3D structure can be determined from the local folds that are found in secondary structures. Moreover, knowing the tertiary structure of proteins can assist in determining their functions. The objective of this thesis is to compare the performance of Neural Networks (NN) and Support Vector Machines (SVM) in predicting the secondary structure of 62 globular proteins from their primary sequence. For each NN and SVM, we created six binary classifiers to distinguish between the classes’ helices (H) strand (E), a
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7

Soni, Ravi. "Computer-aided modeling and simulation of molecular systems and protein secondary structure prediction." Ohio : Ohio University, 1993. http://www.ohiolink.edu/etd/view.cgi?ohiou1176235817.

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8

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 applicati
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9

Islam, Md Nasrul. "A Balanced Secondary Structure Predictor." ScholarWorks@UNO, 2015. http://scholarworks.uno.edu/td/1995.

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Secondary structure (SS) refers to the local spatial organization of the polypeptide backbone atoms of a protein. Accurate prediction of SS is a vital clue to resolve the 3D structure of protein. SS has three different components- helix (H), beta (E) and coil (C). Most SS predictors are imbalanced as their accuracy in predicting helix and coil are high, however significantly low in the beta. The objective of this thesis is to develop a balanced SS predictor which achieves good accuracies in all three SS components. We proposed a novel approach to solve this problem by combining a genetic algor
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10

Reyaz-Ahmed, Anjum B. "Protein Secondary Structure Prediction Using Support Vector Machines, Nueral Networks and Genetic Algorithms." Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/cs_theses/43.

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Bioinformatics techniques to protein secondary structure prediction mostly depend on the information available in amino acid sequence. Support vector machines (SVM) have shown strong generalization ability in a number of application areas, including protein structure prediction. In this study, a new sliding window scheme is introduced with multiple windows to form the protein data for training and testing SVM. Orthogonal encoding scheme coupled with BLOSUM62 matrix is used to make the prediction. First the prediction of binary classifiers using multiple windows is compared with single window
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11

Brown, Nigel P. "Patterns in protein secondary structure packing : a database for prediction." Thesis, University College London (University of London), 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.315268.

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12

Hayward, Steven John. "Studies in protein secondary structure prediction with neural network models." Thesis, University of Edinburgh, 1991. http://hdl.handle.net/1842/14034.

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The aim of this work was to predict protein secondary structure using neural network models. Initially a Hopfield network was used but abandoned in favour of a layered network trained using the back propagation algorithm. In the early stages of this work an exploration of the many different approaches to this problem was undertaken. These included attempts to predict boundaries between secondary structures, the secondary structures of individual residues, and the secondary structures of sequences wholly within a particular secondary structure. Results indicated the latter to be the best approa
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13

Eldud, Omer Ahmed Abdelkarim. "Prediction of protein secondary structure using binary classificationtrees, naive Bayes classifiers and the Logistic Regression Classifier." Thesis, Rhodes University, 2016. http://hdl.handle.net/10962/d1019985.

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The secondary structure of proteins is predicted using various binary classifiers. The data are adopted from the RS126 database. The original data consists of protein primary and secondary structure sequences. The original data is encoded using alphabetic letters. These data are encoded into unary vectors comprising ones and zeros only. Different binary classifiers, namely the naive Bayes, logistic regression and classification trees using hold-out and 5-fold cross validation are trained using the encoded data. For each of the classifiers three classification tasks are considered, namely helix
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14

Gerloff, Dietlind L. Gerloff Dietlind H. "A new method for protein secondary structure prediction using evolutionary information /." [S.l.] : [s.n.], 1994. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=10909.

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15

Cumbaa, Christian. "Modeling Protein Secondary Structure by Products of Dependent Experts." Thesis, University of Waterloo, 2001. http://hdl.handle.net/10012/1045.

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A phenomenon as complex as protein folding requires a complex model to approximate it. This thesis presents a bottom-up approach for building complex probabilistic models of protein secondary structure by incorporating the multiple information sources which we call experts. Expert opinions are represented by probability distributions over the set of possible structures. Bayesian treatment of a group of experts results in a consensus opinion that combines the experts' probability distributions using the operators of normalized product, quotient and exponentiation. The expression of t
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16

PORFIRIO, DAVID JONATHAN. "SINGLE-SEQUENCE PROTEIN SECONDARY STRUCTURE PREDICTION BY NEAREST-NEIGHBOR CLASSIFICATION OF PROTEIN WORDS." Thesis, The University of Arizona, 2016. http://hdl.handle.net/10150/613449.

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Predicting protein secondary structure is the process by which, given a sequence of amino acids as input, the secondary structure class of each position in the sequence is predicted. Our approach is built on the extraction of protein words of a fixed length from protein sequences, followed by nearest-neighbor classification in order to predict the secondary structure class of the middle position in each word. We present a new formulation for learning a distance function on protein words based on position-dependent substitution scores on amino acids. These substitution scores are learned
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17

Aydin, Zafer. "Bayesian models and algoritms for protein secondary structure and beta-sheet prediction." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/26471.

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Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2009.<br>Committee Chair: Yucel Altunbasak; Committee Co-Chair: Mark Borodovsky; Committee Member: Brani Vidakovic; Committee Member: Ghassan Alregib; Committee Member: James McClellan; Committee Member: Russel Mersereau. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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18

Korff, Matti Gerrit [Verfasser]. "Protein Secondary Structure Prediction Using a Vector Valued Classifier / Matti Gerrit Korff." Berlin : Freie Universität Berlin, 2016. http://d-nb.info/1081935456/34.

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19

Hering, Joachim A. "Protein secondary structure prediction using artificial intelligence techniques based on spectral data." Thesis, De Montfort University, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.398270.

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20

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 describe
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21

Hyrš, Martin. "Predikce vlivu aminokyselinových mutací na sekundární strukturu proteinů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-236396.

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In this thesis I investigate the effect of amino acid substitutions on secondary structure of proteins. I found that the secondary structure is relatively resistant to mutations, some regions hold the same secondary structure, even though their sequences are very different. Since this effect was observed also for random sequences, I conclude that it is a general property of the amino acid sequence. The particular elements of secondary structures are differentially sensitive to the changes caused by mutations. Protein's sensitivity to mutations depends on the composition of its secondary struct
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22

Alistair, Chalk. "PREDICTION OF PROTEIN SECONDARY STRUCTURE by Incorporating Biophysical Information into Artificial Neural Networks." Thesis, University of Skövde, Department of Computer Science, 1998. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-235.

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<p>This project applied artificial neural networks to the field of secondary structure prediction of proteins. A NETtalk architecture with a window size 13 was used. Over-fitting was avoided by the use of 3 real numbers to represent amino acids, reducing the number of adjustable weights to 840. Two alternative representations of amino acids that incorporated biophysical data were created and tested. They were tested both separately and in combination on a standard 7-fold cross-validation set of 126 proteins. The best performance was achieved using an average result from two predictions. This w
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23

Tran, Thuong Van Du. "Modeling and predicting super-secondary structures of transmembrane beta-barrel proteins." Phd thesis, Ecole Polytechnique X, 2011. http://tel.archives-ouvertes.fr/tel-00647947.

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Les protéines transmembranaires canaux-β (TMBs) se trouvent dans les membranes externes des bactéries à Gram négatif, des mitochondries ainsi que des chloroplastes. Elles traversent entièrement la membrane cellulaire et exercent différentes fonctions importantes. Vu qu'il y a un petit nombre des structures des TMBs déterminées, en raison des difficultés avec les méthodes expérimentales, il est douteux que ces approches puis- sent bien trouver et prédire les TMBs qui ne sont pas homologues avec celles connues. Nous construisons un modèle de graphe pour la classification et la prédiction de stru
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24

Käll, Lukas. "Predicting transmembrane topology and signal peptides with hidden Markov models /." Stockholm, 2006. http://diss.kib.ki.se/2006/91-7140-719-7/.

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25

Clayton, Arnshea. "The Relative Importance of Input Encoding and Learning Methodology on Protein Secondary Structure Prediction." Digital Archive @ GSU, 2006. http://digitalarchive.gsu.edu/cs_theses/19.

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In this thesis the relative importance of input encoding and learning algorithm on protein secondary structure prediction is explored. A novel input encoding, based on multidimensional scaling applied to a recently published amino acid substitution matrix, is developed and shown to be superior to an arbitrary input encoding. Both decimal valued and binary input encodings are compared. Two neural network learning algorithms, Resilient Propagation and Learning Vector Quantization, which have not previously been applied to the problem of protein secondary structure prediction, are examined. Input
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26

Bettella, Francesco [Verfasser]. "Protein secondary structure prediction using optimized scoring functions : a comparative statistical method / Francesco Bettella." Berlin : Freie Universität Berlin, 2009. http://d-nb.info/1023817845/34.

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27

Imai, Kenichiro, 賢一郎 今井, Naoyuki Asakawa, et al. "Secondary structure breakers and hairpin structures in myoglobin and hemoglobin." Chem-Bio Informatics Society, 2005. http://hdl.handle.net/2237/9271.

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28

Orgah, Augustine Ada. "Toward a Database of Geometric Interrelationships of Protein Secondary Structure Elements for De Novo Protein Design, Prediction and Analysis." ScholarWorks@UNO, 2010. http://scholarworks.uno.edu/td/100.

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Computational methods of analyzing, simulating, and modeling proteins are essential towards understanding protein structure and its interactions. Computational methods are easier as not all protein structures can be determined experimentally due to the inherent difficultly of working with some proteins. In order to predict, design, analyze, simulate or model a protein, data from experimentally determined proteins such as those located in the repository of the Protein Data Bank (PDB) are essential. The assumption here is that we can use pieces of known proteins to piece together a "new" protein
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29

Villem, Lukáš. "Webový server pro predikci sekundární struktury proteinů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-236159.

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This master&#8217;s thesis deals with protein secondary structure prediction. There is a theoretical introduction followed by study of available tools, proposal and implementation of web application, which combines functionality of several web tools used to predict secondary structure. User is asked to choose prediction methods and insert input sequence as plain text or upload a file. Results collected from selected tools serve to convert data into common format, show the result and create new type of prediction. Finally, the testing is applied and influences of tools are adjusted in order to
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30

Cheng, Haitao. "Protein structure prediction and conformational transitions I. Improvement of protein secondary structure prediction : II. Pathways of conformational transition originating in phosphorylation : a study of CDK2 using targeted molecular dynamics and coarse grained models /." [Ames, Iowa : Iowa State University], 2009. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3360333.

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31

Brigant, Vladimír. "Predikce sekundární struktury proteinů pomocí celulárních automatů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-236230.

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This work describes a method of the secondary structure prediction of proteins based on cellular automaton (CA) model - CASSP. Optimal model and CA transition rule parameters are acquired by evolutionary algorithm. Prediction model uses only statistical characteristics of amino acids, so its prediction is fast. Achieved results was compared with results of other tools for this purpose. Prediction cooperation with a existing tool PSIPRED was also tested. It didn't succeed to beat this existing tool, but partial improvement was achieved in prediction of only alpha-helix secondary structure motif
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32

WEBER, M. ELISABETH. "Transporteurs de pyrimidines chez saccharomyces cerevisiae : sequence de deux genes et prediction de structure des proteines correspondantes." Université Louis Pasteur (Strasbourg) (1971-2008), 1987. http://www.theses.fr/1987STR13197.

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33

Wilman, Henry R. "Computational studies of protein helix kinks." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:21225f0e-efed-49c6-af27-5d3fe78fa731.

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Kinks are functionally important structural features found in the alpha-helices of many proteins, particularly membrane proteins. Structurally, they are points at which a helix abruptly changes direction. Previous kink definition and identification methods often disagree with one another. Here I describe three novel methods to characterise kinks, which improve on existing approaches. First, Kink Finder, a computational method that consistently locates kinks and estimates the error in the kink angle. Second the B statistic, a statistically robust method for identifying kinks. Third, Alpha Helic
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34

Mathuriya, Amrita. "Prediction of secondary structures for large RNA molecules." Thesis, Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/28195.

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Thesis (M. S.)--Computing, Georgia Institute of Technology, 2009.<br>Committee Chair: Bader, David; Committee Co-Chair: Heitsch, Christine; Committee Member: Harvey, Stephen; Committee Member: Vuduc, Richard.
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35

Hearnshaw, Stephen J. "Multicofactor proteins : structure, prediction, function." Thesis, University of East Anglia, 2011. https://ueaeprints.uea.ac.uk/32110/.

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36

Lazar, Iustin. "A multi-level nearest-neighbour algorithm for predicting protein secondary structure." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ39987.pdf.

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37

Lindström, Anton. "A multivariate approach to characterization of drug-like molecules, proteins and the interactions between them." Doctoral thesis, Umeå universitet, Kemi, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-1924.

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En sjukdom kan många gånger härledas till en kaskadereaktion mellan proteiner, co-faktorer och substrat. Denna kaskadreaktion blir många gånger målet för att behandla sjukdomen med läkemedel. För att designa nya läkemedelsmoleyler används vanligen datorbaserade verktyg. Denna design av läkemedelsmolekyler drar stor nytta av att målproteinet är känt och då framförallt dess tredimensionella (3D) struktur. Är 3D-strukturen känd kan man utföra så kallad struktur- och datorbaserad molekyldesign, 3D-geometrin (f.f.a. för inbindningsplatsen) blir en vägledning för designen av en ny molekyl. Många fak
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38

Yang, Qian 1973. "RNA sequence alignment and secondary structure prediction." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=82453.

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Functional RNA sequences typically have structural elements that are highly conserved during evolution. Here we present an algorithmic method for multiple alignment of RNAs, taking into consideration both structural similarity and sequence identity. Furthermore, we performed a comparative analysis on pairing probability matrices of a set of aligned orthologous sequences and predicted the conserved secondary structure. Our alignment method outperforms the most widely used multiple alignment tool - Clustal W, and the structure prediction approach we proposed can generate a more accurate s
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39

Hosna, Hosna. "RNA secondary structure prediction using hierarchical folding." Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/32001.

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Algorithms for prediction of RNA secondary structure— the set of base pairs that form when an RNA molecule folds— are valuable to biologists who aim to understand RNA structure and function. Improving the accuracy and efficiency of prediction methods'is an ongoing challenge, particularly for pseudoknotted secondary structures, in which base pairs overlap. This challenge is biologically important, since pseudoknotted structures play essential roles in functions of many RNA molecules, such as splicing and ribosomal frameshifting. State-of-the- art methods, which are based on free energy minimiz
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40

Fu, Y.-X. "Statistical theory of change points with application to the prediction of protein secondary structures." Thesis, University of Reading, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.383409.

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41

Signorell, Gian Andrea. "Structure prediction of proteins using nearest neighbor trees /." Zürich : ETH, Eidgenössische Technische Hochschule Zürich, [Departement für Informatik, Institut für Wissenschaftliches Rechnen], 2003. http://e-collection.ethbib.ethz.ch/show?type=dipl&nr=118.

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42

Han, Dianwei. "COMPUTER METHODS FOR PRE-MICRORNA SECONDARY STRUCTURE PREDICTION." UKnowledge, 2012. http://uknowledge.uky.edu/cs_etds/7.

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This thesis presents a new algorithm to predict the pre-microRNA secondary structure. An accurate prediction of the pre-microRNA secondary structure is important in miRNA informatics. Based on a recently proposed model, nucleotide cyclic motifs (NCM), to predict RNA secondary structure, we propose and implement a Modified NCM (MNCM) model with a physics-based scoring strategy to tackle the problem of pre-microRNA folding. Our microRNAfold is implemented using a global optimal algorithm based on the bottom-up local optimal solutions. It has been shown that studying the functions of multiple gen
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43

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.<br>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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44

Saaidi, Afaf. "Multi-dimensional probing for RNA secondary structure(s) prediction." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLX067/document.

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En bioinformatique structurale, la prédiction de la (des) structure(s) secondaire(s) des acides ribonucléiques (ARNs) constitue une direction de recherche majeure pour comprendre les mécanismes cellulaires. Une approche classique pour la prédiction de la structure postule qu'à l'équilibre thermodynamique, l'ARN adopte plusieurs conformations, caractérisées par leur énergie libre, dans l’ensemble de Boltzmann. Les approches modernes privilégient donc une considération des conformations dominantes. Ces approches voient leur précision limitées par l'imprécision des modèles d'énergie et les restri
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45

Minor, Daniel Louis. "Tertiary packing effects on secondary structure formation in proteins." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/39379.

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46

Romanet, Patrick. "Prédiction de la structure secondaire des protéines par analyse spectrale de la séquence d'hydrophobicité." Grenoble 1, 1999. http://www.theses.fr/1999GRE10139.

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Ce travail presente trois methodes de prediction de la structure secondaire des proteines par analyse spectrale de la sequence d'hydrophobicite. Une etude comparative preliminaire sur 41 echelles d'hydrophobicite ainsi que sur differentes methodes d'analyse spectrale classiques et parametriques a ete menee pour determiner l'echelle optimale et la methode spectrale la mieux adaptee a chaque type de prediction. Les parametres des methodes de prediction ont ete evalues a partir d'une base de donnees, comportant 1028 chaines peptidiques ayant moins de 25% d'homologie (223157 residus). Les trois me
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47

Ranganathan, Sushilee. "Automated and accurate description of protein structure -- from secondary to tertiary structure." Scholarly Commons, 2008. https://scholarlycommons.pacific.edu/uop_etds/707.

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The automated protein structure analysis (APSA) has been developed that describes protein structure via its backbone in a novel way. APSA generates a smooth line for the backbone which is completely described using curvature κ and torsion τ as a function of arc lengths. Diagrams of κ(s) and τ(s) reveal conformational features as typical patterns. In this way ideal and natural helices (α, 310 and π) and β-strands (left and right-handed, parallel and antiparallel) can be rapidly distinguished, their distortions classified, and a detailed picture of secondary structure developed. Such foundations
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48

Pereira, José Geraldo de Carvalho. "Redes neurais residuais profundas e autômatos celulares como modelos para predição que fornecem informação sobre a formação de estruturas secundárias proteicas." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/95/95131/tde-03052018-095932/.

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O processo de auto-organização da estrutura proteica a partir da cadeia de aminoácidos é conhecido como enovelamento. Apesar de conhecermos a estrutura tridimencional de muitas proteínas, para a maioria delas, não possuímos uma compreensão suficiente para descrever em detalhes como a estrutura se organiza a partir da sequência de aminoácidos. É bem conhecido que a formação de núcleos de estruturas locais, conhecida como estrutura secundária, apresenta papel fundamental no enovelamento final da proteína. Desta forma, o desenvolvimento de métodos que permitam não somente predizer a estrutura sec
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49

Granseth, Erik. "Structure, prediction, evolution and genome wide studies of membrane proteins." Doctoral thesis, Stockholm : Department of Biochemistry and Biophysics, Stockholm University, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-7027.

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50

Bamborough, Paul. "Theoretical structure prediction applied to proteins of the immune system." Thesis, University of Oxford, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.240650.

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