Literatura académica sobre el tema "Protein Structure Models"
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Artículos de revistas sobre el tema "Protein Structure Models"
Mucsi, Z., Z. Gaspari, G. Orosz y A. Perczel. "Structure-oriented rational design of chymotrypsin inhibitor models". Protein Engineering Design and Selection 16, n.º 9 (1 de septiembre de 2003): 673–81. http://dx.doi.org/10.1093/protein/gzg090.
Texto completoKim, HyungRae. "Residue Environment Score for Selecting Protein Structure Models and Protein-Protein Docking Models". Biophysical Journal 110, n.º 3 (febrero de 2016): 346a. http://dx.doi.org/10.1016/j.bpj.2015.11.1862.
Texto completoKoliński, A. y J. Skolnick. "High coordination lattice models of protein structure, dynamics and thermodynamics." Acta Biochimica Polonica 44, n.º 3 (30 de septiembre de 1997): 389–422. http://dx.doi.org/10.18388/abp.1997_4393.
Texto completoYang, Yifeng David, Preston Spratt, Hao Chen, Changsoon Park y Daisuke Kihara. "Sub-AQUA: real-value quality assessment of protein structure models". Protein Engineering, Design and Selection 23, n.º 8 (4 de junio de 2010): 617–32. http://dx.doi.org/10.1093/protein/gzq030.
Texto completoKihara, Daisuke, Hao Chen y Yifeng Yang. "Quality Assessment of Protein Structure Models". Current Protein & Peptide Science 10, n.º 3 (1 de junio de 2009): 216–28. http://dx.doi.org/10.2174/138920309788452173.
Texto completoHirsch, M. y M. Habeck. "Mixture models for protein structure ensembles". Bioinformatics 24, n.º 19 (28 de julio de 2008): 2184–92. http://dx.doi.org/10.1093/bioinformatics/btn396.
Texto completoGhosh, Soma y Saraswathi Vishveshwara. "Ranking the quality of protein structure models using sidechain based network properties". F1000Research 3 (21 de enero de 2014): 17. http://dx.doi.org/10.12688/f1000research.3-17.v1.
Texto completoDomingues, F. S., J. Rahnenfuhrer y T. Lengauer. "Automated clustering of ensembles of alternative models in protein structure databases". Protein Engineering Design and Selection 17, n.º 6 (3 de agosto de 2004): 537–43. http://dx.doi.org/10.1093/protein/gzh063.
Texto completoShatabda, Swakkhar, M. A. Hakim Newton, Mahmood A. Rashid, Duc Nghia Pham y Abdul Sattar. "How Good Are Simplified Models for Protein Structure Prediction?" Advances in Bioinformatics 2014 (29 de abril de 2014): 1–9. http://dx.doi.org/10.1155/2014/867179.
Texto completoBienkowska, J., Hongxian He y T. F. Smith. "Automatic pattern embedding in protein structure models". IEEE Intelligent Systems 16, n.º 6 (noviembre de 2001): 21–25. http://dx.doi.org/10.1109/5254.972074.
Texto completoTesis sobre el tema "Protein Structure Models"
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.
Texto completoGriffiths-Jones, Samuel R. "Peptide models for protein beta-sheets". Thesis, University of Nottingham, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364650.
Texto completoWróblewska, Liliana. "Refinement of reduced protein models with all-atom force fields". Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/26606.
Texto completoGamalielsson, 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.
Texto completoEvolutionary 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.
Käll, Lukas. "Predicting transmembrane topology and signal peptides with hidden Markov models /". Stockholm, 2006. http://diss.kib.ki.se/2006/91-7140-719-7/.
Texto completoTångrot, 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.
Texto completoBioinformatik 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.
Pettitt, Christopher Steven. "Refinement of protein structure models with multi-objective genetic algorithms". Thesis, University College London (University of London), 2007. http://discovery.ucl.ac.uk/1446043/.
Texto completoHayward, Steven John. "Studies in protein secondary structure prediction with neural network models". Thesis, University of Edinburgh, 1991. http://hdl.handle.net/1842/14034.
Texto completoGregor, Craig Robert. "Epitopes, aggregation and membrane binding : investigating the protein structure-function relationship". Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/5833.
Texto completoChippington-Derrick, T. C. "Models, methods and algorithms for constraint dynamics simulations of long chain molecules". Thesis, University of Reading, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.234776.
Texto completoLibros sobre el tema "Protein Structure Models"
Protein structure prediction. 3a ed. New York: Humana Press, 2014.
Buscar texto completoHenrik, Bohr y Brunak S[0]ren, eds. Protein structure by distance analysis. Amsterdam: IOS Press, 1994.
Buscar texto completoD, Fasman Gerald, ed. Prediction of protein structure and the principles of protein conformation. New York: Plenum Press, 1989.
Buscar texto completoD, Fasman Gerald, ed. Prediction of protein structure and the principles of protein confirmation. New York: Plenum, 1989.
Buscar texto completo1964-, Liang Jie, Xu Ying 1960- y Xu Dong 1965-, eds. Computational methods for protein structure prediction and modeling. New York, N.Y: Springer, 2007.
Buscar texto completoZimmermann, Karl-Heinz. An introduction to protein informatics. Boston: Kluwer Academic Publishers, 2003.
Buscar texto completoZimmermann, Karl-Heinz. An introduction to protein informatics. Boston: Kluwer Academic Publishers, 2003.
Buscar texto completoRangwala, Huzefa, G. Karypis y G. Karypis. Introduction to protein structure prediction: Methods and algorithms. Hoboken, N.J: Wiley, 2010.
Buscar texto completoRigden, Daniel John. From protein structure to function with bioinformatics. [Dordrecht]: Springer, 2009.
Buscar texto completoZimmermann, Karl-Heinz. An introduction to protein informatics. Dordrecht: Springer-Science+Business Media, B.V., 2003.
Buscar texto completoCapítulos de libros sobre el tema "Protein Structure Models"
Bystroff, Christopher y Anders Krogh. "Hidden Markov Models for Prediction of Protein Features". En Protein Structure Prediction, 173–98. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-59745-574-9_7.
Texto completoMasso, Majid y Iosif I. Vaisman. "Structure-Based Machine Learning Models for Computational Mutagenesis". En Introduction to Protein Structure Prediction, 403–30. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470882207.ch18.
Texto completoTautermann, Christofer S. "Target Based Virtual Screening by Docking into Automatically Generated GPCR Models". En Membrane Protein Structure and Dynamics, 255–70. Totowa, NJ: Humana Press, 2012. http://dx.doi.org/10.1007/978-1-62703-023-6_15.
Texto completoKihara, Daisuke, Yifeng David Yang y Hao Chen. "Error Estimation of Template-Based Protein Structure Models". En Multiscale Approaches to Protein Modeling, 295–314. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-6889-0_13.
Texto completoFrauenfelder, Hans. "Heme Protein Reactions: Models, Concepts, and Problems". En Structure, Dynamics and Function of Biomolecules, 10–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-71705-5_3.
Texto completoIsin, Basak, Kalyan C. Tirupula, Zoltán N. Oltvai, Judith Klein-Seetharaman y Ivet Bahar. "Identification of Motions in Membrane Proteins by Elastic Network Models and Their Experimental Validation". En Membrane Protein Structure and Dynamics, 285–317. Totowa, NJ: Humana Press, 2012. http://dx.doi.org/10.1007/978-1-62703-023-6_17.
Texto completoRigden, Daniel J., Iwona A. Cymerman y Janusz M. Bujnicki. "Prediction of Protein Function from Theoretical Models". En From Protein Structure to Function with Bioinformatics, 467–98. Dordrecht: Springer Netherlands, 2017. http://dx.doi.org/10.1007/978-94-024-1069-3_15.
Texto completoBlaszczyk, Maciej, Dominik Gront, Sebastian Kmiecik, Mateusz Kurcinski, Michal Kolinski, Maciej Pawel Ciemny, Katarzyna Ziolkowska, Marta Panek y Andrzej Kolinski. "Protein Structure Prediction Using Coarse-Grained Models". En Springer Series on Bio- and Neurosystems, 27–59. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95843-9_2.
Texto completoHansen-Goos, Hendrik y Seth Lichter. "Geometric Models of Protein Secondary-Structure Formation". En Nano and Cell Mechanics, 411–35. Chichester, UK: John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781118482568.ch16.
Texto completoBlaszczyk, Maciej, Dominik Gront, Sebastian Kmiecik, Katarzyna Ziolkowska, Marta Panek y Andrzej Kolinski. "Coarse-Grained Protein Models in Structure Prediction". En Computational Methods to Study the Structure and Dynamics of Biomolecules and Biomolecular Processes, 25–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-28554-7_2.
Texto completoActas de conferencias sobre el tema "Protein Structure Models"
Winter, Pawel y Rasmus Fonseca. "Alpha Complexes in Protein Structure Prediction". En International Conference on Bioinformatics Models, Methods and Algorithms. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005251401780182.
Texto completo"DIFFERENTIAL EVOLUTION TO MULTI-OBJECTIVE PROTEIN STRUCTURE PREDICTION". En International Conference on Bioinformatics Models, Methods and Algorithms. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0003767002950298.
Texto completo"STUDY OF PROTEIN STRUCTURE ALIGNMENT PROBLEM IN PARAMETERIZED COMPUTATION". En International Conference on Bioinformatics Models, Methods and Algorithms. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0003769701740181.
Texto completo"A Hybrid Local Search for Simplified Protein Structure Prediction". En International Conference on Bioinformatics Models, Methods and Algorithms. SciTePress - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004239401580163.
Texto completoRAIMONDO, DOMENICO, ALEJANDRO GIORGETTI, DOMENICO COZZETTO y ANNA TRAMONTANO. "QUALITY AND EFFECTIVENESS OF PROTEIN STRUCTURE COMPARATIVE MODELS". En Proceedings of the International Symposium on Mathematical and Computational Biology. WORLD SCIENTIFIC, 2006. http://dx.doi.org/10.1142/9789812773685_0017.
Texto completoArora, Bhumika. "Refinement of G protein-coupled receptor structure models". En BCB '20: 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3388440.3414920.
Texto completoArikawa, Keisuke. "Analyzing Motion Properties of Proteins Affected by Localized Structures From a Robot Kinematics Perspective". En ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-47010.
Texto completo"BENEFITS OF GENETIC ALGORITHM FEATURE-BASED RESAMPLING FOR PROTEIN STRUCTURE PREDICTION". En International Conference on Bioinformatics Models, Methods and Algorithms. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0003770801880194.
Texto completoChu, Jia-Han, Chun Yuan Lin, Cheng-Wen Chang, Chihan Lee, Yuh-Shyong Yang, Chuan Yi Tang, Tuan D. Pham y Xiaobo Zhou. "TIM Barrel Protein Structure Classification Using Alignment Approach and Best Hit Strategy". En COMPUTATIONAL MODELS FOR LIFE SCIENCES/CMLS '07. AIP, 2007. http://dx.doi.org/10.1063/1.2816621.
Texto completoAntczak, Piotr Lukasiak Maciej, Tomasz Ratajczak, Piotr Lukasiak y Jacek Blazewicz. "SphereGrinder - reference structure-based tool for quality assessment of protein structural models". En 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2015. http://dx.doi.org/10.1109/bibm.2015.7359765.
Texto completoInformes sobre el tema "Protein Structure Models"
Hart, W. E. y S. Istrail. Lattice and off-lattice side chain models of protein folding: Linear time structure prediction better than 86% of optimal. Office of Scientific and Technical Information (OSTI), agosto de 1996. http://dx.doi.org/10.2172/425317.
Texto completoToda, Magdalena y Bhagya Athukorallage. Geometric Models for Secondary Structures in Proteins. GIQ, 2015. http://dx.doi.org/10.7546/giq-16-2015-282-300.
Texto completoChristopher, David A. y Avihai Danon. Plant Adaptation to Light Stress: Genetic Regulatory Mechanisms. United States Department of Agriculture, mayo de 2004. http://dx.doi.org/10.32747/2004.7586534.bard.
Texto completoElbaum, Michael y Peter J. Christie. Type IV Secretion System of Agrobacterium tumefaciens: Components and Structures. United States Department of Agriculture, marzo de 2013. http://dx.doi.org/10.32747/2013.7699848.bard.
Texto completoOhad, Nir y Robert Fischer. Regulation of Fertilization-Independent Endosperm Development by Polycomb Proteins. United States Department of Agriculture, enero de 2004. http://dx.doi.org/10.32747/2004.7695869.bard.
Texto completoKirchhoff, Helmut y Ziv Reich. Protection of the photosynthetic apparatus during desiccation in resurrection plants. United States Department of Agriculture, febrero de 2014. http://dx.doi.org/10.32747/2014.7699861.bard.
Texto completoNelson, Nathan y Charles F. Yocum. Structure, Function and Utilization of Plant Photosynthetic Reaction Centers. United States Department of Agriculture, septiembre de 2012. http://dx.doi.org/10.32747/2012.7699846.bard.
Texto completoEpel, Bernard y Roger Beachy. Mechanisms of intra- and intercellular targeting and movement of tobacco mosaic virus. United States Department of Agriculture, noviembre de 2005. http://dx.doi.org/10.32747/2005.7695874.bard.
Texto completoChamovitz, Daniel A. y Albrecht G. Von Arnim. eIF3 Complexes and the eIF3e Subunit in Arabidopsis Development and Translation Initiation. United States Department of Agriculture, septiembre de 2009. http://dx.doi.org/10.32747/2009.7696545.bard.
Texto completoChen, Junping, Zach Adam y Arie Admon. The Role of FtsH11 Protease in Chloroplast Biogenesis and Maintenance at Elevated Temperatures in Model and Crop Plants. United States Department of Agriculture, mayo de 2013. http://dx.doi.org/10.32747/2013.7699845.bard.
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