Structural biology in drug discovery: methods, techniques, and practices

Cover......Page 1
Title Page......Page 5
Copyright Page......Page 6
Contents......Page 9
List of Contributors......Page 24
Preface......Page 29
Part I Overview, Concepts, and Approaches......Page 31
  1.1  Introduction......Page 33
  1.2  The Expanding Toolbox of Structural Biology for Drug Discovery......Page 35
  1.3  The Various Uses of Structural Biology in Drug Discovery......Page 40
  1.4  Evolving Drugs and Targets......Page 42
  1.5  Current Trends and Perspectives......Page 43
  References......Page 44
  2.1 Introduction......Page 53
  2.2 Views on Target Druggability......Page 54
  2.3 In Silico Methods for Druggability Assessment of Targets with Well-defined Pockets......Page 55
   2.3.1 Binding Site Identification......Page 56
   2.3.2 Selection of Descriptors and Datasets for Method Development......Page 58
   2.3.3 Development of Druggability Models......Page 59
   2.3.4 Druggability Prediction via Alternative Methods......Page 63
  2.4 Experimental Methods for Druggability Assessment......Page 64
  2.5 A Challenge for Druggability Predictions: Protein–Protein Interactions......Page 67
    2.5.1.1 Computational Solvent Mapping......Page 69
    2.5.1.2 Molecular Dynamics and Monte Carlo Simulations......Page 70
    2.5.1.3 Hot Spot Identification......Page 72
   2.5.2 Druggability Assessment of Protein–Protein Interaction Targets......Page 73
  2.6 Perspective......Page 75
  References......Page 76
  3.1  Introduction......Page 83
   3.2.1  Disease Mechanisms Call for the Need of A Systematic Targeting Approach......Page 84
   3.2.2  Multiple Ways to Adopt A Multi-target Approach with Low-Affinity Binders......Page 85
   3.2.3  Polypharmacology Allows for Repurposing of Marketed Drugs and Drug Rescue......Page 86
  3.3  Computer-Aided Approaches for Profiling Bioactivities of Ligands......Page 87
    3.3.1.2 Ligand-Based Pharmacophore-Based Models for Target Prediction Not only two-dimensional......Page 88
    3.3.1.3 Identification of Privileged Ligand Scaffolds and Structure–Selectivity Cliffs......Page 89
    3.3.2.1 Chemogenomics for All-Against-All Bioactivity Prediction......Page 90
    3.3.2.2 Proteochemometric Modeling Combines Protein–Ligand Space in a Predictive Statistical Method......Page 91
    3.3.3.1 Cavity Detection Methods Locate Probable Ligand Binding Sites......Page 92
    3.3.3.2 Cavity Description Methods Simplify Protein Structure Description for Fast Binding......Page 93
    3.3.3.3 Similarity Search Methods......Page 94
    3.3.3.5 Protein-Based Pharmacophore Modeling......Page 95
    3.3.3.7 Post-Processing Docking Data to Complement Scoring Functions......Page 96
    3.3.3.8 Knowledge-Based Structural Databases to Navigate Protein–Ligand Interaction Space......Page 97
  3.4  Applications......Page 98
   3.4.3  Target Prediction in Chemical Genetics Using SEA......Page 99
   3.4.6  Prospective Prediction of Cross‐Reaction Protein Kinase Inhibitors Using Binding Site Comparison Method SiteAlign......Page 100
  3.5  Conclusion......Page 101
  References......Page 102
  4.2 The Evolution of FBLD......Page 109
  4.3 The FBLD Process......Page 111
   4.4.1 Step A: Chemoinformatics Selection......Page 112
  4.5 Maintaining a Fragment Library......Page 113
   4.6.1 Nuclear Magnetic Resonance (NMR)......Page 114
   4.6.2 Surface Plasmon Resonance (SPR)......Page 117
   4.6.7 Isothermal Titration Calorimetry (ITC)......Page 118
  4.7 Integrating Fragments with Other Compounds......Page 119
  4.8 Validating Fragment Hits: Comparing Methods......Page 120
  4.10 Determining Structures of Fragment: Protein Complexes......Page 121
  4.11 Fragment Evolution......Page 122
  References......Page 126
    5.2.1.2 Commercial ITC Instrument History......Page 129
    5.2.1.4 The ITC Experiment......Page 130
    5.2.1.5 Measurement of Thermodynamic Parameters......Page 131
    5.2.1.6 Benefits of Applying Thermodynamic Measurements in Hit to Lead......Page 133
    5.2.1.7 Exploitation of Measured Thermodynamics......Page 134
    5.3.1.1 Introduction......Page 137
    5.3.1.3 General SPR Instrumentation......Page 138
    5.3.1.4 The SPR Experiment......Page 139
    5.3.1.7 Radioligand Binding......Page 143
    5.3.1.8 Residence Times......Page 144
    5.3.1.10 Protein Structure and Dynamics......Page 146
    5.3.1.11 Correlating Kinetic and Structural Data in Hit-to-Lead Programs......Page 147
    5.3.1.12 Correlating Structural and Thermodynamic Data in Hit-to-Lead Programs......Page 148
  5.4 Summary......Page 151
  References......Page 152
   6.1.1 Experimental Data Highlight the Complexity of Allosteric Events, Beyond MWC or KNF Models: Bacterial Chaperonin GroEL as a Paradigm......Page 155
   6.1.2 New Views: Allostery as Population Shift Between Preexisting Conformers or Reconfiguration Along Preexisting Soft Path......Page 157
  6.2 Computational Methods......Page 158
   6.2.1 Gaussian Network Model (GNM): Simplest ENM......Page 159
   6.2.2 Anisotropic Network Model (ANM) Theory and Assumptions......Page 161
    6.3.1.2 Leucine Transporter (LeuT)......Page 162
   6.3.2 Allosteric Drug Binding Sites Inferred from ENM Analysis: Application to HIV‐1 Reverse Transcriptase......Page 163
   6.3.3 Pharmacophore Modeling from Druggability Simulations......Page 164
   6.4.1 Allosteric Cooperativity and Cellular Effects......Page 166
  References......Page 168
Part II Tools......Page 173
  7.1 Biophysical Methods in Drug Discovery......Page 175
   7.2.1 Recovering Active Enzyme for Ligand Co-crystallization: The Lysosomal Cysteine Protease Cathepsin S......Page 176
  7.3 The Role of Biophysical Methods in the Optimization of Protein Crystallization......Page 180
   7.3.1 Biophysical Methods Performed in Solution (Liquid Sample)......Page 181
   7.4.1 Optimizing Detergent Conditions for Membrane Proteins: The Mitochondrial ‐Oxidation Pacemaker Carnitine Palmitoyltransferase 2......Page 184
   7.4.2 Comparison of AUC to SEC for the Characterization of CPT-2......Page 188
  7.5 Outlook and Concluding Remarks: Requirements for Upcoming Biophysical Methods......Page 189
  References......Page 190
   8.2.1 Protein Crystallography in a Nutshell......Page 195
   8.2.2 Application of X‐Ray Analysis to the Study of Ligand Binding......Page 196
   8.3.2 Validation of Ligand Binding......Page 197
   8.3.5 Complementary Techniques: NMR, SPR, ITC, DSF, SAXS, and EM......Page 198
  8.4 Preparing Protein for Successful Crystallization......Page 199
   8.4.2 Protein Production......Page 200
   8.4.3 Protein Purification and Quality Assessment......Page 201
   8.5.2 Crystallization Screening......Page 202
   8.5.3 Automation of Crystallization Setup and Imaging......Page 203
   8.5.5 What If the Protein Does Not Crystallize?......Page 204
   8.6.1 Introduction to Ligands and Compound Selection......Page 205
   8.6.3 Hot Spots, Potency, and IC50’s......Page 206
   8.6.6 Pitfalls of Detecting Ligand Binding in a Protein Crystal......Page 207
   8.7.2 Advantages and Potential of Soaking Ligands......Page 208
   8.7.3 Ligand Concentrations......Page 209
   8.7.4 Exemplary Soaking Can Guide Drug Discovery......Page 210
   8.7.7 Case Study: Complex Structures of HdmX with p53 Peptide Analogs......Page 212
  References......Page 214
   9.1.2  Membrane Protein Biochemistry......Page 217
  9.2  Membrane Protein Production......Page 219
   9.2.3  Insect Cell Expression Systems......Page 221
   9.3.1  Detergent in Membrane Protein Biochemistry......Page 222
   9.3.4  Amphipols......Page 224
   9.4.1  Overview of Protein Sample Preparation......Page 225
   9.4.2  Criteria for Selecting Suitable Protein Targets......Page 226
   9.4.3  Quality Control of Purified MPs......Page 227
  9.5  Membrane Protein Crystallization......Page 228
   9.5.1  In Surfo Crystallization......Page 229
   9.5.3  Bicelle Crystallization......Page 230
   9.5.5  Effect of Detergent and Lipid in Crystallization......Page 231
   9.5.8  Evaluating Crystallization Conditions......Page 232
   9.6.2  X-Ray Free-Electron Laser......Page 233
  9.7  Conclusion and Outlooks......Page 234
  References......Page 235
  10.1 Introduction......Page 241
   10.2.2 MX for SBDD in a Nutshell: A Vast Combinatorial Search......Page 242
   10.2.3 The High-Throughput (HT) Imperative in MX-for-SBDD Workflows......Page 243
  10.3 Baseline: The “Human, All Too Human” Workflow of Early MX for SBDD......Page 244
  10.4 First Wave of Automation Toward High-Throughput Operation: Robotics Without Refactoring......Page 245
  10.5 Second Wave of Automation: The Twin Tracks of In Situ Crystallography and Microcrystallography......Page 247
    10.5.1.3 Data Collection from Multiple Small Crystals......Page 248
    10.5.1.4 Robotics Unbound: Take 1......Page 249
    10.5.1.5 Related Instrumental Developments......Page 250
    10.5.2.2 Hard Limits on MX Diffraction Measurements from Fundamental Principles......Page 251
    10.5.2.3 Aiming for the Highest Possible S/N Ratio in MX Experiments......Page 252
   10.5.3 Convergence Toward Multi-crystal Data Collection......Page 258
    10.5.3.1 Microcrystallography in Action: A Chronicle of Early GPCR Structure......Page 259
    10.5.3.3 Evolution of Processing Methods for Multi-crystal Datasets......Page 261
    10.6.1.1 Sample Delivery Issues......Page 262
   10.6.3 Pros and Cons of SMX vs. SFX......Page 263
   10.6.4 The Heart of the Matter for Serial Crystallography: The Humpty Dumpty Problem......Page 264
    10.7.1.2 High-Quality Reference Model......Page 265
    10.7.2.3 Eliciting Difference Density for Ligand Detection......Page 266
    10.7.2.5 Final Refinement of Ligand–Target Complex......Page 267
   10.7.4 Auto-processing at Synchrotrons: Toward Specialized Cloud Computing?......Page 268
  10.8 Conclusions and Outlook: Whither HTMX for Drug Discovery?......Page 269
  References......Page 270
  11.1 Introduction......Page 283
   11.3.1 Dataset Quality: Resolution......Page 286
   11.3.2 Dataset Quality: Other Quality Indicators of Diffraction Data......Page 287
    11.3.3.2 Agreement of the Model with the Experimental Data: B-Factors......Page 288
    11.3.3.3 Agreement of the Model to the Experimental Data: Electron Density Fit......Page 289
    11.3.4.2 Stereochemical Accuracy of the Model: Ramachandran Plot and Peptide Planarity......Page 291
    11.3.4.3 Stereochemical Accuracy of the Model: van der Waals Clashes and Hydrogen Bonding Networks......Page 292
  11.4 Low-Resolution Structures......Page 293
  11.5 Possible Influence of Crystal Packing......Page 294
  11.6 Software Tools......Page 295
  11.7 Analysis of Quality Metrics......Page 298
  References......Page 301
  12.2 Differences in Characteristics of X‐Rays and Neutrons......Page 307
  12.5 Current Status of Neutron Protein Crystallography......Page 308
    12.6.1.1 Sample Requirement for Neuron Crystallography......Page 309
    12.6.1.4 Consideration of Chemical Instability During the Crystallization Experiment......Page 310
    12.6.3.2 Neutron Diffraction Data Collection......Page 311
    12.6.3.3 Data Processing of the Neutron Diffraction Dataset......Page 312
    12.7.1.1 Hydrogen Bonding Interaction......Page 313
   12.7.2 Hydration Structure......Page 314
    12.8.1.2 Neutron Structure Analysis of HIV-1 Protease......Page 315
    12.8.2.1 Serine Proteases as a Drug Target Protein......Page 316
   12.9.1 Determination of the Ionization State of Active Site Residues......Page 317
   12.9.4 Improvement of Protein–Protein Association......Page 318
  References......Page 319
  13.1 Introduction......Page 325
   13.2.1 Introduction......Page 326
   13.2.2 The NMR Observables......Page 327
   13.2.3 From NMR Observables to 3D Models......Page 329
   13.2.4 Current Developments......Page 330
    13.3.2.1 Backbone Assignment......Page 331
    13.3.2.2 Side-Chain Assignment......Page 332
    13.3.4.2 Classical 15N, 15N─13C, and 15N─13C─2H Approaches......Page 333
  13.4 NMR and Dynamic Aspects......Page 334
   13.5.1 Introduction......Page 337
   13.5.2 Probing Disorder: Relaxation Measurements and Interpretation......Page 338
   13.5.3 Beyond the Rotational Diffusion Limit......Page 339
   13.6.2 General Presentation of IDPs......Page 340
   13.6.3 NMR Techniques for IDP......Page 341
   13.6.4 Interpreting IDP Spectra......Page 342
   13.7.1 Solid-State NMR......Page 343
   13.7.2 Membrane Proteins......Page 344
   13.8.2 Sample Preparation......Page 345
    13.8.3.1 Quality Control......Page 346
    13.8.3.3 2D Spectroscopy on Unlabeled Samples......Page 347
  13.9 Conclusion......Page 348
  References......Page 349
  14.1 Introduction......Page 355
   14.2.1 Target Validation......Page 357
   14.2.2 Hit Identification and Validation......Page 358
   14.2.3 Hit-to-Lead Optimization......Page 362
  14.3 Lead Optimization......Page 363
   14.4.1 Membrane Proteins......Page 365
   14.4.3 Intrinsically Disordered Proteins......Page 367
  References......Page 370
   15.2.1 Extent of the Construct......Page 377
   15.2.2 Electron Density Fit......Page 378
   15.2.5 Proteome Coverage......Page 379
   15.3.1 Homology Modeling Process......Page 381
   15.3.3 Model Quality......Page 382
    15.3.5.3 Backbone Deviations......Page 383
  15.4 Prediction of Protein–Ligand Interactions......Page 384
  15.5 Future Perspective......Page 387
  References......Page 388
Part III Structure-Based Discovery in Some Important or Promising Targets and Therapeutic Families......Page 393
  16.1 Protein Kinases and Their Structural Elements: A Dynamic Landscape for Drug Discovery......Page 395
  16.2 The Challenge of Selectivity and Drug Resistance: Design and Discovery of Afatinib, the First Irreversible Protein Kinase Inhibitor Approved for Cancer Treatment......Page 400
  16.3 When Structural Biology Drives Chemistry to Therapeutic Breakthrough: The Vemurafenib Case History......Page 405
  16.4 Second-Generation Anaplastic Lymphoma Kinase Inhibitors: The Discovery of the First-in-Class Drug Ceritinib......Page 407
  16.5 The Discovery of Type II Inhibitor Ponatinib: A Milestone in the Struggle Against the ABL Gatekeeper Resistant Mutation T315I......Page 410
  16.6 Protein Kinase Inhibitor Drug Discovery and Structural Biology: Future Perspective......Page 413
  References......Page 414
   17.1.2 The Blood Coagulation Cascade......Page 425
   17.1.3 General Background About Serine Proteases......Page 426
   17.1.4 Indirect Coagulation Inhibitors......Page 427
   17.2.2 Exosite-I......Page 428
   17.2.6 Hirudin-Derived Thrombin Inhibitors......Page 430
   17.2.7 Thrombin Active Site Inhibitors......Page 431
   17.2.10 The Quest for Orally Available, Non-prodrug Direct Thrombin Inhibitors......Page 432
   17.3.1 Factor Xa Crystal Structure and Active Site......Page 434
    17.3.4.1 DX-9065a......Page 435
    17.3.5.1 Reversed Binding Mode of Chlorobenzothiophene Substituents......Page 437
    17.3.5.2 Apixaban......Page 438
    17.3.5.4 The Secret Behind Chloro-aryl Binding in the S1 Pocket......Page 439
    17.3.5.5 Dual Thrombin/FXa Inhibitors......Page 440
  17.4 Factor VIIa (FVIIa)......Page 441
   17.4.3 Discovery of Direct FVIIa Inhibitors......Page 442
  17.6 Factor IXa......Page 444
  17.8 Impact of Structure‐Based Drug Design......Page 445
  References......Page 446
  18.1 Introduction......Page 453
  18.2 Acetylation/Deacetylation......Page 454
    18.2.1.1 Metal Ion-Dependent HDACs......Page 455
    18.2.1.2 Sirtuins......Page 457
   18.2.2 Bromodomains......Page 459
  18.3 Methylation/Demethylation......Page 460
   18.3.1 Protein Methyltransferases......Page 461
    18.3.1.2 Inhibition via Substrate Competitive Binding: EHMT2......Page 462
    18.3.1.3 Inhibition Through an Allosteric Mechanism: PRMT3......Page 465
   18.3.2 Demethylases......Page 466
    18.3.2.2 JmjC Demethylases......Page 467
    18.3.3.2 The “Royal Family”......Page 468
  18.5 The Future of Epigenetic Drug Discovery......Page 470
  References......Page 471
  19.2 Topology and Classes of GPCRs......Page 479
  19.3 GPCR X-Ray Crystal Structures......Page 480
  19.4 Class A GPCR Small Ligand Binding Sites and Druggability......Page 481
  19.5 Class B GPCR X-Ray Crystal Structures......Page 485
  19.6 Class C GPCRs and Allosteric Modulators......Page 488
  19.7 GPCR Activation......Page 489
   19.8.1 Virtual Screening......Page 491
   19.8.2 Fragment-Based Drug Design......Page 498
   19.8.4 A Case History of Full SBDD for GPCRs: “High-End” Design......Page 500
  References......Page 503
  20.2 Detection and Analysis of PPIs......Page 509
   20.3.2 Fragment-Based Screening Methods......Page 512
   20.4.1 Inhibitors of MDM2–p53: A Breakthrough in PPI Targeting......Page 513
    20.4.1.1 Structure-Based Design of Spirooxindoles as MDM2 Inhibitors......Page 514
    20.4.1.3 Other Opportunities for Inhibition......Page 515
   20.4.2 Mimicry of Smac Peptide, IAP Antagonists......Page 516
    20.4.4.1 Bromodomains......Page 517
    20.4.4.2 Methyl-lysine Reader Domains......Page 520
   20.4.5 Competitive Antagonists: IL-2 Receptor......Page 522
   20.6.1 Protein Degradation......Page 524
   20.6.2 PPI Stabilizers......Page 525
  References......Page 526
  21.1 Introduction......Page 533
   21.2.1 Denaturing MS......Page 535
  21.3 Middle-Up mAb Analysis......Page 536
  21.4 Bottom-Up Peptide Mapping for Primary Structure Assessment......Page 538
   21.5.2 Middle-Down mAb Analysis......Page 540
  21.6 Hydrogen/Deuterium Exchange Mass Spectrometry......Page 543
  21.8 From Optimized Antibodies (OptimAbs) to Optimized Antibody–Drug Conjugates (OptimADCs)......Page 544
  Acknowledgments......Page 545
  References......Page 546
Part IV Challenges and New Frontiers......Page 551
   22.1.2 Structure and Function of HIV-1 Protease......Page 553
   22.1.4 Viral Resistance to HIV-1 Protease Inhibitors......Page 554
   22.2.3 NS3/4A Protease as an Antiviral Target......Page 558
  22.3 Protein Dynamics Is Key to Molecular Recognition......Page 560
   22.3.2 Protein Dynamics Is Often Neglected in Drug Design......Page 561
   22.4.1 Drug Target’s Function and Protein Dynamics......Page 562
   22.4.2 Integrating Dynamics into Drug Discovery While Avoiding Resistance: Dynamic Substrate Envelope......Page 563
  22.5 Future Perspective: Integrating Evolution and Conformational Dynamics into Drug Design......Page 566
  References......Page 567
   23.2.2 Epidemiologic Data and Treatment......Page 575
   23.2.3 Current Problems and Risks: The Challenge of a Tuberculosis‐Free World......Page 576
   23.3.3 The Structural Proteome of Mycobacterium tuberculosis......Page 578
   23.4.1 Target Identification......Page 579
    23.4.3.1 Isoniazid and Ethionamide......Page 580
    23.4.3.3 RNA Polymerase and Regulatory Proteins......Page 584
    23.4.3.4 Fluoroquinolones Target DNA Gyrase......Page 585
    23.4.3.5 Examples of TB Drugs in Development and Their Targets......Page 587
  23.5 Conclusion and Perspectives......Page 588
  References......Page 589
 Chapter 24 Using Crystal Structures of Drug-Metabolizing Enzymes in Mechanism-Based Modeling for Drug Design......Page 597
  24.1 Structure-Based Modeling of Cytochrome P450s......Page 599
   24.1.1 CYP3A4......Page 602
   24.1.3 CYP2D6......Page 604
  24.2 Other Phase I Drug-Metabolizing Enzymes......Page 605
  24.3 Phase II Drug-Metabolizing Enzymes......Page 606
  24.4 Interplay Between Metabolism and Inhibition......Page 607
  24.5 Future Directions......Page 609
  References......Page 611
   25.1.1 Introducing Intrinsically Disordered Proteins......Page 617
   25.1.2 Techniques for Structural Characterization of IDPs and IDPRs......Page 618
   25.1.3 Abundance of IDPs and IDPRs and Their Biological Functions......Page 622
   25.1.4 IDPs/IDPRs in Human Diseases......Page 623
    25.2.2.1 Major Categories of Protein-Directed Drugs......Page 624
   25.3.3 Drugs for IDPs and IDPRs......Page 625
  25.4 Disorder-Based Rational Drug Design......Page 626
  25.5 Direct Targeting of IDPs/IDPRs......Page 627
  25.7 Targeting Intrinsically Disordered Structural Ensembles: -Synuclein as an Illustration......Page 629
  25.8 Targeting Aggregating IDPs......Page 631
  25.9 Conclusions......Page 632
  References......Page 633
  26.2 The Resolution Revolution......Page 643
   26.4.1 Biochemistry......Page 644
   26.4.2 Screening the Conditions to Obtain an Optimal Sample......Page 645
   26.4.3 Sample Vitrification......Page 646
   26.4.5 Data Collection......Page 647
   26.4.6 Increasing the Image Contrast......Page 649
    26.4.6.1 Choosing a Suitable Detector......Page 650
   26.4.7 Three-Dimensional Reconstruction and Map Interpretation......Page 652
   26.5.2 Visualizing Ligands in High-Resolution Cryo-EM Maps of TRPV1......Page 654
   26.5.3 Visualizing Large Complexes Without Symmetry: The Ribosome......Page 655
   26.5.5 Cryo-EM and SBDD: Antimalarial Mefloquine Derivatives......Page 656
  References......Page 657
  27.1 Introduction: Overview of X-Ray Free-Electron Lasers......Page 663
  27.2 Comparison with Conventional X-Ray Crystallography......Page 664
   27.3.1 Photosystem I Structure at 8.5 Å: Proof of Concept......Page 666
   27.3.3 Single-Virus-Particle Imaging: Mimivirus at 30 nm......Page 667
   27.3.4 Cathepsin B Structure at 2.1 Å: First Unknown Structural Insight and Use of In Vivo Grown Crystals......Page 668
   27.3.5 Serotonin Receptor Structure at 2.8 Å: Room-Temperature Conformation Different from the Cryogenic Structure Solved at Synchrotron......Page 669
   27.3.7 Photosystem I and Ferredoxin: First Proof of Principle for Time-Resolved Studies......Page 670
   27.3.8 Photosystem II: Unraveling the Water Oxidation Process – An Attempt to Make a Molecular Movie......Page 671
  27.4 Challenges in XFELs......Page 672
  27.6 Conclusion......Page 674
  References......Page 675
Index......Page 679
EULA......Page 691