Computational and structural approaches to drug discovery : ligand-protein interactions

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.

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

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