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Protein Ligand Interactions: A Method and its Application to Drug Discovery PHAR 201/Bioinformatics I Philip E. Bourne Department of Pharmacology, UCSD.

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Presentation on theme: "Protein Ligand Interactions: A Method and its Application to Drug Discovery PHAR 201/Bioinformatics I Philip E. Bourne Department of Pharmacology, UCSD."— Presentation transcript:

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 Todays 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 Ehrlichs 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 PHAR201 Lecture 12 2012 Golovin A, Henrick K: MSDmotif: exploring protein sites and motifs. BMC Bioinformatics 2008, 9:312. http://www.ebi.ac.uk/pdbe-site/pdbemotif/

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 Mol Recognit 2010 23: 209-219. PHAR201 Lecture 12 2012

11 The Method Described Here Starts with a 3D Drug-Receptor Complex - The PDB Contains Many Examples Generic NameOther NameTreatmentPDBid LipitorAtorvastatinHigh cholesterol1HWK, 1HW8… Testosterone Osteoporosis1AFS, 1I9J.. TaxolPaclitaxelCancer1JFF, 2HXF, 2HXH ViagraSildenafil citrateED, pulmonary arterial hypertension 1TBF, 1UDT, 1XOS.. DigoxinLanoxinCongestive heart failure 1IGJ 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 Initially assign C atom with a value that is the distance to the environmental boundary Update the value with those of surrounding C atoms dependent on distances and orientation – atoms within a 10A radius define i Conceptually similar to hydrophobicity or electrostatic potential that is dependant on both global and local environments Characterization of the Ligand Binding Site - The Geometric Potential 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 1000 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 L E R V K D L L E R V K D L Structure AStructure B 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 Xie and Bourne 2008 PNAS, 105(14) 5441 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 f a, f b are the 20 amino acid target frequencies of profile a and b, respectively S a, S b 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 SMAP http://funsite.sdsc.edu PHAR201 Lecture 12 2012

21 What Have These Off-targets and Networks Told Us So Far? Some Examples… 1.Nothing 2.A possible explanation for a side-effect of a drug already on the market (SERMs - PLoS Comp. Biol., 2007 3(11) e217) 3.A possible repositioning of a drug (Nelfinavir) to treat a completely different condition ( PLoS Comp. Biol. 7(4) e1002037 ) 4.A multi-target/drug strategy to attack a pathogen (TB- drugome PLoS Comp Biol 2010 6(11): e1000976) 5.The reason a drug failed (Torcetrapib - PLoS Comp Biol 2009 5(5) e1000387) 6.How to optimize a NCE ( NCE against T. Brucei PLoS 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 thromboembolic disorders ocular toxicities loss of calcium homeostatis ????? Side Effects - The Tamoxifen Story PLoS Comp. Biol., 2007 3(11) e217 PHAR201 Lecture 12 2012

24 Ligand Binding Site Similarity Search On a Proteome Scale 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 ER ranked top with p-value<0.0001 from reversed search against SERCA ER SERCA Side Effects - The Tamoxifen Story PLoS Comp. Biol., 2007 3(11) e217 PHAR201 Lecture 12 2012

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 StoryPLoS Comp. Biol., 2007 3(11) e217

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

29 What Have These Off-targets and Networks Told Us So Far? Some Examples… 1.Nothing 2.A possible explanation for a side-effect of a drug already on the market (SERMs - PLoS Comp. Biol., 2007 3(11) e217) 3.A possible repositioning of a drug (Nelfinavir) to treat a completely different condition ( PLoS Comp. Biol. 7(4) e1002037 ) 4.A multi-target/drug strategy to attack a pathogen (TB- drugome PLoS Comp Biol 2010 6(11): e1000976) 5.The reason a drug failed (Torcetrapib - PLoS Comp Biol 2009 5(5) e1000387) 6.How to optimize a NCE ( NCE against T. Brucei PLoS 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 Possible Nelfinavir Repositioning PLoS Comp. Biol. 2011 7(4) e1002037 PHAR201 Lecture 12 2012

31 Possible Nelfinavir Repositioning PHAR201 Lecture 12 2012

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

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 Possible Nelfinavir Repositioning PHAR201 Lecture 12 2012 PLoS Comp. Biol. 2011 7(4) e1002037

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

36 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 H-bond: Met793 with quinazoline N1 H-bond: Met793 with benzamide hydroxy O38 EGFR-DJK Co-crys ligand EGFR-Nelfinavir Interactions between Inhibitors and Epidermal Growth Factor Receptor (EGFR) – 74% of binding site resides are comparable DJK = N-[4-(3-BROMO-PHENYLAMINO)-QUINAZOLIN-6-YL]-ACRYLAMIDE PHAR201 Lecture 12 2012

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

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 kinase that 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 Possible Nelfinavir Repositioning PHAR201 Lecture 12 2012

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 Possible Nelfinavir Repositioning PHAR201 Lecture 12 2012 PLoS Comp. Biol. 2011 7(4) e1002037

40 What Have These Off-targets and Networks Told Us So Far? Some Examples… 1.Nothing 2.A possible explanation for a side-effect of a drug already on the market (SERMs - PLoS Comp. Biol., 2007 3(11) e217) 3.A possible repositioning of a drug (Nelfinavir) to treat a completely different condition ( PLoS Comp. Biol. 7(4) e1002037 ) 4.A multi-target/drug strategy to attack a pathogen (TB- drugome PLoS Comp Biol 2010 6(11): e1000976) 5.The reason a drug failed (Torcetrapib - PLoS Comp Biol 2009 5(5) e1000387) 6.How to optimize a NCE ( NCE against T. Brucei PLoS 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 1.Determine the TB structural proteome 2.Determine all known drug binding sites from the PDB 3.Determine which of the sites found in 2 exist in 1 4.Call the result the TB-drugome A Multi-target/drug Strategy Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976 PHAR201 Lecture 12 2012

44 1. Determine the TB Structural Proteome 284 1, 446 3, 996 2, 266 TB proteome homology models solved structures High quality homology models from ModBase (http://modbase.compbio.ucsf.edu) increase structural coverage from 7.1% to 43.3% A Multi-target/drug Strategy PHAR201 Lecture 12 2012 Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976

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 drug binding sites No. of drugs Methotrexate Chenodiol Alitretinoin Conjugated estrogens Darunavir Acarbose A Multi-target/drug Strategy PHAR201 Lecture 12 2012 Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976

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 No. of potential TB targets No. of drugs raloxifene alitretinoin conjugated estrogens & methotrexate ritonavir testosterone levothyroxine chenodiol A Multi-target/drug Strategy PHAR201 Lecture 12 2012 Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976

48 Top 5 Most Highly Connected Drugs DrugIntended targetsIndications No. of connections TB proteins levothyroxine transthyretin, thyroid hormone receptor α & β-1, thyroxine-binding globulin, mu-crystallin homolog, serum albumin hypothyroidism, goiter, chronic lymphocytic thyroiditis, myxedema coma, stupor 14 adenylyl cyclase, argR, bioD, CRP/FNR trans. reg., ethR, glbN, glbO, kasB, lrpA, nusA, prrA, secA1, thyX, trans. reg. protein alitretinoin retinoic acid receptor RXR-α, β & γ, retinoic acid receptor α, β & γ-1&2, cellular retinoic acid-binding protein 1&2 cutaneous lesions in patients with Kaposi's sarcoma 13 adenylyl cyclase, aroG, bioD, bpoC, CRP/FNR trans. reg., cyp125, embR, glbN, inhA, lppX, nusA, pknE, purN conjugated estrogens estrogen receptor menopausal vasomotor symptoms, osteoporosis, hypoestrogenism, primary ovarian failure 10 acetylglutamate kinase, adenylyl cyclase, bphD, CRP/FNR trans. reg., cyp121, cysM, inhA, mscL, pknB, sigC methotrexate dihydrofolate reductase, serum albumin gestational choriocarcinoma, chorioadenoma destruens, hydatidiform mole, severe psoriasis, rheumatoid arthritis 10 acetylglutamate kinase, aroF, cmaA2, CRP/FNR trans. reg., cyp121, cyp51, lpd, mmaA4, panC, usp raloxifene estrogen receptor, estrogen receptor β osteoporosis in post- menopausal women 9 adenylyl cyclase, CRP/FNR trans. reg., deoD, inhA, pknB, pknE, Rv1347c, secA1, sigC 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 Kinnings et al. 2009 PLoS Comp Biol 5(7) e1000423 PHAR201 Lecture 12 2012

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 Repositioning - The TB Story Kinnings et al. 2009 PLoS Comp Biol 5(7) e1000423 PHAR201 Lecture 12 2012

51 What Have These Off-targets and Networks Told Us So Far? Some Examples… 1.Nothing 2.A possible explanation for a side-effect of a drug already on the market (SERMs - PLoS Comp. Biol., 2007 3(11) e217) 3.A possible repositioning of a drug (Nelfinavir) to treat a completely different condition ( PLoS Comp. Biol. 7(4) e1002037 ) 4.A multi-target/drug strategy to attack a pathogen (TB- drugome PLoS Comp Biol 2010 6(11): e1000976) 5.The reason a drug failed (Torcetrapib - PLoS Comp Biol 2009 5(5) e1000387) 6.How to optimize a NCE ( NCE against T. Brucei PLoS Comp Biol. 2010 6(1): e1000648) PHAR201 Lecture 12 2012

52 In An Upcoming Lecture.. Roger Chang will describe how systems Biology can be used to further model protein-drug interactions in a dynamic way. PHAR201 Lecture 12 2012


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