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Molecular modeling : basic principles and applications / Hans-Dieter Höltje... [et al.]; in collaboration with Robin Ghosh, Pavel Pospisil

Coauthor Höltje, Hans-Dieter Secondary Author Ghosh, Robin
Pospisil, Pavel
Country Alemanha. Edition [2nd ed] Publication Weinheim : Wiley-VCH, imp. 2005 Description XII, 228 p. ; 25 cm ISBN 3-527-30589-0 CDU 541.1:681.3 681.3:541.1 544.112 577.2
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Holdings
Item type Current location Call number Status Date due Barcode Item holds
Monografia Biblioteca de Química
BQ 541.1:681.3 - M Indisponível | Not available 363959
Total holds: 0

Enhanced descriptions from Syndetics:

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)

Table of contents provided by Syndetics

  • Preface (p. 9)
  • 1 Introduction (p. 1)
  • 1.1 Modern History of Molecular Modeling (p. 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 Building (p. 4)
  • 1.5 The Final Step is Design (p. 5)
  • 1.6 The Scope of the Book (p. 6)
  • 2 Small Molecules (p. 9)
  • 2.1 Generation of 3D Coordinates (p. 9)
  • 2.1.1 Crystal Data (p. 9)
  • 2.1.2 Fragment Libraries (p. 10)
  • 2.1.3 Sketch Approach (p. 12)
  • 2.1.4 Conversion of 2D Structural Data into 3D Form (p. 12)
  • 2.2 Computational Tools for Geometry Optimization (p. 15)
  • 2.2.1 Force Fields (p. 15)
  • 2.2.2 Geometry Optimization (p. 17)
  • 2.2.3 Energy-Minimizing Procedures (p. 18)
  • 2.2.4 Use of Charges, Solvation Effects (p. 20)
  • 2.2.5 Quantum Mechanical Methods (p. 21)
  • 2.3 Conformational Analysis (p. 27)
  • 2.3.1 Conformational Analysis Using Systematic Search Procedures (p. 29)
  • 2.3.2 Conformational Analysis Using Monte Carlo Methods (p. 32)
  • 2.3.3 Conformational Analysis Using Molecular Dynamics (p. 33)
  • 2.4 Determination of Molecular Interaction Potentials (p. 42)
  • 2.4.1 Molecular Electrostatic Potentials (MEPs) (p. 42)
  • 2.4.2 Molecular Interaction Fields (p. 50)
  • 2.4.3 Display of Properties on a Molecular Surface (p. 56)
  • 2.5 Pharmacophore Identification (p. 59)
  • 2.5.1 Molecules to be Matched (p. 59)
  • 2.5.2 Atom-by-Atom Superposition (p. 61)
  • 2.5.3 Superposition of Molecular Fields (p. 63)
  • 2.6 3D QSAR Methods (p. 65)
  • 2.6.1 The CoMFA Method (p. 65)
  • 2.6.2 CoMFA-related Methods (p. 69)
  • 2.6.3 More 3D QSAR Methods (p. 70)
  • 3 A Case Study for Small Molecule Modeling: Dopamine D[subscript 3] Receptor Antagonists (p. 173)
  • 3.1 A Pharmacophore Model for Dopamine D[subscript 3] Receptor Antagonists (p. 73)
  • 3.1.1 The Aromatic-Basic Fragment (p. 76)
  • 3.1.2 The Spacer (p. 78)
  • 3.1.3 The Aromatic-Amidic Residue (p. 79)
  • 3.1.4 Resulting Pharmacophore (p. 79)
  • 3.1.5 Molecular Interaction Fields (p. 80)
  • 3.2 3D QSAR Analysis (p. 82)
  • 3.2.1 Variable Reduction and PLS model (p. 82)
  • 3.2.2 Validation of the Method (p. 84)
  • 3.2.3 Prediction of External Ligands (p. 85)
  • 4 Introduction to Comparative Protein Modeling (p. 87)
  • 4.1 Where and How to get Information on Proteins (p. 87)
  • 4.2 Terminology and Principles of Protein Structure (p. 91)
  • 4.2.1 Conformational Properties of Proteins (p. 91)
  • 4.2.2 Types of Secondary Structural Elements (p. 94)
  • 4.2.3 Homologous Proteins (p. 98)
  • 4.3 Comparative Protein Modeling (p. 100)
  • 4.3.1 Procedures for Sequence Alignments (p. 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 Modeling (p. 109)
  • 4.3.5 Distance Geometry Approach (p. 111)
  • 4.3.6 Secondary Structure Prediction (p. 111)
  • 4.3.7 Threading Methods (p. 115)
  • 4.4 Optimization Procedures--Model Refinement--Molecular Dynamics (p. 119)
  • 4.4.1 Force Fields for Protein Modeling (p. 119)
  • 4.4.2 Geometry Optimization (p. 120)
  • 4.4.3 The Use of Molecular Dynamics Simulations in Model Refinement (p. 121)
  • 4.4.4 Treatment of Solvated Systems (p. 123)
  • 4.4.5 Ligand-Binding Site Complexes (p. 124)
  • 4.5 Validation of Protein Models (p. 126)
  • 4.5.1 Stereochemical Accuracy (p. 127)
  • 4.5.2 Packing Quality (p. 131)
  • 4.5.3 Folding Reliability (p. 133)
  • 4.6 Properties of Proteins (p. 138)
  • 4.6.1 Electrostatic Potential (p. 138)
  • 4.6.2 Interaction Potentials (p. 142)
  • 4.6.3 Hydrophobicity (p. 142)
  • 5 Protein-based Virtual Screening (p. 145)
  • 5.1 Preparation (p. 145)
  • 5.1.1 Database Preparation (p. 145)
  • 5.1.2 Representation of Proteins and Ligands (p. 147)
  • 5.2 Docking Algorithms (p. 149)
  • 5.2.1 Incremental Construction Methods (p. 150)
  • 5.2.2 Genetic Algorithms (p. 152)
  • 5.2.3 Tabu Search (p. 153)
  • 5.2.4 Simulated Annealing and Monte Carlo Simulations (p. 154)
  • 5.2.5 Shape-fitting Methods (p. 155)
  • 5.2.6 Miscellaneous Approaches (p. 155)
  • 5.3 Scoring Functions (p. 156)
  • 5.3.1 Empirical Scoring Functions (p. 157)
  • 5.3.2 Force Field-based Scoring Functions (p. 158)
  • 5.3.3 Knowledge-based Scoring Functions (p. 158)
  • 5.4 Postfiltering VS Results (p. 159)
  • 5.4.1 Filtering by Topological Properties (p. 159)
  • 5.4.2 Filtering by Multiple Scoring (p. 159)
  • 5.4.3 Filtering by Combining Computational Procedures (p. 160)
  • 5.4.4 Filtering by Chemical Diversity (p. 161)
  • 5.4.5 Filtering by Visual Inspection (p. 161)
  • 5.5 Comparison of Different Docking and Scoring Methods (p. 161)
  • 5.6 Examples of Successful Virtual Screening Studies (p. 162)
  • 5.7 The Future of Virtual Screening (p. 164)
  • 6 Scope and Limits of Molecular Docking (p. 169)
  • 6.1 Docking in the Polar Active Site That Contains Water Molecules - Viral Thymidine Kinase (p. 170)
  • 6.1.1 Setting the Scene (p. 171)
  • 6.2 Learning from the Results (p. 172)
  • 6.2.1 Water Contribution on dT and ACV Docking (p. 172)
  • 6.2.2 In Search of the Binding Constant (p. 175)
  • 6.2.3 Application to Virtual Screening (p. 176)
  • 7 Example for the Modeling of Protein-Ligand Complexes: Antigen Presentation by MHC Class I (p. 179)
  • 7.1 Biochemical and Pharmacological Description of the Problem (p. 179)
  • 7.1.1 Antigenic Proteins are Presented as Nonapeptides (p. 180)
  • 7.1.2 Pharmacological Target: Autoimmune Reactions (p. 180)
  • 7.2 Molecular Modeling of the Antigenic Complex Between a Viral Peptide and a Class I MHC Glycoprotein (p. 181)
  • 7.2.1 Modeling of the Ligand (p. 181)
  • 7.2.2 Homology Modeling of the MHC Protein (p. 183)
  • 7.3 Molecular Dynamics Studies of MHC-Peptide Complexes (p. 192)
  • 7.3.1 HLA-A2--The Fate of the Complex during Molecular Dynamics Simulations (p. 192)
  • 7.3.2 HLA-B*2705 (p. 194)
  • 7.4 Analysis of Models that Emerged from Molecular Dynamics Simulations (p. 199)
  • 7.4.1 Hydrogen Bonding Network (p. 200)
  • 7.4.2 Atomic Fluctuations (p. 200)
  • 7.4.3 Solvent-Accessible Surface Areas (p. 203)
  • 7.4.4 Interaction Energies (p. 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 Protein (p. 206)
  • 7.5.1 The Design of New Ligands (p. 206)
  • 7.5.2 Experimental Validation of the Designed Ligands (p. 209)
  • 7.6 How Far Does the Model Hold? Studies on Fine Specificity of Antigene Binding to Other MHC Proteins and Mutants (p. 211)
  • 7.7 The T-Cell Receptor Comes in (p. 211)
  • 7.8 Some Concluding Remarks (p. 214)
  • Index (p. 217)

Author notes provided by Syndetics

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.

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