{"product_id":"introduction-to-protein-structure-prediction-isbn-9780470470596","title":"Introduction to Protein Structure Prediction","description":"\u003cp\u003e\u003cb\u003eA look at the methods and algorithms used to predict protein structure\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA thorough knowledge of the function and structure of proteins is critical for the advancement of biology and the life sciences as well as the development of better drugs, higher-yield crops, and even synthetic bio-fuels. To that end, this reference sheds light on the methods used for protein structure prediction and reveals the key applications of modeled structures. This indispensable book covers the applications of modeled protein structures and unravels the relationship between pure sequence information and three-dimensional structure, which continues to be one of the greatest challenges in molecular biology.\u003c\/p\u003e \u003cp\u003eWith this resource, readers will find an all-encompassing examination of the problems, methods, tools, servers, databases, and applications of protein structure prediction and they will acquire unique insight into the future applications of the modeled protein structures. The book begins with a thorough introduction to the protein structure prediction problem and is divided into four themes: a background on structure prediction, the prediction of structural elements, tertiary structure prediction, and functional insights. Within those four sections, the following topics are covered:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eDatabases and resources that are commonly used for protein structure prediction\u003c\/li\u003e \u003cli\u003eThe structure prediction flagship assessment (CASP) and the protein structure initiative (PSI)\u003c\/li\u003e \u003cli\u003eDefinitions of recurring substructures and the computational approaches used for solving sequence problems\u003c\/li\u003e \u003cli\u003eDifficulties with contact map prediction and how sophisticated machine learning methods can solve those problems\u003c\/li\u003e \u003cli\u003eStructure prediction methods that rely on homology modeling, threading, and fragment assembly\u003c\/li\u003e \u003cli\u003eHybrid methods that achieve high-resolution protein structures\u003c\/li\u003e \u003cli\u003eParts of the protein structure that may be conserved and used to interact with other biomolecules\u003c\/li\u003e \u003cli\u003eHow the loop prediction problem can be used for refinement of the modeled structures\u003c\/li\u003e \u003cli\u003eThe computational model that detects the differences between protein structure and its modeled mutant\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eWhether working in the field of bioinformatics or molecular biology research or taking courses in protein modeling, readers will find the content in this book invaluable.\u003c\/p\u003e  Preface.  \u003cp\u003eContributors.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction to Protein Structure Prediction\u003c\/b\u003e (\u003ci\u003eHuzefa Rangwala and George Karypis\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 CASP: A Driving Force in Protein Structure Modeling\u003c\/b\u003e (\u003ci\u003eAndriy Kryshtafovych, Krzysztof Fidelis, and John Moult\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 The Protein Structure Initiative\u003c\/b\u003e (\u003ci\u003eAndras Fiser, Adam Godzik, Christine Orengo, and Burkhard Rost\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Prediction of One-Dimensional Structural Properties of Proteins by Integrated Neural Networks\u003c\/b\u003e (\u003ci\u003eYaoqi Zhou and Eshel Faraggi\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Local Structure Alphabets\u003c\/b\u003e (\u003ci\u003eAgnel Praveen Joseph, Aurélie Bornot, and Alexandre G. de Brevern\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Shedding Light on Transmembrane Topology\u003c\/b\u003e (\u003ci\u003eGábor E. Tusnády and István Simon\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Contact Map Prediction by Machine Learning\u003c\/b\u003e (\u003ci\u003eAlberto J.M. Martin, Catherine Mooney, Ian Walsh, and Gianluca Pollastri\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 A Survey of Remote Homology Detection and Fold Recognition Methods\u003c\/b\u003e (\u003ci\u003eHuzefa Rangwala\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Interactive Protein Fold Recognition by Alignments and Machine Learning\u003c\/b\u003e (\u003ci\u003eAllison N. Tegge, Zheng Wang, and Jianlin Cheng\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Tasser-Based Protein Structure Prediction\u003c\/b\u003e (\u003ci\u003eShashi Bhushan Pandit, Hongyi Zhou, and Jeffrey Skolnick\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Composite Approaches to Protein Tertiary Structure Prediction: A Case-Study by I-Tasser\u003c\/b\u003e (\u003ci\u003eAmbrish Roy, Sitao Wu, and Yang Zhang\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Hybrid Methods for Protein Structure Prediction\u003c\/b\u003e (\u003ci\u003eDmitri Mourado, Bostjan Kobe, Nicholas E. Dixon, and Thomas Huber\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Modeling Loops in Protein Structures\u003c\/b\u003e (\u003ci\u003eNarcis Fernandez-Fuentes, Andras Fiser\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Model Quality Assessment Using A Statistical Program that Adopts A Side Chain Environment Viewpoint\u003c\/b\u003e (\u003ci\u003eGenki Terashi, Mayuko Takeda-Shitaka, Kazuhiko Kanou and Hideaki Umeyama\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Model Quality Prediction\u003c\/b\u003e (\u003ci\u003eLiam J. McGuffin\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Ligand-Binding Residue Prediction\u003c\/b\u003e (\u003ci\u003eChris Kauffman and George Karypis\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Modeling and Validation of Transmembrane Protein Structures\u003c\/b\u003e (\u003ci\u003eMaya Schushan and Nir Ben-Tal\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Structure-Based Machine Learning Models for Computational Mutagenesis\u003c\/b\u003e (\u003ci\u003eMajid Masso and Iosif I. Vaisman\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Conformational Search for the Protein Native State\u003c\/b\u003e (\u003ci\u003eAmarda Shehu\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Modeling Mutations in Proteins Using MEDUSA and Discrete Molecule Dynamics\u003c\/b\u003e (\u003ci\u003eShuangye Yin, Feng Ding, and Nikolay V. Dokholyan\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003eIndex.\u003c\/p\u003e   \u003cbr\u003e \u003cbr\u003e  \u003cb\u003eDR. HUZEFA RANGWALA\u003c\/b\u003e is an assistant professor in computer science and bioengineering at George Mason University. He has published in various conferences and journals on the topic of bioinformatics.  \u003cp\u003e\u003cb\u003eDR. GEORGE KARYPIS\u003c\/b\u003e is a professor in computer science and engineering at the University of Minnesota. He has authored more than one hundred journal and conference papers and also serves on the editorial board of the \u003ci\u003eInternational Journal of Data Mining and Bioinformatics.\u003c\/i\u003e\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989465383141,"sku":"NP9780470470596","price":171.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470470596.jpg?v=1761784212","url":"https:\/\/k12savings.com\/es\/products\/introduction-to-protein-structure-prediction-isbn-9780470470596","provider":"K12savings","version":"1.0","type":"link"}