Protein structure prediction : concepts and applications

Başlık:

Yazar:

Tramontano, Anna.

ISBN:

9783527311675

Ek Yazar:

Tramontano, Anna.

Yayım Bilgisi:

Weinheim : Wiley-VCH, c2006.

Fiziksel Tanım:

xix, 205 p. : ill. ; 25 cm.

Mevcut:*

Library	Materyal Türü	Barkod	Yer Numarası	Durum
Searching... Pamukkale Merkez Kütüphanesi	Kitap	0052390	QP551.T725 2006	Searching... Unknown

While most textbooks on bioinformatics focus on genetic algorithms and treat protein structure prediction only superficially, this course book assumes a novel and unique focus. Adopting a didactic approach, the author explains all the current methods in terms of their reliability, limitations and user-friendliness. She provides practical examples to help first-time users become familiar with the possibilities and pitfalls of computer-based structure prediction, making this a must-have for students and researchers.

Author Notes

Anna Tramontano is a member of the European Molecular Biology Organization, the Scientific Council of Institute Pasteur - Fondazione Cenci Bolognetti, and the organizing Committee of the Critical Assesment of Techniques for Protein Structure Prediction (CSAP) initiative

Reviews (1)

Choice Review

Tramontano's short book on the methods and strategies used in protein structure prediction is an appropriate introduction for biophysicists. It provides a good review of how the field has progressed and what one can expect with current methodologies. This book requires readers to have a background in protein chemistry and structure. Although the sections on computational analysis methods are adequate, the background sections on amino acid properties and membranes are sparse and not terribly informative. There is no mention of modeling quaternary structures. Most background topics are not referenced in enough detail to allow for further research. The chapter order is strange: students should read the book in a different order if it is used as a resource in an advanced protein structure course. Though several chapters contain Web addresses for some of the structure prediction algorithms presented, many do not. This work cries out for an interactive Web site that allows readers to render the two-dimensional images in three dimensions as well as include demonstrations on the use of the methods with simple examples. This is a book that could have been much more. Summing Up: Optional. Upper-division undergraduates through professionals. J. M. Tomich Kansas State University

Foreword	p. VII
Preface	p. XII
Acknowledgments	p. XV
Introduction	p. XVI
1 Sequence, Function, and Structure Relationships	p. 1
1.1 Introduction	p. 1
1.2 Protein Structure	p. 4
1.3 The Properties of Amino Acids	p. 12
1.4 Experimental Determination of Protein Structures	p. 14
1.5 The PDB Protein Structure Data Archive	p. 20
1.6 Classification of Protein Structures	p. 22
1.7 The Protein-folding Problem	p. 24
1.8 Inference of Function from Structure	p. 27
1.9 The Evolution of Protein Function	p. 29
1.10 The Evolution of Protein Structure	p. 34
1.11 Relationship Between Evolution of Sequence and Evolution of Structure	p. 37
2 Reliability of Methods for Prediction of Protein Structure	p. 41
2.1 Introduction	p. 41
2.2 Prediction of Secondary Structure	p. 43
2.3 Prediction of Tertiary Structure	p. 46
2.4 Benchmarking a Prediction Method	p. 50
2.5 Blind Automatic Assessments	p. 51
2.6 The CASP Experiments	p. 51
3 Ab-initio Methods for Prediction of Protein Structures	p. 55
3.1 The Energy of a Protein Configuration	p. 55
3.2 Interactions and Energies	p. 55
3.3 Covalent Interactions	p. 56
3.4 Electrostatic Interactions	p. 58
3.5 Potential-energy Functions	p. 62
3.6 Statistical-mechanics Potentials	p. 62
3.7 Energy Minimization	p. 65
3.8 Molecular Dynamics	p. 66
3.9 Other Search Methods: Monte Carlo and Genetic Algorithms	p. 67
3.10 Effectiveness of Ab-initio Methods for Folding a Protein	p. 70
4 Evolutionary-based Methods for Predicting Protein Structure: Comparative Modeling	p. 73
4.1 Introduction	p. 73
4.2 Theoretical Basis of Comparative Modeling	p. 75
4.3 Detection of Evolutionary Relationships from Sequences	p. 77
4.4 The Needleman and Wunsch Algorithm	p. 79
4.5 Substitution Matrices	p. 81
4.6 Template(s) Identification Part I	p. 84
4.7 The Problem of Domains	p. 90
4.8 Alignment	p. 91
4.9 Template(s) Identification Part II	p. 96
4.10 Building the Main Chain of the Core	p. 97
4.11 Building Structurally Divergent Regions	p. 98
4.12 A Special Case: Immunoglobulins	p. 102
4.13 Side-chains	p. 106
4.14 Model Optimization	p. 107
4.15 Other Approaches	p. 108
4.16 Effectiveness of Comparative Modeling Methods	p. 109
5 Sequence-Structure Fitness Identification: Fold-recognition Methods	p. 117
5.1 The Theoretical Basis of Fold-recognition	p. 117
5.2 Profile-based Methods for Fold-recognition	p. 119
5.3 Threading Methods	p. 121
5.4 Profile-Profile Methods	p. 124
5.5 Construction and Optimization of the Model	p. 124
6 Methods Used to Predict New Folds: Fragment-based Methods	p. 127
6.1 Introduction	p. 127
6.2 Fragment-based Methods	p. 128
6.3 Splitting the Sequence into Fragments and Selecting Fragments from the Database	p. 130
6.4 Generation of Structures	p. 135
7 Low-dimensionality Prediction: Secondary Structure and Contact Prediction	p. 137
7.1 Introduction	p. 137
7.2 A Short History of Secondary structure Prediction Methods	p. 140
7.3 Automatic learning Methods	p. 142
7.3.1 Artificial Neural Networks	p. 142
7.3.2 Support Vector Machines	p. 148
7.4 Secondary structure Prediction Methods Based on Automatic Learning Techniques	p. 150
7.5 Prediction of Long-range Contacts	p. 153
8 Membrane Proteins	p. 159
8.1 Introduction	p. 159
8.2 Prediction of the Secondary Structure of Membrane Proteins	p. 162
8.3 The Hydrophobic Moment	p. 165
8.4 Prediction of the Topology of Membrane Proteins	p. 166
9 Applications and Examples	p. 169
9.1 Introduction	p. 169
9.2 Early Attempts	p. 169
9.3 The HIV Protease	p. 171
9.4 Leptin and Obesity	p. 174
9.5 The Envelope Glycoprotein of the Hepatitis C Virus	p. 176
9.6 HCV Protease	p. 178
9.7 Cyclic Nucleotide Gated Channels	p. 181
9.8 The Effectiveness of Models of Proteins in Drug Discovery	p. 183
9.9 The Effectiveness of Models of Proteins in X-ray Structure Solution	p. 186
Conclusions	p. 188
Glossary	p. 190
Index	p. 201

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