Computational and Structural Approaches to Drug Discovery

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.

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;