Cover image for Molecular modeling : basic principles and applications.
Title:
Molecular modeling : basic principles and applications.
Author:
Höltje, Hans-Dieter.
Edition:
Second edition / [Hans-Dieter Höltje ... [and others] in collaboration with Robin Ghosh and Pavel Pospisil.
Publication Information:
Weinheim ; [Great Britain] : Wiley-VCH, [2003]

©2003
Physical Description:
xii, 228 pages : illustrations (some color) ; 25 cm
General Note:
Previous ed.: / Hans-Dieter Höltje and Gerd Folkers. 1997.
Language:
English
Added Author:
ISBN:
9783527305896
Format :
Book

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Call Number
Material Type
Home Location
Status
Central Library QP517.M3 M65 2003 Adult Non-Fiction Non-Fiction Area
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Summary

Summary

Written by experienced experts in the field, this book describes the basics to the extent necessary for reliably judging the results from molecular modeling calculations.
Without unnecessary overhead, it leads readers from simple calculations on small molecules to the modeling of proteins and other relevant biomolecules. Beginners are guided through their first modeling experiment, while routine users of modeling software are provided with invaluable troubleshooting hints. A unique resource for students, researchers and lecturers, now available in this all-new, enlarged edition.

"If the currently popular 'Dummies' series of computer books were to publish a volume on molecular modeling this would be it" (Journal of the American Chemical Society)

"The book is well written and assumes no prior knowledge of molecular biology, statistical mechanics, or quantum chemistry. The authors provide practical hints for the application of the majority of available programs in computational chemistry" (Computers in Physics)


Author Notes

Hans-Dieter Höltje is director of the Institute of Pharmaceutical Chemistry at the Heinrich-Heine-Universität Düsseldorf, where he also holds the chair of Medicinal Chemistry. His main interest is the molecular mechanism of drug action. He is especially interested in modeling G-Protein-Coupled receptors, cytochromes, enzymes of therapeutic importance and phospholipid membranes.

Wolfgang Sippl is Professor of Pharmaceutical Chemistry at the Martin-Luther-University of Halle-Wittenberg, Germany. He is interested in 3D QSAR, molecular docking and molecular dynamics, and their applications in drug design and pharmacokinetics.

Didier Rognan leads the Drug Bioinformatics Group at the Laboratory for Molecular Pharmacochemistry in Illkrich (France) He is mainly interested in all aspects (method development, applications) of protein-based drug design and virtual screening.

Gerd Folkers is Professor of Pharmaceutical Chemistry at the ETH Zurich. The focus of his research is the molecular interaction between drugs and their binding sites. Besides his work on the molecular mechanism of "conventional" nucleoside therapeutics against virus infection and cancer, his special interest has shifted to immuno-therapeutics.


Table of Contents

Prefacep. 9
1 Introductionp. 1
1.1 Modern History of Molecular Modelingp. 2
1.2 Do Today's Molecular Modeling Methods Illustrate only the Lukretian World?p. 3
1.3 What are Models Used for?p. 4
1.4 Molecular Modeling Uses All Four Types for Model Buildingp. 4
1.5 The Final Step is Designp. 5
1.6 The Scope of the Bookp. 6
2 Small Moleculesp. 9
2.1 Generation of 3D Coordinatesp. 9
2.1.1 Crystal Datap. 9
2.1.2 Fragment Librariesp. 10
2.1.3 Sketch Approachp. 12
2.1.4 Conversion of 2D Structural Data into 3D Formp. 12
2.2 Computational Tools for Geometry Optimizationp. 15
2.2.1 Force Fieldsp. 15
2.2.2 Geometry Optimizationp. 17
2.2.3 Energy-Minimizing Proceduresp. 18
2.2.4 Use of Charges, Solvation Effectsp. 20
2.2.5 Quantum Mechanical Methodsp. 21
2.3 Conformational Analysisp. 27
2.3.1 Conformational Analysis Using Systematic Search Proceduresp. 29
2.3.2 Conformational Analysis Using Monte Carlo Methodsp. 32
2.3.3 Conformational Analysis Using Molecular Dynamicsp. 33
2.4 Determination of Molecular Interaction Potentialsp. 42
2.4.1 Molecular Electrostatic Potentials (MEPs)p. 42
2.4.2 Molecular Interaction Fieldsp. 50
2.4.3 Display of Properties on a Molecular Surfacep. 56
2.5 Pharmacophore Identificationp. 59
2.5.1 Molecules to be Matchedp. 59
2.5.2 Atom-by-Atom Superpositionp. 61
2.5.3 Superposition of Molecular Fieldsp. 63
2.6 3D QSAR Methodsp. 65
2.6.1 The CoMFA Methodp. 65
2.6.2 CoMFA-related Methodsp. 69
2.6.3 More 3D QSAR Methodsp. 70
3 A Case Study for Small Molecule Modeling: Dopamine D[subscript 3] Receptor Antagonistsp. 173
3.1 A Pharmacophore Model for Dopamine D[subscript 3] Receptor Antagonistsp. 73
3.1.1 The Aromatic-Basic Fragmentp. 76
3.1.2 The Spacerp. 78
3.1.3 The Aromatic-Amidic Residuep. 79
3.1.4 Resulting Pharmacophorep. 79
3.1.5 Molecular Interaction Fieldsp. 80
3.2 3D QSAR Analysisp. 82
3.2.1 Variable Reduction and PLS modelp. 82
3.2.2 Validation of the Methodp. 84
3.2.3 Prediction of External Ligandsp. 85
4 Introduction to Comparative Protein Modelingp. 87
4.1 Where and How to get Information on Proteinsp. 87
4.2 Terminology and Principles of Protein Structurep. 91
4.2.1 Conformational Properties of Proteinsp. 91
4.2.2 Types of Secondary Structural Elementsp. 94
4.2.3 Homologous Proteinsp. 98
4.3 Comparative Protein Modelingp. 100
4.3.1 Procedures for Sequence Alignmentsp. 101
4.3.2 Determination and Generation of Structurally Conserved Regions (SCRs)p. 106
4.3.3 Construction of Structurally Variable Regions (SVRs)p. 108
4.3.4 Side Chain Modelingp. 109
4.3.5 Distance Geometry Approachp. 111
4.3.6 Secondary Structure Predictionp. 111
4.3.7 Threading Methodsp. 115
4.4 Optimization Procedures--Model Refinement--Molecular Dynamicsp. 119
4.4.1 Force Fields for Protein Modelingp. 119
4.4.2 Geometry Optimizationp. 120
4.4.3 The Use of Molecular Dynamics Simulations in Model Refinementp. 121
4.4.4 Treatment of Solvated Systemsp. 123
4.4.5 Ligand-Binding Site Complexesp. 124
4.5 Validation of Protein Modelsp. 126
4.5.1 Stereochemical Accuracyp. 127
4.5.2 Packing Qualityp. 131
4.5.3 Folding Reliabilityp. 133
4.6 Properties of Proteinsp. 138
4.6.1 Electrostatic Potentialp. 138
4.6.2 Interaction Potentialsp. 142
4.6.3 Hydrophobicityp. 142
5 Protein-based Virtual Screeningp. 145
5.1 Preparationp. 145
5.1.1 Database Preparationp. 145
5.1.2 Representation of Proteins and Ligandsp. 147
5.2 Docking Algorithmsp. 149
5.2.1 Incremental Construction Methodsp. 150
5.2.2 Genetic Algorithmsp. 152
5.2.3 Tabu Searchp. 153
5.2.4 Simulated Annealing and Monte Carlo Simulationsp. 154
5.2.5 Shape-fitting Methodsp. 155
5.2.6 Miscellaneous Approachesp. 155
5.3 Scoring Functionsp. 156
5.3.1 Empirical Scoring Functionsp. 157
5.3.2 Force Field-based Scoring Functionsp. 158
5.3.3 Knowledge-based Scoring Functionsp. 158
5.4 Postfiltering VS Resultsp. 159
5.4.1 Filtering by Topological Propertiesp. 159
5.4.2 Filtering by Multiple Scoringp. 159
5.4.3 Filtering by Combining Computational Proceduresp. 160
5.4.4 Filtering by Chemical Diversityp. 161
5.4.5 Filtering by Visual Inspectionp. 161
5.5 Comparison of Different Docking and Scoring Methodsp. 161
5.6 Examples of Successful Virtual Screening Studiesp. 162
5.7 The Future of Virtual Screeningp. 164
6 Scope and Limits of Molecular Dockingp. 169
6.1 Docking in the Polar Active Site That Contains Water Molecules - Viral Thymidine Kinasep. 170
6.1.1 Setting the Scenep. 171
6.2 Learning from the Resultsp. 172
6.2.1 Water Contribution on dT and ACV Dockingp. 172
6.2.2 In Search of the Binding Constantp. 175
6.2.3 Application to Virtual Screeningp. 176
7 Example for the Modeling of Protein-Ligand Complexes: Antigen Presentation by MHC Class Ip. 179
7.1 Biochemical and Pharmacological Description of the Problemp. 179
7.1.1 Antigenic Proteins are Presented as Nonapeptidesp. 180
7.1.2 Pharmacological Target: Autoimmune Reactionsp. 180
7.2 Molecular Modeling of the Antigenic Complex Between a Viral Peptide and a Class I MHC Glycoproteinp. 181
7.2.1 Modeling of the Ligandp. 181
7.2.2 Homology Modeling of the MHC Proteinp. 183
7.3 Molecular Dynamics Studies of MHC-Peptide Complexesp. 192
7.3.1 HLA-A2--The Fate of the Complex during Molecular Dynamics Simulationsp. 192
7.3.2 HLA-B*2705p. 194
7.4 Analysis of Models that Emerged from Molecular Dynamics Simulationsp. 199
7.4.1 Hydrogen Bonding Networkp. 200
7.4.2 Atomic Fluctuationsp. 200
7.4.3 Solvent-Accessible Surface Areasp. 203
7.4.4 Interaction Energiesp. 204
7.5 SAR of the Antigenic Peptides from Molecular Dynamics Simulations and Design of Non-natural Peptides as High-Affinity Ligands for a MHC I Proteinp. 206
7.5.1 The Design of New Ligandsp. 206
7.5.2 Experimental Validation of the Designed Ligandsp. 209
7.6 How Far Does the Model Hold? Studies on Fine Specificity of Antigene Binding to Other MHC Proteins and Mutantsp. 211
7.7 The T-Cell Receptor Comes inp. 211
7.8 Some Concluding Remarksp. 214
Indexp. 217

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