Available:*
Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
---|---|---|---|---|---|
Searching... | 30000010167849 | RM301.42 C65 2008 | Open Access Book | Book | Searching... |
On Order
Summary
Summary
Computational methods impact all aspects of modern drug discovery and most notably these methods move rapidly from academic exercises to becoming drugs in clinical trials... This insightful book represents the experience and understanding of the global experts in the field and spotlights both the structural and medicinal chemistry aspects of drug design. The need to 'encode' the factors that determine adsorption, distribution, metabolism, excretion and toxicology are explored, as they remain the critical issues in this area of research. This indispensable resource provides the reader with: * A rich understanding of modern approaches to docking * A comparison and critical evaluation of state-of-the-art methods * Details on harnessing computational methods for both analysis and prediction * An insight into prediction potencies and protocols for unbiased evaluations of docking and scoring algorithms * Critical reviews of current fragment based methods with perceptive applications to kinases Addressing a wide range of uses of protein structures for drug discovery the Editors have created and essential reference for professionals in the pharmaceutical industry and moreover an indispensable core text for all graduate level courses covering molecular interactions and drug discovery.
Author Notes
Robert M. Stroud is a professor at the University of California and has been a fellow of the Royal Society of Medicine (UK) since 1992 and a member of the National Academt of Sciences (US) since 2003. His prestigious career spans over 35 years and he is and has served on the scientific advisory boards of many companies and institutions including the National Cancer Institute, the Neutron Diffraction facility, Axys Pharmaceuticals, and Sunesis Phamraceuticals. Janet Finer-Moore is a Research Biologist at the University of California Her contribution to the detailed determination of the structural and chemical mechanism of a two substrate enzyme and detection of amphipathic helices in protein and gene sequences have perpetuated over 28 publications. She is a member of the AAAS, the ACA, the ACS and the Biophysical Society.
Table of Contents
Section 1 Overview | |
Chapter 1 Facing the Wall in Computationally Based Approaches to Drug DiscoveryJanet S. Finer-Moore and Jeff Blaney and Robert M. Stroud | |
1.1 The Promise, and the Problem | p. 3 |
1.2 Current Limitations in Structure-guided Lead Design | p. 5 |
1.3 Lessons in Structure-based Drug Design from Thymidylate Synthase | p. 7 |
1.3.1 Mechanism-based Inhibitors and Enzyme-catalyzed Therapeutics | p. 7 |
1.3.2 Iterative Structure-based Drug Design | p. 8 |
1.3.3 Docking, Fragments and Optimizability | p. 8 |
1.4 New Developments in Structure-based Drug-design Methods | p. 13 |
1.4.1 Fragment-based Methods | p. 13 |
1.4.2 Identifying Drug Target Sites on a Protein | p. 16 |
1.4.3 Targeting Protein-Protein Interactions | p. 17 |
1.4.4 Computational Docking to Nominated Sites | p. 18 |
1.5 Conclusion | p. 19 |
References | p. 20 |
Chapter 2 The Changing Landscape in Drug DiscoveryHugo Kubinyi | |
2.1 Introduction | p. 24 |
2.2 QSAR - Understanding Without Prediction | p. 25 |
2.3 Gene Technology - from Mice to Humans | p. 27 |
2.4 Combinatorial Library Design - Driven by Medicinal Chemistry | p. 28 |
2.5 Docking and Scoring - Solved and Unsolved Problems | p. 32 |
2.6 Virtual Screening - the Road to Success | p. 35 |
2.7 Fragment-based and Combinatorial Design - A New Challenge | p. 37 |
2.8 Summary and Conclusions | p. 38 |
References | p. 41 |
Section 2 Structure-Based Design | |
Chapter 3 Purine Nucleoside PhosphorylaseYang Zhang and Steven E. Ealick | |
3.1 Introduction | p. 49 |
3.2 Three-dimensional Structures of PNPs | p. 51 |
3.3 Related Enzymes of the PNP Family | p. 54 |
3.4 PNP Active Sites | p. 55 |
3.5 Human PNP Inhibitors | p. 58 |
3.6 Other Applications of Molecular Design to PNP | p. 62 |
3.7 Applications of Molecular Design to Enzymes Related to PNP | p. 64 |
3.8 PNP Inhibitors and Clinical Trials | p. 65 |
3.9 Conclusions and Future Directions | p. 66 |
Note Added in Proof | p. 66 |
References | p. 67 |
Chapter 4 Application and Limitations of X-Ray Crystallographic Data in Structure-Guided Ligand and Drug DesignAndrew M. Davis and Simon J. Teague and Gerard J. Kleywegt | |
4.1 Introduction | p. 73 |
4.2 Structure-guided Ligand Design and Drug Design | p. 74 |
4.3 Some Limitations in the Use of X-ray Data | p. 79 |
4.3.1 Basic Crystallography Terms | p. 79 |
4.3.2 Uncertainty in the Identity or Location of Protein or Ligand Atoms | p. 83 |
4.3.3 Effect of Crystallization Conditions | p. 86 |
4.3.4 Identification and Location of Water | p. 87 |
4.4 Macromolecular Structures to Determine Small-molecule Structures | p. 88 |
4.5 Assessing the Validity of Structure Models | p. 89 |
4.6 Summary and Outlook | p. 90 |
References | p. 91 |
Chapter 5 Dealing with Bound Waters in a Site: Do they Leave or Stay?Donald Hamelberg and J. Andrew McCammon | |
5.1 Introduction | p. 95 |
5.2 Localized Water Molecules in Binding Sites of Proteins | p. 96 |
5.3 Identifying Localized Water Molecules from Computer Simulations | p. 99 |
5.4 Calculation of Free-energy Cost of Displacing a Site-bound Water Molecule | p. 101 |
5.5 Inclusion of Explicit Water Molecules in Drug Discovery | p. 104 |
Acknowledgements | p. 106 |
References | p. 106 |
Chapter 6 Knowledge-Based Methods in Structure-Based DesignMarcel L. Verdonk and Wijnand T.M. Mooij | |
6.1 Introduction | p. 111 |
6.2 Atom-based Potentials | p. 111 |
6.3 Group-based Potentials | p. 112 |
6.4 Methodologies | p. 114 |
6.4.1 The Reference State | p. 115 |
6.4.2 Volume Corrections | p. 116 |
6.5 Applications | p. 117 |
6.5.1 Visualization and Interaction 'Hot Spots' | p. 117 |
6.5.2 Docking and Scoring | p. 118 |
6.5.3 De Novo Design | p. 120 |
6.5.4 Targeted Scoring Functions | p. 120 |
6.6 Discussion | p. 121 |
6.7 Conclusion | p. 123 |
References | p. 123 |
Chapter 7 Combating Drug Resistance - Identifying Resilient Molecular Targets and Robust DrugsCelia A. Schiffer | |
7.1 Introduction | p. 127 |
7.2 Resilient Targets and Robust Drugs | p. 128 |
7.3 Example of HIV-1 Protease: Substrate Recognition vs. Drug Resistance | p. 129 |
7.4 Implications for Future Structure-based Drug Design | p. 132 |
Acknowledgements | p. 132 |
References | p. 132 |
Section 3 Docking | |
Chapter 8 Docking Algorithms and Scoring Functions; State-of-the-Art and Current LimitationsGregory L. Warren and Catherine E. Peishoff and Martha S. Head | |
8.1 Introduction | p. 137 |
8.1.1 Binding Mode Prediction | p. 138 |
8.1.2 Virtual Screening for Lead Identification | p. 139 |
8.1.3 Potency Prediction for Lead Optimization | p. 139 |
8.2 A Brief Review of Recent Docking Evaluations | p. 140 |
8.3 What these Evaluations Tell us about the Performance of Docking Algorithms | p. 143 |
8.3.1 Binding Mode Predictions | p. 143 |
8.3.2 Virtual Screening | p. 144 |
8.3.3 Affinity Prediction | p. 145 |
8.4 How an Ideal Evaluation Data Set Might be Structured | p. 147 |
8.4.1 Binding Mode Prediction | p. 147 |
8.4.2 Virtual Screening | p. 148 |
8.4.3 Affinity Prediction | p. 148 |
8.5 Concluding Remarks | p. 149 |
8.5.1 Binding Mode Prediction | p. 149 |
8.5.2 Virtual Screening | p. 150 |
8.5.3 Rank Order by Affinity | p. 151 |
8.5.4 The State-of-the-art | p. 152 |
References | p. 153 |
Chapter 9 Application of Docking Methods to Structure-Based Drug DesignDemetri T. Moustakas | |
9.1 Introduction | p. 155 |
9.2 Docking Methods, Capabilities and Limitations | p. 156 |
9.2.1 Molecule Preparation | p. 156 |
9.2.2 Sampling Methods | p. 157 |
9.2.3 Scoring Methods | p. 160 |
9.2.4 Managing Errors in Docking | p. 162 |
9.3 How is Docking Applied to Drug Design? | p. 164 |
9.3.1 Drug Target Selection and Characterization | p. 165 |
9.3.2 Lead Compound Discovery | p. 168 |
9.3.3 Lead Compound Optimization | p. 171 |
9.4 Summary | p. 172 |
References | p. 172 |
Chapter 10 Strength in Flexibility: Modeling Side-Chain Conformational Change in Docking and ScreeningLeslie A. Kuhn | |
10.1 Introduction | p. 181 |
10.2 Background | p. 181 |
10.2.1 Improving Docking and Screening Through Side-chain Flexibility Modeling | p. 181 |
10.2.2 Enhancing Target Specificity Through Flexibility Modeling | p. 182 |
10.3 Approaches | p. 183 |
10.3.1 The State of the Art in Modeling Protein Side-chain Flexibility | p. 183 |
10.3.2 Learning from Nature: Observing Side-chain Motions Upon Ligand Binding | p. 185 |
10.4 The Future: Knowledge-based Modeling of Side-chain Motions | p. 189 |
Acknowledgements | p. 189 |
References | p. 190 |
Chapter 11 Avoiding the Rigid Receptor: Side-Chain RotamersAmy C. Anderson | |
11.1 Introduction | p. 192 |
11.2 Rotamer Libraries | p. 194 |
11.3 Successful Applications of Rotamer Libraries in Drug Design | p. 195 |
11.3.1 Aspartic Acid Protease Inhibitors | p. 195 |
11.3.2 Matrix Metalloproteinase-1 Inhibitors | p. 195 |
11.3.3 Thymidylate Synthase Inhibitors | p. 199 |
11.3.4 Protein Tyrosine Phosphatase 1B Inhibitors | p. 200 |
11.3.5 HIV Protease Drug-resistant Mutants Bound to Inhibitors | p. 201 |
11.3.6 Trypsin-benzamidine and Phosphocholine-McPC 603 | p. 201 |
11.4 Conclusions | p. 202 |
Acknowledgements | p. 202 |
References | p. 202 |
Section 4 Screening | |
Chapter 12 Computational Prediction of Aqueous Solubility, Oral Bioavailability, P450 Activity and hERG Channel BlockadeDavid E. Clark | |
12.1 Introduction | p. 207 |
12.2 Aqueous Solubility | p. 208 |
12.3 Oral Bioavailability | p. 211 |
12.4 Cytochrome P450 Activity | p. 212 |
12.5 hERG Channel Blockade | p. 215 |
12.6 Conclusions | p. 219 |
References | p. 220 |
Chapter 13 Shadows on ScreensBrian K. Shoichet and Brian Y. Feng and Kristin E.D. Coan | |
13.1 Introduction | p. 223 |
13.2 Phenomenology of Aggregation | p. 224 |
13.3 What Sort of Compounds Aggregate? | p. 227 |
13.4 Mechanism of Aggregation-based Inhibition | p. 232 |
13.5 A Rapid Counter-screen for Aggregation-based Inhibitors | p. 233 |
13.6 Biological Implications? | p. 239 |
13.7 The Spirit-haunted World of Screening | p. 239 |
Acknowledgements | p. 240 |
References | p. 240 |
Chapter 14 Iterative Docking Strategies for Virtual Ligand ScreeningAlbert E. Beuscher IV and Arthur J. Olson | |
14.1 Introduction | p. 242 |
14.2 AutoDock Background | p. 243 |
14.2.1 Scoring Function | p. 243 |
14.2.2 Search Function | p. 244 |
14.2.3 AutoDockTools | p. 244 |
14.2.4 AutoDockTools Analysis | p. 245 |
14.3 Diversity-based Virtual Screening Studies | p. 246 |
14.3.1 AICAR Transformylase | p. 246 |
14.3.2 Protein Phosphatase 2C | p. 246 |
14.4 Comparison with Existing VLS Strategies | p. 253 |
14.4.1 Hierarchical VLS | p. 256 |
14.4.2 Monolithic VLS Strategy | p. 258 |
14.5 Other AutoDock VLS Studies | p. 259 |
14.5.1 Acetylcholine Esterase Peripheral Anionic Site | p. 259 |
14.5.2 Human P2Y[subscript 1] Receptor | p. 260 |
14.6 Diversity-based vs. Issues | p. 260 |
14.6.1 Library Choice | p. 260 |
14.6.2 Similarity Search | p. 261 |
14.6.3 Apo Versus Ligand-bound Docking Models | p. 262 |
14.6.4 Binding Site Choices | p. 263 |
14.7 Future Work | p. 264 |
References | p. 264 |
Chapter 15 Challenges and Progresses in Calculations of Binding Free Energies - What Does it Take to Quantify Electrostatic Contributions to Protein-Ligand Interactions?Mitsunori Kato and Sonja Braun-Sand and Arieh Warshel | |
15.1 Introduction | p. 268 |
15.2 Computational Strategies | p. 269 |
15.2.1 Free-energy Perturbation, Linear Response Approximation and Potential of Mean Force Calculations by All-atom Models | p. 269 |
15.2.2 Proper and Improper Treatments of Long-range Effects in All-atom Models | p. 273 |
15.2.3 Calculations of Electrostatic Energies by Simplified Models | p. 274 |
15.3 Calculating Binding Free Energies | p. 277 |
15.3.1 Studies of Drug Mutations by FEP Approaches | p. 277 |
15.3.2 Evaluation of Absolute Binding Energies by the LRA and LIE Approaches | p. 278 |
15.3.3 Using Semi-macroscopic and Macroscopic Approaches in Studies of Ligand Binding | p. 279 |
15.3.4 Protein-protein Interactions | p. 281 |
15.4 Challenges and New Advances | p. 282 |
15.5 Perspectives | p. 285 |
Acknowledgement | p. 285 |
References | p. 285 |
Section 5 Fragment-Based Design | |
Chapter 16 Discovery and Extrapolation of Fragment Structures towards Drug DesignAlessio Ciulli and Tom L. Blundell and Chris Abell | |
16.1 Structure-based Approaches to Drug Discovery | p. 293 |
16.2 Properties of Molecular Fragments | p. 294 |
16.3 From Molecular Fragments to Drug Leads | p. 296 |
16.3.1 Fragment Growing | p. 296 |
16.3.2 Fragment Linking | p. 297 |
16.3.3 Fragment Assembly | p. 299 |
16.4 Screening and Identification of Fragments | p. 300 |
16.5 X-ray Crystallography for Fragment-based Lead Identification | p. 301 |
16.6 NMR Spectroscopy | p. 302 |
16.6.1 Protein-based Methods: Structure-activity Relationship by NMR | p. 302 |
16.6.2 Ligand-based Methods | p. 303 |
16.7 Mass Spectrometry | p. 306 |
16.7.1 Covalent Mass Spectrometric Methods | p. 306 |
16.7.2 Non-covalent Mass Spectrometric Methods | p. 307 |
16.7.3 Looking at the Protein or the Ligand | p. 308 |
16.8 Thermal Shift | p. 309 |
16.9 Isothermal Titration Calorimetry | p. 309 |
16.10 Surface Plasmon Resonance | p. 310 |
16.11 Concluding Remarks | p. 311 |
Acknowledgements | p. 311 |
References | p. 311 |
Chapter 17 A Link Means a Lot: Disulfide Tethering in Structure-Based Drug DesignJeanne A. Hardy | |
17.1 Introduction: What is Disulfide Tethering? | p. 319 |
17.2 Success of Native Cysteine Tethering | p. 323 |
17.3 Role of Structure in Engineered-cysteine Tethering | p. 325 |
17.4 Cooperative Tethering | p. 328 |
17.5 Extended Tethering | p. 330 |
17.6 Breakaway Tethering | p. 333 |
17.7 Discovery of Novel Allosteric Sites with Tethering | p. 335 |
17.8 Tethering as a Validation Tool | p. 339 |
17.9 Tethering vs. Traditional Medicinal Chemistry | p. 340 |
17.10 Tethering in Structural Determination | p. 341 |
17.11 The Challenge of Covalency | p. 342 |
17.12 Hydrophobic Binders | p. 343 |
17.13 Conclusions: The Future of Tethering | p. 344 |
References | p. 345 |
Chapter 18 The Impact of Protein Kinase Structures on Drug DiscoveryChao Zhang and Sung-Hou Kim | |
18.1 Introduction | p. 349 |
18.2 The Hinge Region and the Concept of Kinase Inhibitor Scaffold | p. 351 |
18.3 High-throughput Crystallography for the Discovery of Novel Scaffolds | p. 353 |
18.3.1 High Potency-High Specificity-High Molecular (H3) Weight Screening | p. 353 |
18.3.2 Low Potency-Low Specificity-Low Molecular Weight (L3) Screening | p. 354 |
18.4 The Gatekeeper Residue and the Selectivity Pocket | p. 355 |
18.5 The Conformational States of the DFG Motif and the Opening of the Back Pocket | p. 357 |
18.6 Allosteric Inhibitors, Non-ATP Competitive Inhibitors, and Irreversible Inhibitors | p. 359 |
18.7 Discovering Kinase Inhibitors in a 500-Dimensional Space | p. 360 |
Acknowledgement | p. 361 |
References | p. 361 |
Subject Index | p. 366 |