Protein Structure Prediction

Anna Tramontano is Professor of Biochemistry at the Medical Faculty of the University of Rome "La Sapienza" since 2001. She received her PhD in Physics from the University of Naples (Italy) in 1980 and held various appointments at research institutes in Europe and in the U.S. before becoming Professor of Bioinformatics at the University of Milan in 1990. From 1996 to 2001 she held the position as director of Computational Biology and Chemistry at the Merck Research Laboratories in Rome.Anna Tramontano is among the organizers of the CASP (critical assessment of protein structure prediction) conferences and is on the editorial boards of several journals.

• Foreword viiPreface xiiAcknowledgments xvIntroduction xvi1 Sequence, Function, and Structure Relationships 11.1 Introduction 11.2 Protein Structure 41.3 The Properties of Amino Acids 121.4 Experimental Determination of Protein Structures 141.5 The PDB Protein Structure Data Archive 201.6 Classification of Protein Structures 221.7 The Protein-folding Problem 241.8 Inference of Function from Structure 271.9 The Evolution of Protein Function 291.10 The Evolution of Protein Structure 341.11 Relationship Between Evolution of Sequence and Evolution of Structure 372 Reliability of Methods for Prediction of Protein Structure 412.1 Introduction 412.2 Prediction of Secondary Structure 432.3 Prediction of Tertiary Structure 462.4 Benchmarking a Prediction Method 502.5 Blind Automatic Assessments 512.6 The CASP Experiments 513 Ab-initio Methods for Prediction of Protein Structures 553.1 The Energy of a Protein Configuration 553.2 Interactions and Energies 553.3 Covalent Interactions 563.4 Electrostatic Interactions 583.5 Potential-energy Functions 623.6 Statistical-mechanics Potentials 623.7 Energy Minimization 653.8 Molecular Dynamics 663.9 Other Search Methods: Monte Carlo and Genetic Algorithms 673.10 Effectiveness of Ab-initio Methods for Folding a Protein 704 Evolutionary-based Methods for Predicting Protein Structure: Comparative Modeling 734.1 Introduction 734.2 Theoretical Basis of Comparative Modeling 754.3 Detection of Evolutionary Relationships from Sequences 774.4 The Needleman and Wunsch Algorithm 794.5 Substitution Matrices 814.6 Template(s) Identification Part I 844.7 The Problem of Domains 904.8 Alignment 914.9 Template(s) Identification Part II 964.10 Building the Main Chain of the Core 974.11 Building Structurally Divergent Regions 984.12 A Special Case: Immunoglobulins 1024.13 Side-chains 1064.14 Model Optimization 1074.15 Other Approaches 1084.16 Effectiveness of Comparative Modeling Methods 1095 Sequence-Structure Fitness Identification: Fold-recognition Methods 1175.1 The Theoretical Basis of Fold-recognition 1175.2 Profile-based Methods for Fold-recognition 1195.3 Threading Methods 1215.4 Profile–Profile Methods 1245.5 Construction and Optimization of the Model 1246 Methods Used to Predict New Folds: Fragment-based Methods 1276.1 Introduction 1276.2 Fragment-based Methods 1286.3 Splitting the Sequence into Fragments and Selecting Fragments from the Database 1306.4 Generation of Structures 1357 Low-dimensionality Prediction: Secondary Structure and Contact Prediction 1377.1 Introduction 1377.2 A Short History of Secondary structure Prediction Methods 1407.3 Automatic learning Methods 1427.3.1 Artificial Neural Networks 1427.3.2 Support Vector Machines 1487.4 Secondary structure Prediction Methods Based on Automatic Learning Techniques 1507.5 Prediction of Long-range Contacts 1538 Membrane Proteins 1598.1 Introduction 1598.2 Prediction of the Secondary Structure of Membrane Proteins 1628.3 The Hydrophobic Moment 1658.4 Prediction of the Topology of Membrane Proteins 1669 Applications and Examples 1699.1 Introduction 1699.2 Early Attempts 1699.3 The HIV Protease 1719.4 Leptin and Obesity 1749.5 The Envelope Glycoprotein of the Hepatitis C Virus 1769.6 HCV Protease 1789.7 Cyclic Nucleotide Gated Channels 1819.8 The Effectiveness of Models of Proteins in Drug Discovery 1839.9 The Effectiveness of Models of Proteins in X-ray Structure Solution 186Conclusions 188Glossary 190Index 201

"...this is a uniquely educational book, ideal as a teaching manual, especially for undergraduate students studying this fast-changing discipline, and for graduate students from other areas of science who want to quickly grasp the basics of protein structure prediction. Excellent value for money!" ChemBioChem "... a good review of how the field has progressed and what one can expect with current methodologies."CHOICE