1. Target Selection Relevant to Health Workshop on Target Selection NIGMS Protein Structure Initiative NIH 13 –14 November 2003 Wim G.J Hol Structural Genomics of Pathogenic Protozoa (SGPP) University of Washington and HHMI Seattle

2. Target Selection to Optimize Medical Benefits of Structural Genomics for Health Structure-Based Drug Design Synthetic Medicines Small – numerous examples Large – a few under way Proteins Improved insulins Humanized antibodies Structure-Based Vaccines Structure-based Vaccine Stabilizers HIV-protein:antibody complexes Structure-Based Diagnostics

3. Drugs acting on Proteins –Active Site Blockers –Cofactor Site Blockers –Receptor Binding Site Binders –Conformational Change Preventers –Conformational Change Accelerators –Protein Assembly Inhibitors –Multi-protein Disassembly Inhibitors –Protein- Protein Glues The mode of action of drugs* varies tremendously. * And promising lead compounds

4. What do safe drugs not do? They do not bind to too many essential, human proteins, nucleic acids, bilayers, and their complexes They do not covalently modify too many essential human proteins, nucleic acids, bilayers They do not bind to or react with too many human metabolites GOOD DRUGS ARE GREAT AVOIDERS

5. Toxicity: How many potential binding sites in humans for small molecules? Guestimate upon Guestimate: ~ 35,000 human genes? ~ 100,000 variant proteins? - splicing ~ 200,000 mature proteins? - splicing plus post-trans modifications ~ 400,000 different single proteins plus protein-protein complexes? including splicing and post trans modifications ~ 800,000 different conformations for the above? assuming two distinct conformations per above ~ 1,600,000 binding pockets? assuming about 2 binding pockets per above. How many binding sites for the RNAs, DNA, bilayers? 400,000? So about 2,000,000 binding pockets per human proteome plus transcriptome??

6. Beneficial versus Harmful Effects Toxicity: How many of these 2,000,000??? potential binding sites in humans are distinctly disadvantageous if drug bound to them? Cancer: How many of these 2,000,000??? potential binding sites are fatal for a cancer cell if a drug bound to them? Infectious diseases: How many of the ~200,000?? Potential binding sites are fatal for a pathogen if a drug bound to them? (Pathogen genomes are typically 10 times smaller than the human genome – except for viruses, which are ~1000 times smaller) Human and Pathogen Structural Genomics superb way to evaluate binding sites and binding modes.

7. Drug Target Selection Human Diseases (A wealth of functional information available) 1. Modulating wt human proteins Neurological disorders Blood pressure irregularities Heart disease Inflammation Immune modulators Diabetes Asthma Trauma’s Surgery needs Painkillers Etc, etc 2. Human genetic diseases 3.Cancer Each of these categories have quite different target selection characteristics

8. Drug Target Selection - Cancer Which Biomacromolecule to target?: Modified protein? or Regular wt protein, or DNA, RNA? Selectivity: Usually difficult to achieve since there is often a close homologue of human protein in healthy cells. Are there opportunities for drugs to compensate problem at all? Loss of function mutations very tricky to restore with drug. Loss of stability mutations perhaps to restore with drug Attempts with p53. One drug might stabilize several different p53 mutants. Selectivity might be less of a problem Note: Drug Resistance a major problem

9. Drug Target Selection - Genetic Diseases Which Biomacromolecule to target?: Modified protein – usually Or pathway of affected protein But in CF – bacterial proteins… Selectivity: Maybe not such a major problem, except perhaps in cases of a member of a protein family with numerous close homologs Are there opportunities for drugs to compensate problem at all? Loss of function mutations very tricky to restore with drug. Loss of stability mutations perhaps to restore with drug Specific case: preventing aggregation very challenging Very well-known case : sickle cell Hemoglobin. Note: Number of patients per specific mutation often very small.

10. Drug Target Selection - Infectious Diseases Which Biomacromolecule to target?: Essential proteins & nucleic acids Sufficiently different from, or absent, in humans Selectivity: Often great opportunities Sometimes selective uptake by pathogen is helpful (CQ) Sometimes no selectivity is required since human homologue turning over very fast (DFMO) Are there opportunities for drugs to compensate problem at all? Yes Note: For certain diseases billions of patients at risk are very poor. Note: Drug resistance a major problem.

11. Drug Target Selection - Infectious Diseases How? Functional Information – often not available - Classical biochemistry - Functional Genomics - Target from a HT screen Essentiality Information – even more often not available - Genome-wide RNAi - Genome-wide Gene disruption Sufficient Dissimilarity with Human Proteins – information available Potential Approaches: - Relative of Drug Target in any species ("Piggy backing") - Relative of Any Enzyme in Any Species - Interaction information Interaction celebrity Interacting with interaction celebrity

12. Drug Target Selection for Structural Genomics of Pathogens Piggy-backing Searching Patent Databases To Identify Proteins that have Inhibitors as Leads for Drug Development Wes Van Voorhis Michael Gelb Gene Quinn Fred Buckner

13. Piggybacking: Bypass the Bottleneck of Identification of Drug-Like Lead Inhibitors Use the aggregate findings of decades of pharmaceutical pursuit for drug-like leads Identify enzymes where inhibitors have already been generated Use these inhibitors as leads for further development

14. Cross Reference Databases 637 Plasmodium falciparum enzymes from PlasmoDB Search World’s Patent Databases for Enzyme + inhibit* = 163 enzymes 50 enzymes with 3 or more small molecule inhibitor patents These enzymes are placed in the SGPP pipeline, also examining currently L. major, T. cruzi, and T. brucei

15. Examples of P. falciparum enzymes where a homologous enzyme has small molecule inhibitors adenosine deaminase, putative33 Patents adenylosuccinate synthetase10 Patents DNA topoisomerase II, putative5 Patents farnesyl pyrophosphate synthase, putative4 Patents glyoxalase I, putative & glyoxalase II family protein, putative6 Patents GMP synthetase5 Patents DEAD-box RNA helicase, putative22 Patents Histone deacetylase, putative77 Patents N-myristoyltransferase14 Patents ornithine aminotransferase7 Patents protoporphyrinogen oxidase, putative26 Patents pyruvate kinase, putative5 Patents

16. Enzymes have: Often good pockets With hydrophobic grooves Are usually quite stable Are often stand-alone entities Liz Worthey, Peter Myler David Kim, David Baker Drug Target Selection for Structural Genomics of Pathogens Search for Enzyme-relatives

17. Redundant dataset comprised: 424 proteins annotated with EC number in PlasmoDB 475 proteins belonging to COGs containing a protein with an EC number 457 proteins from Blastp against BRENDA enzyme database ~470 proteins from Psiblast against BRENDA enzyme database After removal of proteins due to redundancy between datasets, standard filtering (e.g. M and C content), and exclusion of proteins that showed more than 60% identity over 100 aa to human proteins we have: 720 proteins selected for expression (plus the number from the psiblasting) Search for Enzyme-Relatives

18. P. falciparum proteins identified in PlasmoDB that contained an Enzyme Commission number in their annotation. P. falciparum proteins with a significant BlastP/ Psiblast match to a protein occurring in the BRENDA enzyme DB (Institute of Biochem, U of Cologne). Selection of enzymes and enzyme-like proteins for P. falciparum P. falciparum proteins belonging to Clusters of Orthologous Genes (David Roos lab, U of Penn), where the cluster contained proteins identified as enzymes (Gene Ontology characterizations). 103 5 3160 2 450152

19. P falciparum pairs: - Often stabilize each other - Sometimes have hydrophobic interacting grooves - Pair partners may suggest function -“Interaction Celebrities” likely very important function P falciparum:human pairs: - Interesting from drug and vaccine point of view Marissa Vignali, Doug LaCount, Lori Schoenfeld, Stan Fields Prolexys Pharmaceuticals, Inc. Pradip Rathod group Drug Target Selection for Structural Genomics of Pathogens Search for Protein Pairs

20. Pick, at random, 6,144 (64x96) yeast clones expressing Binding Domain (BD) fusions Mate each BD clone with an Activation Domain (AD) fusion library Plate under selective conditions Pick positives Sequence inserts in BD and AD plasmids to determine identity of interacting proteins Analyze data Non-classical Experimental Y2H Strategy

21. 530 783 BD fusion AD fusion 234 296 487 Current P falciparum Y2H* Dataset Three types of interactions: Both partners have annotation (21%) One partner has annotation, one is hypothetical (49%) Both partners are hypothetical (30%)

22. Match the Biomacromolecular World with the Chemical Universe About 200,000 to 2,000,000?? Binding Sites in the Bioworld to be matched with the effectively infinite Chemical Universe (10 60 small molecules below 800 Daltons…) Good representation of the Chemical Universe a Challenge

23. The useful part of the chemical universe For oral drugs: The Lipinski's "rule of 5" states that poor absorption or permeation is more likely when: - molecular weight (MW) is over 500 - more than 5 H-bond donors (expressed as the sum of OHs and NHs). - more than 10 H-bond acceptors (expressed as the sum of Ns and Os). - the calculated ClogP is greater than 5 (or MlogP > 4.15) Citation: C. A. Lipinski, F. Lombardo, B. W. Dominy, and P. J. Feeney, "Experimental and Computational Approaches to Estimate Solubility and Permeability in Drug Discovery and Development Settings," Advanced Drug Delivery Reviews 23, 3-25 (1997)

24. Matching the Proteome and Transcriptome with the Chemical Universe Find small molecules which interact with one or more important binding sites. Binder Discovery: Each drug target protein vs. each compound Pair Stabilizer Discovery: Each Interacting Protein Pair vs. each compound Pair- Forming-Preventer Discovery: Each Known Protein pair vs. each compound Glue discovery: All proteins vs. all proteins vs. each compound

25. Binder Discovery In Solution: - General Screens ThermoFluor – thermal denaturation effect NMR Frontal Affinity Chromatography - Specific Screens In Crystals: - Prior to Crystal Growth Random co-crystallants with protein-loving properties - After Crystal Growth Soak with smart cocktails

26. Special Types of General Screens needed for: Pair Stabilizer Discovery: Each Interacting Protein Pair vs. each compound Pair Forming Preventer Discovery: Each Known Protein pair vs. each compound Glue discovery: All proteins vs. all proteins vs. each compound Pair Stabilizers and “Glue”s likely to promote crystal formation

27. Relative Intensity 1 0.5 Time (Min) 024681012 Screening of Ligand Mixtures Frontal Affinity Chromatography Low Affinity ~20  M 5  M < 1  M 10  l Beads, 2  M each compound Tight Binders often increase crystal growth success rate Jizhen Li, Erkang Fan Yuko Ogata (Turecek Group, UW Chemistry) Christophe Verlinde

28. X Numerous Protein:Ligand Complexes Proteome Chemical Universe Medicinal SG Essential And Sufficiently Different From Human Essential And No Human Counterpart Essential But Too Human- Like Non- essential

29. 1. Human Drug targets, If possible with compounds bound 2. Pathogenic Drug Targets Preferably not present in humans Preferably with compounds boun 3. All human Proteins revealing Potential Toxic Binding pockets Medicinal Structural Genomics of Pathogens and Humans leads to Structures of: An accelerated translation of the genome sequence wealth into therapies

30. Thank You