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PHAR 201/Bioinformatics I Philip E. Bourne

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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 PHAR201 Lecture

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

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

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 PHAR201 Lecture

5 Further Motivators 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 PHAR201 Lecture

6 Further Motivators 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 PHAR201 Lecture

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 Absorption, distribution, metabolism and excretion PHAR201 Lecture

8 What Methods Exist to Find Binding Sites?
PHAR201 Lecture

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: exploring protein sites and motifs. BMC Bioinformatics 2008, 9:312. PHAR201 Lecture

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 : PHAR201 Lecture

11 The Method Described Here Starts with a 3D Drug-Receptor Complex - The PDB Contains Many Examples
Generic Name Other Name Treatment PDBid Lipitor Atorvastatin High cholesterol 1HWK, 1HW8… Testosterone Osteoporosis 1AFS, 1I9J .. Taxol Paclitaxel Cancer 1JFF, 2HXF, 2HXH Viagra Sildenafil citrate ED, pulmonary arterial hypertension 1TBF, 1UDT, 1XOS.. Digoxin Lanoxin Congestive heart failure 1IGJ PHAR201 Lecture

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

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 P distance to environmental boundary; Pi Di and alphai D distance to central atom alpha direction to central atom Xie and Bourne 2007 BMC Bioinformatics, 8(Suppl 4):S9 PHAR201 Lecture

14 Discrimination Power of the Geometric Potential
Geometric potential can distinguish binding and non-binding sites 100 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

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

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

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 18

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 PHAR201 Lecture 19

20 This Approach is Called SMAP http://funsite.sdsc.edu
PHAR201 Lecture

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., (11) e217) A possible repositioning of a drug (Nelfinavir) to treat a completely different condition (PLoS Comp. Biol. 7(4) e ) A multi-target/drug strategy to attack a pathogen (TB-drugome PLoS Comp Biol (11): e ) The reason a drug failed (Torcetrapib - PLoS Comp Biol (5) e ) How to optimize a NCE (NCE against T. Brucei PLoS Comp Biol (1): e ) PHAR201 Lecture

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., (11) e217

23 Adverse Effects of SERMs
cardiac abnormalities thromboembolic disorders loss of calcium homeostatis ????? ocular toxicities PLoS Comp. Biol., (11) e217 PHAR201 Lecture 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< from reversed search against SERCA PHAR201 Lecture Side Effects - The Tamoxifen Story PLoS Comp. Biol., (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., (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., (11) e217

27 Off-Target of SERMs cardiac abnormalities loss of calcium
thromboembolic disorders loss of calcium homeostatis 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., (11) e217 PHAR201 Lecture

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., (11) e217 PHAR201 Lecture 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., (11) e217) A possible repositioning of a drug (Nelfinavir) to treat a completely different condition (PLoS Comp. Biol. 7(4) e ) A multi-target/drug strategy to attack a pathogen (TB-drugome PLoS Comp Biol (11): e ) The reason a drug failed (Torcetrapib - PLoS Comp Biol (5) e ) How to optimize a NCE (NCE against T. Brucei PLoS Comp Biol (1): e ) PHAR201 Lecture

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 Possible Nelfinavir Repositioning PLoS Comp. Biol (4) e

31 PHAR201 Lecture Possible Nelfinavir Repositioning

32 1OHR 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 PLoS Comp. Biol (4) e PHAR201 Lecture

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 Possible Nelfinavir Repositioning PLoS Comp. Biol (4) e

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 Possible Nelfinavir Repositioning PLoS Comp. Biol (4) e

35 Distribution of Top Hits on the Human Kinome
p-value < 1.0e-4 p-value < 1.0e-3 This is great data! Manning et al., Science, 2002, V298, 1912 PHAR201 Lecture 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 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 PLoS Comp. Biol (4) e 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 ; 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 (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, (14): p Nelfinavir can induce apoptosis in leukemia cells as a single agent Bruning, A., et al. , Molecular Cancer, :19 Nelfinavir may inhibit BCR-ABL PHAR201 Lecture 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 PLoS Comp. Biol (4) e 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., (11) e217) A possible repositioning of a drug (Nelfinavir) to treat a completely different condition (PLoS Comp. Biol. 7(4) e ) A multi-target/drug strategy to attack a pathogen (TB-drugome PLoS Comp Biol (11): e ) The reason a drug failed (Torcetrapib - PLoS Comp Biol (5) e ) How to optimize a NCE (NCE against T. Brucei PLoS Comp Biol (1): e ) PHAR201 Lecture

41 As a High Throughput Approach…..
PHAR201 Lecture

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 Tuberculosis, which is caused by the bacterial pathogen Mycobacterium tuberculosis, is a leading cause of mortality among the infectious diseases. It has been estimated by the World Health Organization (WHO) that almost one-third of the world's population, around 2 billion people, is infected with the disease. Every year, more than 8 million people develop an active form of the disease, which claims the lives of nearly 2 million. This translates to over 4,900 deaths per day, and more than 95% of these are in developing countries. Despite the current global situation, antitubercular drugs have remained largely unchanged over the last four decades. The widespread use of these agents has provided a strong selective pressure for M.tuberculosis, thus encouraging the emergence of resistant strains. Multidrug resistant (MDR) tuberculosis is defined as resistance to the first-line drugs isoniazid and rifampin. The effective treatment of MDR tuberculosis necessitates long-term use of second-line drug combinations, an unfortunate consequence of which is the emergence of further drug resistance. Enter extensively drug resistant (XDR) tuberculosis - M.tuberculosis strains that are resistant to both isoniazid plus rifampin, as well as key second-line drugs. Since the only remaining drug classes exhibit such low potency and high toxicity, XDR tuberculosis is extremely difficult to treat. The rise of XDR tuberculosis around the world imposes a great threat on human health, therefore reinforcing the development of new antitubercular agents as an urgent priority. Very few Mtb proteins explored as drug targets PHAR201 Lecture

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 Biol 6(11): e PHAR201 Lecture A Multi-target/drug Strategy

44 1. Determine the TB Structural Proteome
TB proteome homology models solved structures 3, 996 2, 266 284 1, 446 3,996 proteins in TB proteome 749 solved structures in the PDB, representing a total of 284 proteins (7.2% coverage) ModBase contains homology models for entire TB proteome 1,446 ‘high quality’ homology models were added to the data set Structural coverage increased to 43.8% Retained only those models with a model score of > 0.7 and a Modpipe quality score of > 1.1 (2818 models). There were multiple models per protein. For each TB protein, chose the model with the best model score, and if they were equal, chose the model with the best Modpipe quality score (1703 models). However, 251 (+6) models were removed since they correspond to TB proteins that already have solved structures models remained) Score for the reliability of a Model, derived from statistical potentials (F. Melo, R. Sanchez, A. Sali,2001 PDF). A model is predicted to be good when the model score is higher than a pre-specified cutoff (0.7). A reliable model has a probability of the correct fold that is larger than 95%. A fold is correct when at least 30% of its Calpha atoms superpose within 3.5A of their correct positions. The ModPipe Protein Quality Score is a composite score comprising sequence identity to the template, coverage, and the three individual scores evalue, z-Dope and GA341. We consider a MPQS of >1.1 as reliable High quality homology models from ModBase (http://modbase.compbio.ucsf.edu) increase structural coverage from 7.1% to 43.3% Kinnings et al 2010 PLoS Comp Biol 6(11): e PHAR201 Lecture 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 (nutraceuticals excluded) Chenodiol Methotrexate No. of drug binding sites Kinnings et al 2010 PLoS Comp Biol 6(11): e PHAR201 Lecture A Multi-target/drug Strategy

46 Map 2 onto 1 – The TB-Drugome
Similarities between the binding sites of M.tb proteins (blue), and binding sites containing approved drugs (red). PHAR201 Lecture

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 A Multi-target/drug Strategy Kinnings et al 2010 PLoS Comp Biol 6(11): e

48 Top 5 Most Highly Connected Drugs
Intended targets Indications 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 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 Multi-target therapy may be more effective than single-target therapy to treat infectious diseases Most of the proteins listed are potential novel drug targets for the development of efficient anti-tuberculosis chemotherapeutics. GSMN-TB: Genome Scale Metabolic Reaction Network of M.tb (http://sysbio/sbs.surrey.ac.uk/tb) 849 reactions, 739 metabolites, 726 genes Can optimize the model for in vivo growth Carry out multiple gene inhibition and compute the maximal theoretical growth rate (if close to zero, that combination of genes is essential for growth) PHAR201 Lecture

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 Kinnings et al PLoS Comp Biol 5(7) e 49

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 Repositioning - The TB Story Kinnings et al PLoS Comp Biol 5(7) e

51 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., (11) e217) A possible repositioning of a drug (Nelfinavir) to treat a completely different condition (PLoS Comp. Biol. 7(4) e ) A multi-target/drug strategy to attack a pathogen (TB-drugome PLoS Comp Biol (11): e ) The reason a drug failed (Torcetrapib - PLoS Comp Biol (5) e ) How to optimize a NCE (NCE against T. Brucei PLoS Comp Biol (1): e ) PHAR201 Lecture

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


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