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Статті в журналах з теми "Protein Structure Models"
Mucsi, Z., Z. Gaspari, G. Orosz, and A. Perczel. "Structure-oriented rational design of chymotrypsin inhibitor models." Protein Engineering Design and Selection 16, no. 9 (September 1, 2003): 673–81. http://dx.doi.org/10.1093/protein/gzg090.
Повний текст джерелаKim, HyungRae. "Residue Environment Score for Selecting Protein Structure Models and Protein-Protein Docking Models." Biophysical Journal 110, no. 3 (February 2016): 346a. http://dx.doi.org/10.1016/j.bpj.2015.11.1862.
Повний текст джерелаKoliński, A., and J. Skolnick. "High coordination lattice models of protein structure, dynamics and thermodynamics." Acta Biochimica Polonica 44, no. 3 (September 30, 1997): 389–422. http://dx.doi.org/10.18388/abp.1997_4393.
Повний текст джерелаYang, Yifeng David, Preston Spratt, Hao Chen, Changsoon Park, and Daisuke Kihara. "Sub-AQUA: real-value quality assessment of protein structure models." Protein Engineering, Design and Selection 23, no. 8 (June 4, 2010): 617–32. http://dx.doi.org/10.1093/protein/gzq030.
Повний текст джерелаKihara, Daisuke, Hao Chen, and Yifeng Yang. "Quality Assessment of Protein Structure Models." Current Protein & Peptide Science 10, no. 3 (June 1, 2009): 216–28. http://dx.doi.org/10.2174/138920309788452173.
Повний текст джерелаHirsch, M., and M. Habeck. "Mixture models for protein structure ensembles." Bioinformatics 24, no. 19 (July 28, 2008): 2184–92. http://dx.doi.org/10.1093/bioinformatics/btn396.
Повний текст джерелаGhosh, Soma, and Saraswathi Vishveshwara. "Ranking the quality of protein structure models using sidechain based network properties." F1000Research 3 (January 21, 2014): 17. http://dx.doi.org/10.12688/f1000research.3-17.v1.
Повний текст джерелаDomingues, F. S., J. Rahnenfuhrer, and T. Lengauer. "Automated clustering of ensembles of alternative models in protein structure databases." Protein Engineering Design and Selection 17, no. 6 (August 3, 2004): 537–43. http://dx.doi.org/10.1093/protein/gzh063.
Повний текст джерелаShatabda, Swakkhar, M. A. Hakim Newton, Mahmood A. Rashid, Duc Nghia Pham, and Abdul Sattar. "How Good Are Simplified Models for Protein Structure Prediction?" Advances in Bioinformatics 2014 (April 29, 2014): 1–9. http://dx.doi.org/10.1155/2014/867179.
Повний текст джерелаBienkowska, J., Hongxian He, and T. F. Smith. "Automatic pattern embedding in protein structure models." IEEE Intelligent Systems 16, no. 6 (November 2001): 21–25. http://dx.doi.org/10.1109/5254.972074.
Повний текст джерелаДисертації з теми "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.
Повний текст джерелаGriffiths-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.
Повний текст джерелаWróblewska, Liliana. "Refinement of reduced protein models with all-atom force fields." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/26606.
Повний текст джерелаGamalielsson, 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.
Повний текст джерелаEvolutionary 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/.
Повний текст джерелаTångrot, 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.
Повний текст джерелаBioinformatik 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/.
Повний текст джерелаHayward, Steven John. "Studies in protein secondary structure prediction with neural network models." Thesis, University of Edinburgh, 1991. http://hdl.handle.net/1842/14034.
Повний текст джерелаGregor, Craig Robert. "Epitopes, aggregation and membrane binding : investigating the protein structure-function relationship." Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/5833.
Повний текст джерелаChippington-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.
Повний текст джерелаКниги з теми "Protein Structure Models"
Protein structure prediction. 3rd ed. New York: Humana Press, 2014.
Знайти повний текст джерелаHenrik, Bohr, and Brunak S[0]ren, eds. Protein structure by distance analysis. Amsterdam: IOS Press, 1994.
Знайти повний текст джерелаD, Fasman Gerald, ed. Prediction of protein structure and the principles of protein conformation. New York: Plenum Press, 1989.
Знайти повний текст джерелаD, Fasman Gerald, ed. Prediction of protein structure and the principles of protein confirmation. New York: Plenum, 1989.
Знайти повний текст джерела1964-, Liang Jie, Xu Ying 1960-, and Xu Dong 1965-, eds. Computational methods for protein structure prediction and modeling. New York, N.Y: Springer, 2007.
Знайти повний текст джерелаZimmermann, Karl-Heinz. An introduction to protein informatics. Boston: Kluwer Academic Publishers, 2003.
Знайти повний текст джерелаZimmermann, Karl-Heinz. An introduction to protein informatics. Boston: Kluwer Academic Publishers, 2003.
Знайти повний текст джерелаRangwala, Huzefa, G. Karypis, and G. Karypis. Introduction to protein structure prediction: Methods and algorithms. Hoboken, N.J: Wiley, 2010.
Знайти повний текст джерелаRigden, Daniel John. From protein structure to function with bioinformatics. [Dordrecht]: Springer, 2009.
Знайти повний текст джерелаZimmermann, Karl-Heinz. An introduction to protein informatics. Dordrecht: Springer-Science+Business Media, B.V., 2003.
Знайти повний текст джерелаЧастини книг з теми "Protein Structure Models"
Bystroff, Christopher, and Anders Krogh. "Hidden Markov Models for Prediction of Protein Features." In Protein Structure Prediction, 173–98. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-59745-574-9_7.
Повний текст джерелаMasso, Majid, and Iosif I. Vaisman. "Structure-Based Machine Learning Models for Computational Mutagenesis." In Introduction to Protein Structure Prediction, 403–30. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470882207.ch18.
Повний текст джерелаTautermann, Christofer S. "Target Based Virtual Screening by Docking into Automatically Generated GPCR Models." In Membrane Protein Structure and Dynamics, 255–70. Totowa, NJ: Humana Press, 2012. http://dx.doi.org/10.1007/978-1-62703-023-6_15.
Повний текст джерелаKihara, Daisuke, Yifeng David Yang, and Hao Chen. "Error Estimation of Template-Based Protein Structure Models." In 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.
Повний текст джерелаFrauenfelder, Hans. "Heme Protein Reactions: Models, Concepts, and Problems." In 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.
Повний текст джерелаIsin, Basak, Kalyan C. Tirupula, Zoltán N. Oltvai, Judith Klein-Seetharaman, and Ivet Bahar. "Identification of Motions in Membrane Proteins by Elastic Network Models and Their Experimental Validation." In Membrane Protein Structure and Dynamics, 285–317. Totowa, NJ: Humana Press, 2012. http://dx.doi.org/10.1007/978-1-62703-023-6_17.
Повний текст джерелаRigden, Daniel J., Iwona A. Cymerman, and Janusz M. Bujnicki. "Prediction of Protein Function from Theoretical Models." In 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.
Повний текст джерелаBlaszczyk, Maciej, Dominik Gront, Sebastian Kmiecik, Mateusz Kurcinski, Michal Kolinski, Maciej Pawel Ciemny, Katarzyna Ziolkowska, Marta Panek, and Andrzej Kolinski. "Protein Structure Prediction Using Coarse-Grained Models." In 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.
Повний текст джерелаHansen-Goos, Hendrik, and Seth Lichter. "Geometric Models of Protein Secondary-Structure Formation." In Nano and Cell Mechanics, 411–35. Chichester, UK: John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781118482568.ch16.
Повний текст джерелаBlaszczyk, Maciej, Dominik Gront, Sebastian Kmiecik, Katarzyna Ziolkowska, Marta Panek, and Andrzej Kolinski. "Coarse-Grained Protein Models in Structure Prediction." In 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.
Повний текст джерелаТези доповідей конференцій з теми "Protein Structure Models"
Winter, Pawel, and Rasmus Fonseca. "Alpha Complexes in Protein Structure Prediction." In International Conference on Bioinformatics Models, Methods and Algorithms. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005251401780182.
Повний текст джерела"DIFFERENTIAL EVOLUTION TO MULTI-OBJECTIVE PROTEIN STRUCTURE PREDICTION." In International Conference on Bioinformatics Models, Methods and Algorithms. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0003767002950298.
Повний текст джерела"STUDY OF PROTEIN STRUCTURE ALIGNMENT PROBLEM IN PARAMETERIZED COMPUTATION." In International Conference on Bioinformatics Models, Methods and Algorithms. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0003769701740181.
Повний текст джерела"A Hybrid Local Search for Simplified Protein Structure Prediction." In International Conference on Bioinformatics Models, Methods and Algorithms. SciTePress - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004239401580163.
Повний текст джерелаRAIMONDO, DOMENICO, ALEJANDRO GIORGETTI, DOMENICO COZZETTO, and ANNA TRAMONTANO. "QUALITY AND EFFECTIVENESS OF PROTEIN STRUCTURE COMPARATIVE MODELS." In Proceedings of the International Symposium on Mathematical and Computational Biology. WORLD SCIENTIFIC, 2006. http://dx.doi.org/10.1142/9789812773685_0017.
Повний текст джерелаArora, Bhumika. "Refinement of G protein-coupled receptor structure models." In 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.
Повний текст джерелаArikawa, Keisuke. "Analyzing Motion Properties of Proteins Affected by Localized Structures From a Robot Kinematics Perspective." In 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.
Повний текст джерела"BENEFITS OF GENETIC ALGORITHM FEATURE-BASED RESAMPLING FOR PROTEIN STRUCTURE PREDICTION." In International Conference on Bioinformatics Models, Methods and Algorithms. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0003770801880194.
Повний текст джерелаChu, Jia-Han, Chun Yuan Lin, Cheng-Wen Chang, Chihan Lee, Yuh-Shyong Yang, Chuan Yi Tang, Tuan D. Pham, and Xiaobo Zhou. "TIM Barrel Protein Structure Classification Using Alignment Approach and Best Hit Strategy." In COMPUTATIONAL MODELS FOR LIFE SCIENCES/CMLS '07. AIP, 2007. http://dx.doi.org/10.1063/1.2816621.
Повний текст джерелаAntczak, Piotr Lukasiak Maciej, Tomasz Ratajczak, Piotr Lukasiak, and Jacek Blazewicz. "SphereGrinder - reference structure-based tool for quality assessment of protein structural models." In 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2015. http://dx.doi.org/10.1109/bibm.2015.7359765.
Повний текст джерелаЗвіти організацій з теми "Protein Structure Models"
Hart, W. E., and 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), August 1996. http://dx.doi.org/10.2172/425317.
Повний текст джерелаToda, Magdalena, and Bhagya Athukorallage. Geometric Models for Secondary Structures in Proteins. GIQ, 2015. http://dx.doi.org/10.7546/giq-16-2015-282-300.
Повний текст джерелаChristopher, David A., and Avihai Danon. Plant Adaptation to Light Stress: Genetic Regulatory Mechanisms. United States Department of Agriculture, May 2004. http://dx.doi.org/10.32747/2004.7586534.bard.
Повний текст джерелаElbaum, Michael, and Peter J. Christie. Type IV Secretion System of Agrobacterium tumefaciens: Components and Structures. United States Department of Agriculture, March 2013. http://dx.doi.org/10.32747/2013.7699848.bard.
Повний текст джерелаOhad, Nir, and Robert Fischer. Regulation of Fertilization-Independent Endosperm Development by Polycomb Proteins. United States Department of Agriculture, January 2004. http://dx.doi.org/10.32747/2004.7695869.bard.
Повний текст джерелаKirchhoff, Helmut, and Ziv Reich. Protection of the photosynthetic apparatus during desiccation in resurrection plants. United States Department of Agriculture, February 2014. http://dx.doi.org/10.32747/2014.7699861.bard.
Повний текст джерелаNelson, Nathan, and Charles F. Yocum. Structure, Function and Utilization of Plant Photosynthetic Reaction Centers. United States Department of Agriculture, September 2012. http://dx.doi.org/10.32747/2012.7699846.bard.
Повний текст джерелаEpel, Bernard, and Roger Beachy. Mechanisms of intra- and intercellular targeting and movement of tobacco mosaic virus. United States Department of Agriculture, November 2005. http://dx.doi.org/10.32747/2005.7695874.bard.
Повний текст джерелаChamovitz, Daniel A., and Albrecht G. Von Arnim. eIF3 Complexes and the eIF3e Subunit in Arabidopsis Development and Translation Initiation. United States Department of Agriculture, September 2009. http://dx.doi.org/10.32747/2009.7696545.bard.
Повний текст джерелаChen, Junping, Zach Adam, and 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, May 2013. http://dx.doi.org/10.32747/2013.7699845.bard.
Повний текст джерела