High-throughput Computational Strategies for Proteomics   Philip E. Bourne University of California San Diego [email_addre...
High-throughput Computation Can Be Applied on Three Axes One to Multiple Targets Bioinformatics Associative Transfer of In...
Here I will focus mostly on the notion of multiple targets
Why We Think This is Important <ul><li>Ehrlich’s philosophy of magic bullets targeting individual chemoreceptors has not b...
Multiple Drugs Multiple Targets <ul><li>Gene knockouts only effect phenotype in 10-20% of cases , why?  </li></ul><ul><ul>...
Polypharmacology - One Drug Binds to Multiple Targets <ul><li>Tykerb – Breast cancer </li></ul><ul><li>Gleevac – Leukemia,...
PKA Phosphoinositide-3 Kinase (D) and Actin-Fragmin Kinase (E) ChaK (“Channel Kinase”) Scheeff & Bourne  2005 PLoS Comp. B...
Can We Propose an Evolutionary History for the Protein Kinase-Like Superfamily?   <ul><li>Bayesian inference of phylogeny ...
AFK PI3K CK APH ChaK PIPKII β AGC CAMK CMGC CK1 TK TKL Proposed Evolutionary History for the Protein Kinase-Like Superfami...
What That Study Told Us <ul><li>Structure comparison algorithms are still not good enough or comprehensive enough to provi...
 
 
 
A Quick Aside – RCSB PDB Pharmacology/Drug View 2010-2011 <ul><li>Establish linkages to drug resources (FDA, PubChem, Drug...
This begins to address the issue of multiple targets that share global similarity.. but often that is not the case .. we n...
Our Approach <ul><li>We can characterize a known protein-ligand binding site from a 3D structure (primary site) and search...
Which Means … <ul><li>We could perhaps find alternative binding sites ( off-targets ) for existing pharmaceuticals and NCE...
What Have These Off-targets and Networks Told Us So Far? Some Examples… <ul><ul><li>Nothing </li></ul></ul><ul><ul><li>A p...
Need to Start with a 3D Drug-Receptor Complex - The PDB Contains Many Examples Computational Methodology Generic Name Othe...
A Reverse Engineering Approach to  Drug Discovery Across Gene Families Characterize ligand binding  site of primary target...
<ul><li>Initially assign C   atom with a value that is the distance to the environmental boundary </li></ul><ul><li>Updat...
Discrimination Power of the Geometric Potential <ul><li>Geometric potential can distinguish binding and non-binding sites ...
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 A...
Similarity Matrix of Alignment <ul><li>Chemical Similarity </li></ul><ul><li>Amino acid grouping: (LVIMC), (AGSTP), (FYW),...
The Future as a High Throughput Approach…..
The TB-Drugome <ul><li>Determine the TB structural proteome </li></ul><ul><li>Determine all known drug binding sites from ...
1. Determine the TB Structural Proteome <ul><li>High quality homology models from ModBase (http://modbase.compbio.ucsf.edu...
2. Determine all Known Drug Binding Sites in the PDB <ul><li>Searched the PDB for protein crystal structures bound with FD...
Map 2 onto 1 – The TB-Drugome http://funsite.sdsc.edu/drugome/TB/ Similarities between the binding sites of  M.tb  protein...
From a Drug Repositioning Perspective <ul><li>Similarities between drug binding sites and TB proteins are found for 61/268...
Top 5 Most Highly Connected Drugs Drug Intended targets Indications No. of connections TB proteins levothyroxine transthyr...
What Have These Off-targets and Networks Told Us So Far? Some Examples… <ul><ul><li>Nothing </li></ul></ul><ul><ul><li>A p...
Nelfinavir  <ul><li>Nelfinavir may have the most potent antitumor activity of the HIV protease inhibitors  </li></ul><ul><...
Possible Nelfinavir Repositioning
binding site comparison protein ligand docking MD simulation & MM/GBSA Binding free energy calculation structural proteome...
Binding Site Comparison <ul><li>5,985 structures or models that cover approximately 30% of the human proteome are searched...
Enrichment of Protein Kinases in Top Hits <ul><li>The top 7 ranked off-targets  belong to the same EC family - aspartyl pr...
Distribution of Top Hits on the Human Kinome p-value < 1.0e-3 p-value < 1.0e-4 Manning et al.,  Science ,  2002, V298, 191...
Interactions between Inhibitors and Epidermal Growth Factor Receptor (EGFR) – 74% of binding site resides are comparable 1...
Off-target Interaction Network Identified off-target Intermediate protein Pathway Cellular effect Activation Inhibition Po...
Summary  <ul><li>The HIV-1 drug Nelfinavir appears to be a broad spectrum low affinity kinase inhibitor </li></ul><ul><li>...
The Future as a Dynamical Network Approach
Computational Evaluation of Drug Off-Target Effects 336 genes 1587 reactions Plos Comp. Biol. 2010 6(9): e1000938 Proteome...
Acknowledgements Sarah Kinnings Lei Xie Li Xie http://funsite.sdsc.edu http://www.slideshare.net/pebourne/ucl120810 Roger ...
Upcoming SlideShare
Loading in …5
×

Pep Talk San Diego 011311

1,351 views

Published on

Presentation made at PepTalk 2011 in San Diego on Jan. 13, 2011. The emphasis is on computational methods to explore global and local structure similarities in determining the possible promiscuity of drugs to bind to multiple protein receptors.

Published in: Education
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
1,351
On SlideShare
0
From Embeds
0
Number of Embeds
2
Actions
Shares
0
Downloads
9
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide
  • Absorption, distribution, metabolism and excretion
  • P distance to environmental boundary; Pi Di and alphai D distance to central atom alpha direction to central atom
  • 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 &gt; 0.7 and a Modpipe quality score of &gt; 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. 1446 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 &gt;1.1 as reliable
  • (nutraceuticals excluded)
  • 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)
  • This is great data!
  • Pep Talk San Diego 011311

    1. 1. High-throughput Computational Strategies for Proteomics Philip E. Bourne University of California San Diego [email_address] http://www.sdsc.edu/ pb PepTalk – January 13, 2011 As Applied to Drug Discovery
    2. 2. High-throughput Computation Can Be Applied on Three Axes One to Multiple Targets Bioinformatics Associative Transfer of Indications Target Disease Drug Cheminfomatics HTS Docking
    3. 3. Here I will focus mostly on the notion of multiple targets
    4. 4. Why We Think This is Important <ul><li>Ehrlich’s philosophy of magic bullets targeting individual chemoreceptors has not been realized in most cases – witness the recent success of big pharma </li></ul><ul><li>Stated another way – The notion of one drug, one target, to treat one disease is a little naïve in a complex system </li></ul>
    5. 5. Multiple Drugs Multiple Targets <ul><li>Gene knockouts only effect phenotype in 10-20% of cases , why? </li></ul><ul><ul><li>redundant functions </li></ul></ul><ul><ul><li>alternative network routes </li></ul></ul><ul><ul><li>robustness of interaction networks </li></ul></ul><ul><li>35% of biologically active compounds bind to more than one target </li></ul>A.L. Hopkins Nat. Chem. Biol. 2008 4:682-690 Paolini et al. Nat. Biotechnol. 2006 24:805–815
    6. 6. Polypharmacology - One Drug Binds to Multiple Targets <ul><li>Tykerb – Breast cancer </li></ul><ul><li>Gleevac – Leukemia, GI cancers </li></ul><ul><li>Nexavar – Kidney and liver cancer </li></ul><ul><li>Staurosporine – natural product – alkaloid – uses many e.g., antifungal antihypertensive </li></ul>Collins and Workman 2006 Nature Chemical Biology 2 689-700 Motivation
    7. 7. PKA Phosphoinositide-3 Kinase (D) and Actin-Fragmin Kinase (E) ChaK (“Channel Kinase”) Scheeff & Bourne 2005 PLoS Comp. Biol. 1(5): e49.
    8. 8. Can We Propose an Evolutionary History for the Protein Kinase-Like Superfamily? <ul><li>Bayesian inference of phylogeny (MrBayes) </li></ul><ul><li>Manual structure alignment produces very high-quality sequence alignment of diverse homologues </li></ul><ul><li>But, sequence information too degraded to produce branching with sufficient support (i.e. a high posterior probability) </li></ul><ul><li>Addition of a matrix of structural characteristics (similar to morphological characteristics) produces a well supported combined model </li></ul><ul><li>Neither sequence structural characteristics sufficient to alone produce resolved tree, must be used in combination. </li></ul>Example columns: 1) Ion pair analogous to K72-E91 in PKA 2) α-Helix B present 3) State of α-Helix C (0: kinked, 1: straight) 4) State of Strand 4 (0: kinked, 1: straight) 5) α-Helix D present 1 2 3 4 5 Scheeff & Bourne 2005 PLoS Comp. Biol. 1(5): e49. 1BO1 Atypical 0 0 0 0 1 1IA9 Atypical 1 1 1 1 0 1E8X Atypical 1 0 1 1 1 1CJA Atypical 1 0 1 1 1 1NW1 Atypical 1 0 1 0 0 1J7U Atypical 1 0 1 0 1 1CDK AGC 1 1 1 0 1 1O6L AGC 1 1 1 0 1 1OMW AGC 1 1 1 0 1 1H1W AGC 1 1 1 0 1 1MUO Other 1 1 1 0 1 1TKI CAMK 1 0 1 0 1 1JKL CAMK 1 0 1 0 1 1A06 CAMK 1 0 1 0 1 1PHK CAMK 1 0 1 0 1 1KWP CAMK 1 0 1 0 1 1IA8 CAMK 1 0 1 0 0 1GNG CMGC 1 0 1 0 1 1HCK CMGC 1 0 1 0 1 1JNK CMGC 1 0 1 0 1 1HOW CMGC 1 0 1 0 1 1LP4 Other 1 0 1 0 1 1F3M STE 1 0 1 0 1 1O6Y Other 1 0 1 0 1 1CSN CK1 1 0 1 0 1 1B6C TKL 1 0 1 0 1 2SRC TK 1 0 1 0 1 1LUF TK 1 0 1 0 1 1IR3 TK 1 0 1 0 1 1M14 TK 1 0 1 0 1 1GJO TK 1 0 1 0 1
    9. 9. AFK PI3K CK APH ChaK PIPKII β AGC CAMK CMGC CK1 TK TKL Proposed Evolutionary History for the Protein Kinase-Like Superfamily <ul><li>Atypical kinase families: Blue </li></ul><ul><li>Typical protein kinase groups (subfamilies): Red </li></ul><ul><li>Branch labels: posterior probability of branch </li></ul><ul><li>Suggests distinctive history for atypical kinases, as opposed to intermittent divergence from the typical protein kinases (TPKs) </li></ul><ul><li>TPK portion of tree shows high degree of agreement with Manning tree </li></ul><ul><li>Branching is supported by species representation of kinase families </li></ul>0.97 1.0 0.78 0.85 0.64 Scheeff & Bourne 2005 PLoS Comp. Biol. 1(5): e49.
    10. 10. What That Study Told Us <ul><li>Structure comparison algorithms are still not good enough or comprehensive enough to provide the level of detail we need for large scale studies…. </li></ul><ul><li>We are starting to address this through our research and the RCSB PDB </li></ul>
    11. 14. A Quick Aside – RCSB PDB Pharmacology/Drug View 2010-2011 <ul><li>Establish linkages to drug resources (FDA, PubChem, DrugBank, ChEBI, BindingDB etc.) </li></ul><ul><li>Create query capabilities for drug information </li></ul><ul><li>Provide superposed views of ligand binding sites </li></ul><ul><li>Analyze and display protein-ligand interactions </li></ul>Mockups of drug view features RCSB PDB Ligand View RCSB PDB Team Drug Name Asp Aspirin Has Bound Drug % Similarity to Drug Molecule 100
    12. 15. This begins to address the issue of multiple targets that share global similarity.. but often that is not the case .. we need to focus on binding site similarity
    13. 16. Our Approach <ul><li>We can characterize a known protein-ligand binding site from a 3D structure (primary site) and search for that site on a proteome wide scale independent of global structure similarity </li></ul>
    14. 17. Which Means … <ul><li>We could perhaps find alternative binding sites ( off-targets ) for existing pharmaceuticals and NCEs? </li></ul><ul><li>If we can make this high throughput we could rationally explore a large network of protein-ligands interactions </li></ul>
    15. 18. What Have These Off-targets and Networks Told Us So Far? Some Examples… <ul><ul><li>Nothing </li></ul></ul><ul><ul><li>A possible explanation for a side-effect of a drug already on the market (SERMs - PLoS Comp. Biol. , 2007 3(11) e217) </li></ul></ul><ul><ul><li>The reason a drug failed (Torcetrapib - PLoS Comp Biol 2009 5(5) e1000387) </li></ul></ul><ul><ul><li>How to optimize a NCE ( NCE against T. Brucei PLoS Comp Biol. 2010 6(1): e1000648) </li></ul></ul><ul><ul><li>A multi-target/drug strategy to attack a pathogen (TB-drugome PLoS Comp Biol 2010 6(11): e1000976) </li></ul></ul><ul><ul><li>A possible repositioning of a drug (Nelfinavir) to treat a completely different condition (under review) </li></ul></ul>Our Stories
    16. 19. Need to Start with a 3D Drug-Receptor Complex - The PDB Contains Many Examples Computational Methodology Generic Name Other Name Treatment PDBid Lipitor Atorvastatin High cholesterol 1HWK, 1HW8… Testosterone 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
    17. 20. 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 … Computational Methodology Xie and Bourne 2009 Bioinformatics 25(12) 305-312
    18. 21. <ul><li>Initially assign C  atom with a value that is the distance to the environmental boundary </li></ul><ul><li>Update the value with those of surrounding C  atoms dependent on distances and orientation – atoms within a 10A radius define i </li></ul><ul><li>Conceptually similar to hydrophobicity </li></ul><ul><li>or electrostatic potential that is </li></ul><ul><li>dependant on both global and local </li></ul><ul><li>environments </li></ul>Characterization of the Ligand Binding Site - The Geometric Potential Xie and Bourne 2007 BMC Bioinformatics, 8(Suppl 4):S9 Computational Methodology
    19. 22. Discrimination Power of the Geometric Potential <ul><li>Geometric potential can distinguish binding and non-binding sites </li></ul>100 0 Geometric Potential Scale Computational Methodology Xie and Bourne 2007 BMC Bioinformatics, 8(Suppl 4):S9
    20. 23. 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 A Structure B <ul><li>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 </li></ul><ul><li>The maximum-weight clique corresponds to the optimum alignment of the two structures </li></ul>Xie and Bourne 2008 PNAS , 105(14) 5441 Computational Methodology
    21. 24. Similarity Matrix of Alignment <ul><li>Chemical Similarity </li></ul><ul><li>Amino acid grouping: (LVIMC), (AGSTP), (FYW), and (EDNQKRH) </li></ul><ul><li>Amino acid chemical similarity matrix </li></ul><ul><li>Evolutionary Correlation </li></ul><ul><li>Amino acid substitution matrix such as BLOSUM45 </li></ul><ul><li>Similarity score between two sequence profiles </li></ul>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 Computational Methodology Xie and Bourne 2008 PNAS , 105(14) 5441
    22. 25. The Future as a High Throughput Approach…..
    23. 26. The TB-Drugome <ul><li>Determine the TB structural proteome </li></ul><ul><li>Determine all known drug binding sites from the PDB </li></ul><ul><li>Determine which of the sites found in 2 exist in 1 </li></ul><ul><li>Call the result the TB-drugome </li></ul>A Multi-target/drug Strategy Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976
    24. 27. 1. Determine the TB Structural Proteome <ul><li>High quality homology models from ModBase (http://modbase.compbio.ucsf.edu) increase structural coverage from 7.1% to 43.3% </li></ul>284 1, 446 3, 996 2, 266 TB proteome homology models solved structures A Multi-target/drug Strategy Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976
    25. 28. 2. Determine all Known Drug Binding Sites in the PDB <ul><li>Searched the PDB for protein crystal structures bound with FDA-approved drugs </li></ul><ul><li>268 drugs bound in a total of 931 binding sites </li></ul>No. of drug binding sites Methotrexate Chenodiol Alitretinoin Conjugated estrogens Darunavir Acarbose A Multi-target/drug Strategy Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976
    26. 29. 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).
    27. 30. From a Drug Repositioning Perspective <ul><li>Similarities between drug binding sites and TB proteins are found for 61/268 drugs </li></ul><ul><li>41 of these drugs could potentially inhibit more than one TB protein </li></ul>No. of potential TB targets raloxifene alitretinoin conjugated estrogens & methotrexate ritonavir testosterone levothyroxine chenodiol A Multi-target/drug Strategy Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976
    28. 31. Top 5 Most Highly Connected Drugs Drug 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 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
    29. 32. What Have These Off-targets and Networks Told Us So Far? Some Examples… <ul><ul><li>Nothing </li></ul></ul><ul><ul><li>A possible explanation for a side-effect of a drug already on the market (SERMs - PLoS Comp. Biol. , 2007 3(11) e217) </li></ul></ul><ul><ul><li>The reason a drug failed (Torcetrapib - PLoS Comp Biol 2009 5(5) e1000387) </li></ul></ul><ul><ul><li>How to optimize a NCE ( NCE against T. Brucei PLoS Comp Biol. 2010 6(1): e1000648) </li></ul></ul><ul><ul><li>A multi-target/drug strategy to attack a pathogen (TB-drugome PLoS Comp Biol 2010 6(11): e1000976) </li></ul></ul><ul><ul><li>A possible repositioning of a drug (Nelfinavir) to treat a completely different condition (under review) </li></ul></ul>Our Stories
    30. 33. Nelfinavir <ul><li>Nelfinavir may have the most potent antitumor activity of the HIV protease inhibitors </li></ul><ul><li>Joell J. Gills et al, Clin Cancer Res, 2007; 13(17) </li></ul><ul><li>Warren A. Chow et al, The Lancet Oncology, 2009, 10(1) </li></ul><ul><li>Nelfinavir can inhibit receptor tyrosine kinase </li></ul><ul><li>Nelfinavir can reduce Akt activation </li></ul><ul><li>Our goal: </li></ul><ul><li>to identify off-targets of Nelfinavir in the human proteome </li></ul><ul><li>to construct an off-target binding network </li></ul><ul><li>to explain the mechanism of anti-cancer activity </li></ul>Possible Nelfinavir Repositioning
    31. 34. Possible Nelfinavir Repositioning
    32. 35. binding site comparison protein ligand docking MD simulation & MM/GBSA Binding free energy calculation structural proteome off-target? network construction & mapping drug target Clinical Outcomes 1OHR Possible Nelfinavir Repositioning
    33. 36. Binding Site Comparison <ul><li>5,985 structures or models that cover approximately 30% of the human proteome are searched against the HIV protease dimer (PDB id: 1OHR) </li></ul><ul><li>Structures with SMAP p-value less than 1.0e-3 were retained for further investigation </li></ul><ul><li>A total 126 Structures have significant p-values < 1.0e-3 </li></ul>Possible Nelfinavir Repositioning
    34. 37. Enrichment of Protein Kinases in Top Hits <ul><li>The top 7 ranked off-targets belong to the same EC family - aspartyl proteases - with HIV protease </li></ul><ul><li>Other off-targets are dominated by protein kinases (51 off-targets) and other ATP or nucleotide binding proteins (17 off-targets) </li></ul><ul><li>14 out of 18 proteins with SMAP p-values < 1.0e-4 are protein kinases </li></ul>Possible Nelfinavir Repositioning
    35. 38. Distribution of Top Hits on the Human Kinome p-value < 1.0e-3 p-value < 1.0e-4 Manning et al., Science , 2002, V298, 1912 Possible Nelfinavir Repositioning
    36. 39. 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 H-bond: Met793 with quinazoline N1 H-bond: Met793 with benzamide hydroxy O38 EGFR-DJK Co-crys ligand EGFR-Nelfinavir DJK = N-[4-(3-BROMO-PHENYLAMINO)-QUINAZOLIN-6-YL]-ACRYLAMIDE
    37. 40. Off-target Interaction Network Identified off-target Intermediate protein Pathway Cellular effect Activation Inhibition Possible Nelfinavir Repositioning
    38. 41. Summary <ul><li>The HIV-1 drug Nelfinavir appears to be a broad spectrum low affinity kinase inhibitor </li></ul><ul><li>Most targets are upstream of the PI3K/Akt pathway </li></ul><ul><li>Findings are consistent with the experimental literature </li></ul><ul><li>More direct experiment is needed </li></ul>Possible Nelfinavir Repositioning
    39. 42. The Future as a Dynamical Network Approach
    40. 43. Computational Evaluation of Drug Off-Target Effects 336 genes 1587 reactions Plos Comp. Biol. 2010 6(9): e1000938 Proteome Drug binding site alignments SMAP Predicted drug targets Drug and endogenous substrate binding site analysis Competitively inhibitable targets Inhibition simulations in context-specific model COBRA Toolbox Predicted causal targets and genetic risk factors Metabolic network Scientific literature Tissue and biofluid localization data Gene expression data Physiological objectives System exchange constraints Flux states optimizing objective Physiological context-specific model Influx Efflux Drug response phenotypes Drug targets Physiological objectives Causal drug targets All targets
    41. 44. Acknowledgements Sarah Kinnings Lei Xie Li Xie http://funsite.sdsc.edu http://www.slideshare.net/pebourne/ucl120810 Roger Chang Bernhard Palsson

    ×