LEADER 04098nam 2200673Ia 450 001 9910806241903321 005 20240509092337.0 010 $a1-118-09946-X 010 $a1-282-78286-X 010 $a9786612782862 010 $a0-470-88220-4 010 $a0-470-88219-0 035 $a(CKB)2670000000047093 035 $a(EBL)589069 035 $a(SSID)ssj0000441277 035 $a(PQKBManifestationID)11925805 035 $a(PQKBTitleCode)TC0000441277 035 $a(PQKBWorkID)10407391 035 $a(PQKB)11291148 035 $a(Au-PeEL)EBL589069 035 $a(CaPaEBR)ebr10419276 035 $a(CaONFJC)MIL278286 035 $a(PPN)19458612X 035 $a(MiAaPQ)EBC589069 035 $a(OCoLC)669165908 035 $a(EXLCZ)992670000000047093 100 $a20100706d2010 uy 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt 182 $cc 183 $acr 200 00$aIntroduction to protein structure prediction $emethods and algorithms /$fedited by Huzefa Rangwala, George Karypis 205 $a1st ed. 210 $aHoboken, NJ $cWiley$d2010 215 $a1 online resource (532 p.) 225 1 $aWiley series in bioinformatics ;$v14 300 $aDescription based upon print version of record. 311 $a0-470-47059-3 320 $aIncludes bibliographical references and index. 327 $aINTRODUCTION TO PROTEIN STRUCTURE PREDICTION: Methods and Algorithms; CONTENTS; PREFACE; CONTRIBUTORS; CHAPTER 1: INTRODUCTION TO PROTEIN STRUCTURE PREDICTION; CHAPTER 2: CASP : A DRIVING FORCE IN PROTEIN STRUCTURE MODELING; CHAPTER 3: THE PROTEIN STRUCTURE INITIATIVE; CHAPTER 4: PREDICTION OF ONE - DIMENSIONAL STRUCTURAL PROPERTIES OF PROTEINS BY INTEGRATED NEURAL NETWORKS; CHAPTER 5: LOCAL STRUCTURE ALPHABETS; CHAPTER 6: SHEDDING LIGHT ON TRANSMEMBRANE TOPOLOGY; CHAPTER 7: CONTACT MAP PREDICTION BY MACHINE LEARNING 327 $aCHAPTER 8: A SURVEY OF REMOTE HOMOLOGY DETECTION AND FOLD RECOGNITION METHODS CHAPTER 9: INTEGRATIVE PROTEIN FOLD RECOGNITION BY ALIGNMENTS AND MACHINE LEARNING; CHAPTER 10: TASSER - BASED PROTEIN STRUCTURE PREDICTION; CHAPTER 11: COMPOSITE APPROACHES TO PROTEIN TERTIARY STRUCTURE PREDICTION: A CASE - STUDY BY I - TASSER; CHAPTER 12: HYBRID METHODS FOR PROTEIN STRUCTURE PREDICTION; CHAPTER 13: MODELING LOOPS IN PROTEIN STRUCTURES; CHAPTER 14: MODEL QUALITY ASSESSMENT USING A STATISTICAL PROGRAM THAT ADOPTS A SIDE CHAIN ENVIRONMENT VIEWPOINT; CHAPTER 15: MODEL QUALITY PREDICTION 327 $aCHAPTER 16: LIGAND - BINDING RESIDUE PREDICTION CHAPTER 17: MODELING AND VALIDATION OF TRANSMEMBRANE PROTEIN STRUCTURES; CHAPTER 18: STRUCTURE - BASED MACHINE LEARNING MODELS FOR COMPUTATIONAL MUTAGENESIS; CHAPTER 19: CONFORMATIONAL SEARCH FOR THE PROTEIN NATIVE STATE; CHAPTER 20: MODELING MUTATIONS IN PROTEINS USING MEDUSA AND DISCRETE MOLECULE DYNAMICS; INDEX; colour plates 330 $aA look at the methods and algorithms used to predict protein structure A thorough knowledge of the function and structure of proteins is critical for the advancement of biology and the life sciences as well as the development of better drugs, higher-yield crops, and even synthetic bio-fuels. To that end, this reference sheds light on the methods used for protein structure prediction and reveals the key applications of modeled structures. This indispensable book covers the applications of modeled protein structures and unravels the relationship between pure sequence information and 410 0$aWiley series on bioinformatics ;$v14. 606 $aProteins$xStructure$xMathematical models 606 $aProteins$xStructure$xComputer simulation 615 0$aProteins$xStructure$xMathematical models. 615 0$aProteins$xStructure$xComputer simulation. 676 $a572/.633 701 $aRangwala$b Huzefa$01639427 701 $aKarypis$b G$g(George)$065765 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910806241903321 996 $aIntroduction to protein structure prediction$93982399 997 $aUNINA