Computational and Structural Approaches to Drug Discovery

Preface: Section 1 Overview: 1 Facing the Wall in Computationally Based Approaches to Drug Discovery;

1.1 The promise, and the problem;

1.2 Current limitations in structure-guided lead design;

1.3 Lessons in structure-based drug design from thymidylate synthase research;

1.3.1 Mechanism-based inhibitors and enzyme-catalyzed therapeutics;

1.3.2 Iterative structure-based drug design;

1.3.3 Docking, fragments and optimizability;

1.4 New developments in structure-based drug-design methods;

1.4.1 Fragment-based methods;

1.4.2 Identifying drug target sites on a protein;

1.4.3 Targeting protein-protein interactions;

1.4.4 Computational docking to nominated sites;

1.5 Conclusion;

References;

2 The Changing Landscape in Drug Discovery;

2.1 Introduction;

2.2 QSAR - understanding without prediction;

2.3 Gene technology - from mice to humans;

2.4 Combinatorial library design - driven by medicinal chemistry;

2.5 Docking and scoring - solved and unsolved problems;

2.6 Virtual screening - the road to success;

2.7 Fragment-based and combinatorial design - a new challenge;

2.8 Summary and conclusions;

References;

Section 2: Structure-Based Design;

3 Purine Nucleoside Phosphorylase;

3.1 Introduction;

3.2 Three-dimensional structures of PNPs;

3.3 Related enzymes of the PNP family;

3.4 PNP active sites;

3.5 Human PNP inhibitors;

3.6 Other applications of molecular design to PNP;

3.7 Applications of molecular design to enzymes related to PNP;

3.8 PNP inhibitors and clinical trials;

3.9 Conclusions and future directions;

References;

4 Application and Limitations of X-Ray Crystallographic Data in Structure-Based Ligand and Drug Design;

4.1. Introduction;

4.2. Structure-based ligand design and drug design;

4.3 Some limitations in the use of X-ray data;

4.3.1 Basic crystallography terms;

4.3.2 Uncertainty in the identity or location of protein or ligand atoms;

4.3.3 Effect of crystallization conditions;

4.3.4 Identification and location of water;

4.4 Macromolecular structures to determine small-molecule structures;

4.5 Assessing the validity of structure models;

4.6 Summary and Outlook;

References;

5 Dealing with Bound Waters in a Site: Do they Leave or Stay?;

5.1 Introduction;

5.2 Localized water molecules in binding sites of proteins;

5.3 Identifying localized water molecules from computer simulations;

5.4 Calculation of free-energy cost of displacing a site-bound water molecule;

5.5 Inclusion of explicit water molecules in drug discovery;

Acknowledgements;

References;

6 Knowledge-Based Methods in Structure-Based Design;

6.1 Introduction;

6.2 Atom-based potentials;

6.3 Group-based potentials;

6.4 Methodologies;

6.4.1 The reference state;

6.4.2 Volume corrections;

6.5 Applications;

6.5.1 Visualization and interaction 'hot spots';

6.5.2 Docking and scoring;

6.5.3 De novo design;

6.5.4 Targeted scoring functions;

6.6 Discussion;

6.7 Conclusion;

References;

7 Combating Drug Resistance -Identifying Resilient Molecular Targets and Robust Drugs;

7.1 Introduction;

7.2 Resilient targets and robust drugs;

7.3 Example of HIV-1 protease: substrate recognition vs. drug resistance;

7.4 Implications for future structure-based drug design;

Acknowledgements;

References;

Section 3: Docking;

8 Docking Algorithms and Scoring Functions;

State-of-the-Art and Current Limitations;

8.1 Introduction;

8.1.1 Binding mode prediction;

8.1.2 Virtual screening for lead identification;

8.1.3 Potency prediction for lead optimization;

8.2 A brief review of recent docking evaluations;

8.3 What these evaluations tell us about the performance of docking algorithms;

8.3.1 Binding mode prediction;

8.3.2 Virtual screening;

8.3.3 Affinity prediction;

8.4 How an ideal evaluation data set might be structured;

8.4.1 Binding mode prediction;

8.4.2 Virtual screening;

8.4.3 Affinity prediction;

8.5 Concluding remarks;

8.5.1 Binding mode prediction;

8.5.2 Virtual screening;

8.5.3 Rank order by affinity;

8.5.4 The state-of-the-art;

References;

9 Application of Docking Methods to Structure-Based Drug Design;

9.1 Introduction;

9.2.1 Molecule preparation;

9.2.2 Sampling methods;

9.2.3 Scoring methods;

9.2.4 Managing errors in docking;

9.4 Docking methods, capabilities and limitations;

9.5 Summary;

References;

10 Strength in Flexibility: Modeling Side-Chain Conformational Change in Docking and Screening;

10.1 Introduction;

10.2 Background;

10.2.1 Improving docking and screening through side-chain flexibility modeling;

10.2.2 Enhancing target specificity through flexibility modeling;

10.3 Approaches;

10.3.1 The state of the art in modeling protein side-chain flexibility;

10.3.2 Learning from nature: observing side-chain motions upon ligand binding;

10.4 The future: knowledge-based modeling of side-chain motions;

Acknowledgements;

References;

11 Avoiding the rigid receptor: side chain rotamers;

11.1 Introduction;

11.2 Rotamer libraries;

11.3 Successful applications of rotamer libraries in drug design;

11.3.1 Aspartic acid protease inhibitors;

11.3.2 Matrix metalloproteinase-1 inhibitors;

11.3.3 Thymidylate synthase inhibitors;

11.3.4 Protein tyrosine phosphatase 1B inhibitors;

11.3.5 HIV protease drug-resistant mutants bound to inhibitors;

11.3.6 Trypsin-benzamidine and phosphocholine-McPC 603;

11.4 Conclusions;

Acknowledgements;

References;

Section 4: Screening;

12 Computational Prediction of Aqueous Solubility, Oral Bioavailability, P450 Activity and hERG Channel Blockade;

12.1 Introduction;

12.2 Aqueous solubility;

12.3 Oral bioavailability;

12.4 P450 activity;

12.5 hERG channel blockade;

12.6 Conclusions;

References;

13 Shadows on Screens;

13.1 Introduction;

13.2 Phenomenology of aggregation;

13.3 What sort of compounds aggregate?;

13.4 Mechanism of aggregation-based inhibition;

13.5 A rapid counter-screen for aggregation-based inhibitors;

13.6 Biological implications?;

13.7 The spirit-haunted world of screening;

Acknowledgements;

References;

14 Iterative Docking Strategies for Virtual Ligand Screening;

14.1 Introduction;

14.2 AutoDock background;

14.2.1 Scoring function;

14.2.2 Search function;

14.2.3 AutoDockTools;

14.2.4 AutoDockTools analysis;

14.3 Diversity-based virtual ligand screening;

14.3.1 AICAR transformylase;

14.3.2 Protein phosphatase 2C;

14.4 Comparison with existing VLS strategies;

14.4.1 Hierarchical VLS;

14.4.2 Monolithic VLS strategy;

14.5 Other AutoDock VLS strategies;

14.5.1 Acetylcholine esterase peripheral anionic site;

14.5.2 Human P2Y1 receptor;

14.6 Diversity-based VLS issues;

14.6.1 Library choice;

14.6.2 Similarity search;

14.6.3 Apo versus ligand-bound docking models;

14.6.4 Binding site choices;

14.7 Future work;

References;

15Challenges and Progresses in Calculations of Binding Free Energies;

What Does it Take to Quantify Electrostatic Contributions to Protein-Ligand Interactions;

15.1 Introduction;

15.2 Computational strategies;

15.2.1 Free-energy perturbation, linear response approximation and potential of mean force calculations by all-atom models;

15.2.2 Proper and improper treatments of long-range effects in all-atom models;

15.2.3 Calculations of electrostatic energies by simplified models;

15.3 Calculating binding free energies;

15.3.1 Studies of drug mutations by FEP approaches;

15.3.2 Evaluation of absolute binding energies by the LRA and LIE approaches;

15.3.3 Using semi-macroscopic and macroscopic approaches in studies of ligand binding;

15.3.4 Protein-protein interactions;

15.4 Challenges and new advances;

15.5 Perspectives;

Acknowledgements;

References;

Section 5: Fragment-Based Design;

16 Discovery and Extrapolation of Fragment Structures towards Drug Design;

16.1 Structure-based approaches to drug discovery;

16.2 Properties of molecular fragments;

16.3 From molecular fragments to drug leads;

16.3.1 Fragment growing;

16.3.2 Fragment linking;

16.3.3 Fragment assembly;

16.4 Screening and identification of fragments;

16.5 X-Ray crystallography for fragment-based lead identification;

16.6 NMR spectroscopy;

16.6.1 Protein-based methods: structure-activity relationship by NMR;

16.6.2 Ligand-based methods;

16.7 Mass spectrometry;

16.7.1 Covalent mass spectrometric methods;

16.7.2 Non-covalent mass spectrometric methods;

16.7.3 Looking at the protein or the ligand;

16.8 Thermal shift;

16.9 Isothermal titration calorimetry;

16.10 Surface plasmon resonance;

16.11 Concluding remarks;

Acknowledgements;

References;

17 A Link Means a Lot: Disulfide Tethering in Structure-Based Drug Design;

17.1 Introduction: what is disulfide Tethering?;

17.2 Successes of native cysteine Tethering;

17.3 Role of structure in engineered-cysteine Tethering;

17.4 Cooperative Tethering;

17.5 Extended Tethering;

17.6 Break-away Tethering;

17.7 Discovery of novel allosteric sites with Tethering;

17.8 Tethering as a validation tool;

17.9 Tethering vs. traditional medicinal chemistry;

17.10 Tethering in structure determination;

17.11 The challenge of covalency;

17.12 Hydrophobic binders;

17.13 Conclusions: the future of Tethering;

References;

18 The Impact of Protein Kinase Structures on Drug Discovery;

18.1 Introduction;

18.2 The hinge region and the concept of kinase inhibitor scaffold;

18.3 High-throughput crystallography for the discovery of novel scaffolds;

18.3.1;

High-throughput crystallography for the discovery of novel scaffolds;

18.3.2 Low potency, low specificity, and low molecular weight screening;

18.4 The gatekeeper residue and the selectivity pocket;

18.5 The conformational states of the DFG motif and the opening of the back pocket;

18.6 Allosteric inhibitors, non-ATP competitive inhibitors, and irreversible inhibitors;

18.7 Discovering kinase inhibitors in a 500-dimensional space;

Acknowledgements;

References;