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Protein Ligand Interactions: A Method and its Application to Drug Discovery

Protein Ligand Interactions: A Method and its Application to Drug Discovery. PHAR 201/Bioinformatics I Philip E. Bourne Department of Pharmacology, UCSD pbourne@ucsd.edu. Today’s Lecture in Context.

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Protein Ligand Interactions: A Method and its Application to Drug Discovery

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  1. Protein Ligand Interactions: A Method and its Application to Drug Discovery PHAR 201/Bioinformatics I Philip E. Bourne Department of Pharmacology, UCSD pbourne@ucsd.edu PHAR201 Lecture 12 2012

  2. Today’s Lecture in Context • Prof Abagyan provided an overview of tools and considerations in looking at protein-ligand interactions • Today we will explore only one methodology in structural bioinformatics in some detail. A method for examining protein-ligand interactions and its implications for drug discovery • In a forthcoming lecture Roger Chang will describe how this approach can be extended into the realm of systems biology, also for drug discovery PHAR201 Lecture 12 2012

  3. Drug Discovery is a Major Reason to Study Protein-Ligand Interactions But.. Failure is telling us that Ehrlich’s idea of a magic bullet ie a highly specific drug for a known receptor is rarely the case PHAR201 Lecture 12 2012

  4. One Drug Binds to Multiple Targets • Tykerb – Breast cancer • Gleevac – Leukemia, GI cancers • Nexavar – Kidney and liver cancer • Staurosporine – natural product – alkaloid – uses many e.g., antifungal antihypertensive Collins and Workman 2006 Nature Chemical Biology 2 689-700 PHAR201 Lecture 12 2012

  5. The truth is we know very little about how the major drugs we take work We know even less about what side effects they might have Drug discovery seems to be approached in a very consistent and conventional way The cost of bringing a drug to market is huge ~$800M The cost of failure is even higher e.g. Vioxx - $4.85Bn - Hence fail early and cheaply Further Motivators PHAR201 Lecture 12 2012

  6. The truth is we know very little about how the major drugs we take work – receptors are unknown We know even less about what side effects they might have - receptors are unknown Drug discovery seems to be approached in a very consistent and conventional way The cost of bringing a drug to market is huge ~$800M – drug reuse is a big business The cost of failure is even higher e.g. Vioxx - $4.85Bn - fail early and cheaply Further Motivators PHAR201 Lecture 12 2012

  7. What if… • We can characterize a protein-ligand binding site from a 3D structure (primary site) and search for that site on a proteome wide scale? • We could perhaps find alternative binding sites for existing pharmaceuticals? • We could use it for lead optimization and possible ADME/Tox prediction PHAR201 Lecture 12 2012

  8. What Methods Exist to Find Binding Sites? PHAR201 Lecture 12 2012

  9. Template Methods e.g. MSDmotif • MSDsite queries descriptions of existing sites e.g. all SHD sites • MSDsite finds unknown sites based on motif search – limited and sequence order dependent • Pocketome – known to exist experimentally - limited • We describe here a method that finds unknown sites based on structure and is sequence order independent Golovin A, Henrick K: MSDmotif: exploringprotein sites and motifs. BMC Bioinformatics 2008, 9:312. http://www.ebi.ac.uk/pdbe-site/pdbemotif/ PHAR201 Lecture 12 2012

  10. Other Methods • 3D structure based methods • Electrostatic potential based methods • 4 point pharmacophore fingerprint and cavity shape descriptors Henrich S, Salo-Ahen OM, Huang B, Rippmann FF, Cruciani G, et al. Computational approaches to identifying and characterizing protein binding sites for ligand design. J MolRecognit2010 23: 209-219. PHAR201 Lecture 12 2012

  11. The Method Described Here Starts with a 3D Drug-Receptor Complex - The PDB Contains Many Examples PHAR201 Lecture 12 2012

  12. A Reverse Engineering Approach to Drug Discovery Across Gene Families Characterize ligand binding site of primary target (Geometric Potential) Identify off-targets by ligand binding site similarity (Sequence order independent profile-profile alignment) Extract known drugs or inhibitors of the primary and/or off-targets Search for similar small molecules … Dock molecules to both primary and off-targets Statistics analysis of docking score correlations PHAR201 Lecture 12 2012

  13. Characterization of the Ligand Binding Site - The Geometric Potential • Conceptually similar to hydrophobicity or electrostatic potential that is dependant on both global and local environments • Initially assign Ca atom with a value that is the distance to the environmental boundary • Update the value with those of surrounding Ca atoms dependent on distances and orientation – atoms within a 10A radius define i Xie and Bourne 2007 BMC Bioinformatics, 8(Suppl 4):S9 PHAR201 Lecture 12 2012

  14. Discrimination Power of the Geometric Potential • Geometric potential can distinguish binding and non-binding sites 100 0 Geometric Potential Scale Xie and Bourne 2007 BMC Bioinformatics, 8(Suppl 4):S9

  15. Local Sequence-order Independent Alignment with Maximum-Weight Sub-Graph Algorithm Structure A Structure B L E R V K D L L E R V K D L Xie and Bourne 2008 PNAS, 105(14) 5441 • Build an associated graph from the graph representations of two structures being compared. Each of the nodes is assigned with a weight from the similarity matrix • The maximum-weight clique corresponds to the optimum alignment of the two structures PHAR201 Lecture 12 2012

  16. Nothing in Biology {including Drug Discovery} Makes Sense Except in the Light of Evolution Theodosius Dobzhansky (1900-1975) PHAR201 Lecture 12 2012

  17. Similarity Matrix of Alignment • Chemical Similarity • Amino acid grouping: (LVIMC), (AGSTP), (FYW), and (EDNQKRH) • Amino acid chemical similarity matrix • Evolutionary Correlation • Amino acid substitution matrix such as BLOSUM45 • Similarity score between two sequence profiles fa, fb are the 20 amino acid target frequencies of profile a and b, respectively Sa, Sb are the PSSM of profile a and b, respectively Xie and Bourne 2008 PNAS, 105(14) 5441 PHAR201 Lecture 12 2012

  18. Lead Discovery from Fragment Assembly Privileged molecular moieties in medicinal chemistry Structural genomics and high throughput screening generate a large number of protein-fragment complexes Similar sub-site detection enhances the application of fragment assembly strategies in drug discovery 1HQC: Holliday junction migration motor protein from Thermus thermophilus 1ZEF: Rio1 atypical serine protein kinase from A. fulgidus PHAR201 Lecture 12 2012

  19. Lead Optimization from Conformational Constraints Same ligand can bind to different proteins, but with different conformations By recognizing the conformational changes in the binding site, it is possible to improve the binding specificity with conformational constraints placed on the ligand 1ECJ: amido-phosphoribosyltransferase from E. Coli 1H3D: ATP-phosphoribosyltransferase from E. Coli PHAR201 Lecture 12 2012

  20. This Approach is Called SMAPhttp://funsite.sdsc.edu PHAR201 Lecture 12 2012

  21. What Have These Off-targets and Networks Told Us So Far?Some Examples… Nothing A possible explanation for a side-effect of a drug already on the market (SERMs - PLoS Comp. Biol., 2007 3(11) e217) A possible repositioning of a drug (Nelfinavir) to treat a completely different condition (PLoS Comp. Biol. 7(4) e1002037) A multi-target/drug strategy to attack a pathogen (TB-drugome PLoS Comp Biol 2010 6(11): e1000976) The reason a drug failed (Torcetrapib - PLoS Comp Biol 2009 5(5) e1000387) How to optimize a NCE (NCE against T. BruceiPLoS Comp Biol. 2010 6(1): e1000648) PHAR201 Lecture 12 2012

  22. Selective Estrogen Receptor Modulators (SERM) • One of the largest classes of drugs • Breast cancer, osteoporosis, birth control etc. • Amine and benzine moiety Side Effects - The Tamoxifen Story PLoS Comp. Biol., 2007 3(11) e217

  23. Adverse Effects of SERMs cardiac abnormalities loss of calcium homeostatis thromboembolic disorders ????? ocular toxicities PLoS Comp. Biol., 2007 3(11) e217 PHAR201 Lecture 12 2012 Side Effects - The Tamoxifen Story

  24. Ligand Binding Site Similarity Search On a Proteome Scale SERCA ERa • Searching human proteins covering ~38% of the drugable genome against SERM binding site • Matching Sacroplasmic Reticulum (SR) Ca2+ ion channel ATPase (SERCA) TG1 inhibitor site • ERa ranked top with p-value<0.0001 from reversed search against SERCA PHAR201 Lecture 12 2012 Side Effects - The Tamoxifen Story PLoS Comp. Biol., 2007 3(11) e217

  25. Structure and Function of SERCA • Regulating cytosolic calcium levels in cardiac and skeletal muscle • Cytosolic and transmembrane domains • Predicted SERM binding site locates in the TM, inhibiting Ca2+ uptake Side Effects - The Tamoxifen Story PLoS Comp. Biol., 2007 3(11) e217

  26. Binding Poses of SERMs in SERCA from Docking Studies • Salt bridge interaction between amine group and GLU • Aromatic interactions for both N-, and C-moiety 6 SERMS A-F (red) Side Effects - The Tamoxifen Story PLoS Comp. Biol., 2007 3(11) e217

  27. Off-Target of SERMs cardiac abnormalities loss of calcium homeostatis thromboembolic disorders SERCA ! ocular toxicities • in vivo and in vitro Studies • TAM play roles in regulating calcium uptake activity of cardiac SR • TAM reduce intracellular calcium concentration and release in the platelets • Cataracts result from TG1 inhibited SERCA up-regulation • EDS increases intracellular calcium in lens epithelial cells by inhibiting SERCA • in silico Studies • Ligand binding site similarity • Binding affinity correlation PLoS Comp. Biol., 2007 3(11) e217 PHAR201 Lecture 12 2012

  28. The Challenge • Design modified SERMs that bind as strongly to estrogen receptors but do not have strong binding to SERCA, yet maintain other characteristics of the activity profile PLoS Comp. Biol., 2007 3(11) e217 PHAR201 Lecture 12 2012 Side Effects - The Tamoxifen Story

  29. What Have These Off-targets and Networks Told Us So Far?Some Examples… Nothing A possible explanation for a side-effect of a drug already on the market (SERMs - PLoS Comp. Biol., 2007 3(11) e217) A possible repositioning of a drug (Nelfinavir) to treat a completely different condition (PLoS Comp. Biol. 7(4) e1002037) A multi-target/drug strategy to attack a pathogen (TB-drugome PLoS Comp Biol 2010 6(11): e1000976) The reason a drug failed (Torcetrapib - PLoS Comp Biol 2009 5(5) e1000387) How to optimize a NCE (NCE against T. BruceiPLoS Comp Biol. 2010 6(1): e1000648) PHAR201 Lecture 12 2012

  30. Nelfinavir • Nelfinavir may have the most potent antitumor activity of the HIV protease inhibitors Joell J. Gills et al, Clin Cancer Res, 2007; 13(17) Warren A. Chow et al, The Lancet Oncology, 2009, 10(1) • Nelfinavir can inhibit receptor tyrosine kinase(s) • Nelfinavir can reduce Akt activation • Our goal: • to identify off-targets of Nelfinavir in the human proteome • to construct an off-target binding network • to explain the mechanism of anti-cancer activity PHAR201 Lecture 12 2012 PLoS Comp. Biol. 2011 7(4) e1002037 Possible Nelfinavir Repositioning

  31. PHAR201 Lecture 12 2012 Possible Nelfinavir Repositioning

  32. drug target off-target? structural proteome binding site comparison 1OHR protein ligand docking MD simulation & MM/GBSA Binding free energy calculation network construction & mapping Clinical Outcomes PHAR201 Lecture 12 2012 PLoS Comp. Biol. 2011 7(4) e1002037

  33. Binding Site Comparison • 5,985 structures or models that cover approximately 30% of the human proteome are searched against the HIV protease dimer (PDB id: 1OHR) • Structures with SMAP p-value less than 1.0e-3 were retained for further investigation • A total 126 structures have significant p-values < 1.0e-3 PHAR201 Lecture 12 2012 PLoS Comp. Biol. 2011 7(4) e1002037 Possible Nelfinavir Repositioning

  34. Enrichment of Protein Kinases in Top Hits • The top 7 ranked off-targets belong to the same EC family - aspartyl proteases - with HIV protease • Other off-targets are dominated by protein kinases (51 off-targets) and other ATP or nucleotide binding proteins (17 off-targets) • 14 out of 18 proteins with SMAP p-values < 1.0e-4 are protein kinases PHAR201 Lecture 12 2012 PLoS Comp. Biol. 2011 7(4) e1002037 Possible Nelfinavir Repositioning

  35. Distribution of Top Hits on the Human Kinome p-value < 1.0e-4 p-value < 1.0e-3 Manning et al., Science, 2002, V298, 1912 PHAR201 Lecture 12 2012 Possible Nelfinavir Repositioning

  36. Interactions between Inhibitors and Epidermal Growth Factor Receptor (EGFR) – 74% of binding site resides are comparable 1. Hydrogen bond with main chain amide of Met793 (without it 3700 fold loss of inhibition) 2. Hydrophobic interactions of aniline/phenyl with gatekeeper Thr790 and other residues EGFR-DJK Co-crys ligand EGFR-Nelfinavir H-bond: Met793 with benzamide hydroxy O38 H-bond: Met793 with quinazoline N1 PHAR201 Lecture 12 2012 DJK = N-[4-(3-BROMO-PHENYLAMINO)-QUINAZOLIN-6-YL]-ACRYLAMIDE

  37. Off-target Interaction Network Identified off-target Pathway Activation Intermediate protein Cellular effect Inhibition PHAR201 Lecture 12 2012 PLoS Comp. Biol. 2011 7(4) e1002037 Possible Nelfinavir Repositioning

  38. Other Experimental Evidence to Show Nelfinavir inhibition on EGFR, IGF1R, CDK2 and Abl is Supportive The inhibitions of Nelfinavir on IGF1R, EGFR, Akt activity were detected by immunoblotting. The inhibition of Nelfinavir on Akt activity is less than a known PI3K inhibitor Joell J. Gills et al. Clinic Cancer Research September 2007 13; 5183 Nelfinavir inhibits growth of human melanoma cells by induction of cell cycle arrest Nelfinavir induces G1 arrest through inhibition of CDK2 activity. Such inhibition is not caused by inhibition of Akt signaling. Jiang W el al. Cancer Res. 2007 67(3) BCR-ABL is a constitutively activated tyrosine kinasethat causes chronic myeloid leukemia (CML) Druker, B.J., et al New England Journal of Medicine, 2001. 344(14): p. 1031-1037 Nelfinavir can induce apoptosis in leukemia cells as a single agent Bruning, A., et al. , Molecular Cancer, 2010. 9:19 Nelfinavir may inhibit BCR-ABL PHAR201 Lecture 12 2012 Possible Nelfinavir Repositioning

  39. Summary • The HIV-1 drug Nelfinavir appears to be a broad spectrum low affinity kinase inhibitor • Most targets are upstream of the PI3K/Akt pathway • Findings are consistent with the experimental literature • More direct experiment is needed PHAR201 Lecture 12 2012 PLoS Comp. Biol. 2011 7(4) e1002037 Possible Nelfinavir Repositioning

  40. What Have These Off-targets and Networks Told Us So Far?Some Examples… Nothing A possible explanation for a side-effect of a drug already on the market (SERMs - PLoS Comp. Biol., 2007 3(11) e217) A possible repositioning of a drug (Nelfinavir) to treat a completely different condition (PLoS Comp. Biol. 7(4) e1002037) A multi-target/drug strategy to attack a pathogen (TB-drugome PLoS Comp Biol 2010 6(11): e1000976) The reason a drug failed (Torcetrapib - PLoS Comp Biol 2009 5(5) e1000387) How to optimize a NCE (NCE against T. BruceiPLoS Comp Biol. 2010 6(1): e1000648) PHAR201 Lecture 12 2012

  41. As a High Throughput Approach….. PHAR201 Lecture 12 2012

  42. The Problem with Tuberculosis • One third of global population infected • 1.7 million deaths per year • 95% of deaths in developing countries • Anti-TB drugs hardly changed in 40 years • MDR-TB and XDR-TB pose a threat to human health worldwide • Development of novel, effective and inexpensive drugs is an urgent priority PHAR201 Lecture 12 2012

  43. The TB-Drugome • Determine the TB structural proteome • Determine all known drug binding sites from the PDB • Determine which of the sites found in 2 exist in 1 • Call the result the TB-drugome Kinnings et al 2010 PLoS Comp Biol6(11): e1000976 PHAR201 Lecture 12 2012 A Multi-target/drug Strategy

  44. 1. Determine the TB Structural Proteome • High quality homology models from ModBase (http://modbase.compbio.ucsf.edu) increase structural coverage from 7.1% to 43.3% TB proteome homology models solved structures 2, 266 3, 996 284 1, 446 Kinnings et al 2010 PLoS Comp Biol6(11): e1000976 PHAR201 Lecture 12 2012 A Multi-target/drug Strategy

  45. 2. Determine all Known Drug Binding Sites in the PDB • Searched the PDB for protein crystal structures bound with FDA-approved drugs • 268 drugs bound in a total of 931 binding sites No. of drugs Acarbose Darunavir Alitretinoin Conjugated estrogens Chenodiol Methotrexate No. of drug binding sites Kinnings et al 2010 PLoS Comp Biol6(11): e1000976 PHAR201 Lecture 12 2012 A Multi-target/drug Strategy

  46. Map 2 onto 1 – The TB-Drugome http://funsite.sdsc.edu/drugome/TB/ Similarities between the binding sites of M.tb proteins (blue), and binding sites containing approved drugs (red). PHAR201 Lecture 12 2012

  47. From a Drug Repositioning Perspective • Similarities between drug binding sites and TB proteins are found for 61/268 drugs • 41 of these drugs could potentially inhibit more than one TB protein conjugated estrogens & methotrexate No. of drugs chenodiol levothyroxine testosterone raloxifene ritonavir alitretinoin No. of potential TB targets PHAR201 Lecture 12 2012 A Multi-target/drug Strategy Kinnings et al 2010 PLoS Comp Biol6(11): e1000976

  48. Top 5 Most Highly Connected Drugs PHAR201 Lecture 12 2012

  49. Vignette within Vignette Entacapone and tolcapone shown to have potential for repositioning Direct mechanism of action avoids M. tuberculosis resistance mechanisms Possess excellent safety profiles with few side effects – already on the market In vivo support Assay of direct binding of entacapone and tolcapone to InhA reveals a possible lead with no chemical relationship to existing drugs PHAR201 Lecture 12 2012 Kinnings et al. 2009 PLoS Comp Biol 5(7) e1000423

  50. Summary from the TB Alliance – Medicinal Chemistry • The minimal inhibitory concentration (MIC) of 260 uM is higher than usually considered • MIC is 65x the estimated plasma concentration • Have other InhA inhibitors in the pipeline PHAR201 Lecture 12 2012 Repositioning- The TB Story Kinnings et al. 2009 PLoS Comp Biol 5(7) e1000423

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