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Structure-Based Target Druggability Assessment

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Target Identification and Validation in Drug Discovery

Part of the book series: Methods in Molecular Biology ((MIMB,volume 986))

Abstract

The focus of this chapter is on the important concepts behind the in silico techniques that are used today to assess target druggability. The first step of the assessment consists of finding cavity space in the protein using 2D and/or 3D topological concepts. These concepts underlie the geometry and energy-based pocketfinder algorithms. Analysis pursues on the physico-chemical complementarity between the binding site and the drug like molecule. Geometrical and molecular flexibility aspect are also included in this assessment. The presence of hot interaction spots are shown to be particularly important for targeting protein-protein interactions. Finally, binding site promiscuity can be assessed by large scale structural comparison with other targets. Common chemical features amongst protein cavities can predict potential cross-reactivity with unwanted targets.

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References

  1. Sakharkar MK, Sakharkar KR (2007) Targetability of human disease genes. Curr Drug Disc Technol 4:48–58

    Article  CAS  Google Scholar 

  2. Wyatt PG, Gilbert IH, Read KD et al (2011) Target validation: linking target and chemical properties to desired product profile. Curr Topics Med Chem 11:1275–1283

    Article  CAS  Google Scholar 

  3. Taboureau O, Nielsen SK, Audouze K et al (2011) ChemProt: a disease chemical biology database. Nucleic Acids Res 39:D367–D372

    Article  PubMed  Google Scholar 

  4. Bender A, Young DW, Jenkins JL et al (2007) Chemogenomic data analysis: prediction of small-molecule targets and the advent of biological fingerprint. Comb Chem High Throughput Screen 10:719–731

    Article  PubMed  CAS  Google Scholar 

  5. Dixon SJ, Stockwell BR (2009) Identifying druggable disease-modifying gene products. Curr Opinion Chem Biol 13:549–555

    Article  CAS  Google Scholar 

  6. Caffrey CR, Rohwer A, Oellien F et al (2009) A comparative chemogenomics strategy to predict potential drug targets in the metazoan pathogen, Schistosomamansoni. PloS One 4:e4413

    Article  PubMed  CAS  Google Scholar 

  7. Bredel M, Jacoby E (2004) Chemogenomics: an emerging strategy for rapid target and drug discovery. Nat Rev Gen 5:262–275

    Article  CAS  Google Scholar 

  8. Li Q, Cheng T, Wang Y et al (2010) PubChem as a public resource for drug discovery. Drug Discov Today 15:1052–1057

    Article  PubMed  CAS  Google Scholar 

  9. Fauman EB, Rai BK, Huang ES (2011) Structure-based druggability assessment-identifying suitable targets for small molecule therapeutics. Curr Opin Chem Biol 15:463–468

    Article  PubMed  CAS  Google Scholar 

  10. Schmidtke P, Barril X (2010) Understanding and predicting druggability. A high-throughput method for detection of drug binding sites. J Med Chem 53:5858–5867

    Article  PubMed  CAS  Google Scholar 

  11. Huang N, Jacobson MP (2010) Binding-site assessment by virtual fragment screening. PloS One 5:e10109

    Article  PubMed  CAS  Google Scholar 

  12. Fuller JC, Burgoyne NJ, Jackson RM (2009) Predicting druggable binding sites at the protein–protein interface. Drug Discov Today 14:155–161

    Article  PubMed  CAS  Google Scholar 

  13. Sugaya N, Furuya T (2011) Dr. PIAS: an integrative system for assessing the druggability of protein–protein interactions. BMC Bioinformatics 9:12–50

    Google Scholar 

  14. Villoutreix BO, Bastard K, Sperandio O et al (2008) In silico-in vitro screening of protein–protein interactions: towards the next generation of therapeutics. Curr Pharm Biotechnol 9:103–122

    Article  PubMed  CAS  Google Scholar 

  15. Sperandio O, Reynès CH, Camproux AC et al (2010) Rationalizing the chemical space of protein–protein interaction inhibitors. Drug Discov Today 15:220–229

    Article  PubMed  CAS  Google Scholar 

  16. Panjkovich A, Daura X (2010) Assessing the structural conservation of protein pockets to study functional and allosteric sites: implications for drug discovery. BMC Struct Biol 10:9–33

    Article  PubMed  CAS  Google Scholar 

  17. Chène P (2008) Challenges in design of biochemical assays for the identification of small molecules to target multiple conformations of protein kinases. Drug Discov Today 13:522–529

    Article  PubMed  CAS  Google Scholar 

  18. Pérot S, Sperandio O, Miteva MA et al (2010) Druggable pockets and binding site centric chemical space: a paradigm shift in drug discovery. Drug Discov Today 15:656–667

    Article  PubMed  CAS  Google Scholar 

  19. Morris RRJ, Najmanovich RRJ, Kahraman A et al (2005) Real spherical harmonic expansion coefficients as 3D shape descriptors for protein binding pocket and ligand comparisons. Bioinformatics (Oxford, England) 21:2347–2355

    Article  CAS  Google Scholar 

  20. Mak L, Grandison S, Morris RJ (2008) An extension of spherical harmonics to region-based rotationally invariant descriptors for molecular shape description and comparison. J Mol Graph Model 26:1035–1045

    Article  PubMed  CAS  Google Scholar 

  21. Kleywegt GJ, Jones TA (1994) Detection, delineation, measurement and display of cavities in macromolecular structures. Acta Crystallogr D Biol Crystallogr 50:178–185

    Article  PubMed  CAS  Google Scholar 

  22. Poy F, Lepourcelet M, Shivdasani RA et al (2001) Structure of a human Tcf4-beta-catenin complex. Nat Struct Biol 8:1053–1057

    Article  PubMed  CAS  Google Scholar 

  23. Tagami S, Sekine S-I, Kumarevel T et al (2010) Crystal structure of bacterial RNA polymerase bound with a transcription inhibitor protein. Nature 468:978–982

    Article  PubMed  CAS  Google Scholar 

  24. Liang J, Edelsbrunner H, Woodward C (1998) Anatomy of protein pockets and cavities: measurement of binding site geometry and implications for ligand design. Protein Sci 7:1884–1897

    Article  PubMed  CAS  Google Scholar 

  25. Kawabata T, Go N (2007) Detection of pockets on protein surfaces using small and large probe spheres to find putative ligand binding sites. Bioinformatics 529:516–529

    Google Scholar 

  26. Kawabata T (2010) Detection of multiscale pockets on protein surfaces using mathematical morphology. Proteins 78:1195–1211

    Article  PubMed  CAS  Google Scholar 

  27. Goodford PJ (1985) A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J Med Chem 28:849–857

    Article  PubMed  CAS  Google Scholar 

  28. Weisel M, Proschak E, Schneider G (2007) PocketPicker: analysis of ligand binding-sites with shape descriptors. Chem Central J 1:7–24

    Article  CAS  Google Scholar 

  29. Tripathi A, Kellogg GE (2010) A novel and efficient tool for locating and characterizing protein cavities and binding sites. Proteins 78:825–842

    Article  PubMed  CAS  Google Scholar 

  30. Peters KP, Fauck J, Frömmel C (1996) The automatic search for ligand binding sites in proteins of known three-dimensional structure using only geometric criteria. J Mol Biol 256:201–213

    Article  PubMed  CAS  Google Scholar 

  31. Zhong S, MacKerell AD (2007) Binding response: a descriptor for selecting ligand binding site on protein surfaces. J Chem Inf Model 47:2303–2315

    Article  PubMed  CAS  Google Scholar 

  32. Binkowski TA, Naghibzadeh S, Liang J (2003) CASTp: computed atlas of surface topography of proteins. Bioinformatics 31:3352–3355

    CAS  Google Scholar 

  33. Petrek M, Otyepka M, Banás P et al (2006) CAVER: a new tool to explore routes from protein clefts, pockets and cavities. BMC Bioinformatics 7:316–325

    Article  PubMed  CAS  Google Scholar 

  34. Guilloux VL, Schmidtke P, Tuffery P (2009) Fpocket: an open source platform for ligand pocket detection. BMC Bioinformatics 11:1–11

    Google Scholar 

  35. Till MS, Ullmann GM (2010) McVol—a program for calculating protein volumes and identifying cavities by a Monte Carlo algorithm. J Mol Modeling 16:419–429

    Article  CAS  Google Scholar 

  36. Brady GP, Stouten PF (2000) Fast prediction and visualization of protein binding pockets with PASS. J Comput Aided Mol Des 14:383–401

    Article  PubMed  CAS  Google Scholar 

  37. Kalidas Y, Chandra N (2008) PocketDepth: a new depth based algorithm for identification of ligand binding sites in proteins. J Struct Biol 161:31–42

    Article  PubMed  CAS  Google Scholar 

  38. Nayal M, Honig B (2006) On the nature of cavities on protein surfaces: application to the identification of drug-binding sites. Proteins: Struct Funct Bioinformatics 63:892–906

    Article  CAS  Google Scholar 

  39. Tseng YY, Dupree C, Chen ZJ et al (2009) SplitPocket: identification of protein functional surfaces and characterization of their spatial patterns. Nucleic Acids Res 37:W384–W389

    Article  PubMed  CAS  Google Scholar 

  40. Laskowski RA (1995) SURFNET: a program for visualizing molecular surfaces, cavities, and intermolecular interactions. J Mol Graphics 13:307–308

    Article  Google Scholar 

  41. Huang B, Schroeder M (2006) LIGSITE csc: predicting ligand binding sites using the Connolly surface and degree of conservation. BMC Structural Biol 11:1–11

    Google Scholar 

  42. Kitchen DB, Decornez H, Furr JR et al (2004) Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov 3:935–949

    Article  PubMed  CAS  Google Scholar 

  43. Harris R, Olson AJ, Goodsell DS (2008) Automated prediction of ligand-binding sites in proteins. Proteins 70:1506–1517

    Article  PubMed  CAS  Google Scholar 

  44. Halgren T (2007) New method for fast and accurate binding-site identification and analysis. Chem Biol Drug Design 69:146–148

    Article  CAS  Google Scholar 

  45. An J, Totrov M, Abagyan R et al (2005) Pocketome via comprehensive identification and classification of ligand binding envelopes. Mol Cell Proteomics 4:752–761

    Article  PubMed  CAS  Google Scholar 

  46. Laurie ATR, Jackson RM (2005) Structural bioinformatics Q-SiteFinder: an energy-based method for the prediction of protein—ligand binding sites. Bioinformatics 21:1908–1916

    Article  PubMed  CAS  Google Scholar 

  47. Hernandez M, Ghersi D, Sanchez R (2009) SITEHOUND-web: a server for ligand binding site identification in protein structures. Nucleic Acids Res 37:W413–W416

    Article  PubMed  CAS  Google Scholar 

  48. Ruppert J, Welch W, Jain AN (1997) Automatic identification and representation of protein binding sites for molecular docking. Protein Sci 6:524–533

    Article  PubMed  CAS  Google Scholar 

  49. Lipinski C (2004) Lead- and drug-like compounds: the rule-of-five revolution. Drug Discov Today: Technologies 1:337–341

    Article  CAS  Google Scholar 

  50. Zhang M-Q, Wilkinson B (2007) Drug discovery beyond the “rule-of-five”. Curr Opinion Biotech 18:478–488

    Article  CAS  Google Scholar 

  51. Hopkins AL, Groom CR (2002) The druggable genome. Nat Rev Drug Discov 1:727–730

    Article  PubMed  CAS  Google Scholar 

  52. Costa PR, Acencio ML, Lemke N (2010) A machine learning approach for genome-wide prediction of morbid and druggable human genes based on systems-level data. BMC Genomics 5(Suppl 11):S9

    Article  Google Scholar 

  53. Lesburg CA, Cable MB, Ferrari E et al (1999) Crystal structure of the RNA-dependent RNA polymerase from hepatitis C virus reveals a fully encircled active site. Nat Struct Mol Biol 6:937–943

    Article  CAS  Google Scholar 

  54. Salah E, Ugochukwu E, Barr AJ et al (2011) Crystal structures of ABL-related gene (ABL2) in complex with imatinib, tozasertib (VX-680), and a type I inhibitor of the triazolecarbothioamide class. J Med Chem 54:2359–2367

    Article  PubMed  CAS  Google Scholar 

  55. Abad-Zapatero C, Metz JT (2005) Ligand efficiency indices as guideposts for drug discovery. Drug Discov Today 10:464–469

    Article  PubMed  Google Scholar 

  56. Ghersi D, Sanchez R (2009) Improving accuracy and efficiency of blind protein–ligand docking by focusing on predicted binding sites. Proteins 74:417–24

    Article  PubMed  CAS  Google Scholar 

  57. Hopkins AL, Groom CR, Alex A (2004) Ligand efficiency: a useful metric for lead selection. Drug Discov Today 9:430–431

    Article  PubMed  Google Scholar 

  58. Bohacek RS, McMartin C (1992) Definition and display of steric, hydrophobic, and hydrogen bonding properties of ligand binding sites in proteins using Lee and Richards accessible surface: validation of a high-resolution graphical tool for drug design. J Med Chem 35:1671–1684

    Article  PubMed  CAS  Google Scholar 

  59. Licata L, Briganti L, Peluso D et al (2011) MINT, the molecular interaction database: 2012 update. Nucleic Acids Res 40:D857–D861

    Article  PubMed  CAS  Google Scholar 

  60. Bourgeas R, Basse M-J, Morelli X et al (2010) Atomic analysis of protein–protein interfaces with known inhibitors: the 2P2I database. PloS One 5:e9598

    Article  PubMed  CAS  Google Scholar 

  61. Kozakov D, Hall DR, Chuang GY et al (2011) Structural conservation of druggable hot spots in protein–protein interfaces. Proc Natl Acad Sci USA 108:13528–13533

    Article  PubMed  CAS  Google Scholar 

  62. Trosset J-Y, Dalvit C, Knapp S et al (2006) Inhibition of protein–protein interactions: the discovery of druglike beta-catenin inhibitors by combining virtual and biophysical screening. Proteins 64:60–67

    Article  PubMed  CAS  Google Scholar 

  63. Fasolini M, Wu X, Flocco M et al (2003) Hot spots in Tcf4 for the interaction with beta-catenin. J Biol Chem 278:21092–21098

    Article  PubMed  CAS  Google Scholar 

  64. Clackson T, Wells JA (1995) A hot spot of binding energy in a hormone-receptor interface. Science 267:383–386

    Article  PubMed  CAS  Google Scholar 

  65. Kuttner YY, Engel S (2011) Protein hot spots—the islands of stability. J Mol Biol 415:419–428

    Article  PubMed  CAS  Google Scholar 

  66. Wanner J, Fry DC, Peng Z et al (2011) Druggability assessment of protein–protein interfaces. Future Med Chem 3:2021–2038

    Article  PubMed  CAS  Google Scholar 

  67. Metz A, Pfleger C, Kopitz H et al (2011) Hot spots and transient pockets: predicting the determinants of small-molecule binding to a protein–protein interface. J Chem Inf Model 52:120–133

    Article  PubMed  CAS  Google Scholar 

  68. Henrich S, Salo-Ahen OMH, Huang B et al. (2010) Computational approaches to identifying and characterizing protein binding sites for ligand design. J Mol Recognit 23:209–219.

    Google Scholar 

  69. McMartin C (2012) A geometry force field which converts low-resolution X-ray models to structures with properties found at ultra high resolution. Protein Sci 21:75–83

    Article  PubMed  CAS  Google Scholar 

  70. Knight ZA, Lin H, Shokat KM (2010) Targeting the cancer kinome through polypharmacology. Nat Rev Cancer 10:130–137

    Article  PubMed  CAS  Google Scholar 

  71. Hopkins AL (2008) Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol 4:682–690

    Article  PubMed  CAS  Google Scholar 

  72. Chikhi R, Sael L, Kihara D (2010) Real-time ligand binding pocket database search using local surface descriptors. Proteins 78:2007–2028

    Article  PubMed  CAS  Google Scholar 

  73. Das S, Kokardekar A, Breneman CM (2009) Rapid comparison of protein binding site surfaces with property encoded shape distributions. J Chem Inf Model 49:2863–2872

    Article  PubMed  CAS  Google Scholar 

  74. Ivanisenko VA, Pintus SS, Grigorovich DA et al (2004) PDBSiteScan: a program for searching for active, binding and posttranslational modification sites in the 3D structures of proteins. Nucleic Acids Res 32:W549–W554

    Article  PubMed  CAS  Google Scholar 

  75. Totrov M (2011) Ligand binding site superposition and comparison based on Atomic Property Fields: identification of distant homologues, convergent evolution and PDB-wide clustering of binding sites. BMC Bioinformatics 12:S35

    Article  PubMed  Google Scholar 

  76. Gold ND, Jackson RM (2005) A searchable database for comparing protein–ligand binding sites for the analysis of structure-function relationships. J Chem Inf Model 46:736–742

    Article  CAS  Google Scholar 

  77. Schmidtke P, Souaille C, Estienne F et al (2010) Large-scale comparison of four binding site detection algorithms. J Chem Inf Model 50:2191–2200

    Article  PubMed  CAS  Google Scholar 

  78. Feldman HJ, Labute P (2010) Pocket similarity: are alpha carbons enough? J Chem Inf Model 50:1466–1475

    Article  PubMed  CAS  Google Scholar 

  79. Fontaine F, Pastor M, Zamora I et al (2005) Anchor-GRIND: filling the gap between standard 3D QSAR and the GRid-INdependentdescriptors. J Med Chem 48:2687–2694

    Article  PubMed  CAS  Google Scholar 

  80. Crivori P, Zamora I, Speed B et al (2004) Model based on GRID-derived descriptors for estimating CYP3A4 enzyme stability of potential drug candidates. J Comput Aided Mol Des 18:155–166

    Article  PubMed  CAS  Google Scholar 

  81. Baroni M, Cruciani G, Sciabola S et al (2007) A common reference framework for analyzing/comparing proteins and ligands. Fingerprints for ligands and proteins (FLAP): theory and application. J Chem Inf Model 47:279–294

    Article  PubMed  CAS  Google Scholar 

  82. Ritchie DW, Venkatraman V (2010) Ultra-fast FFT protein docking on graphics processors. Bioinformatics 26:2398–2405

    Article  PubMed  CAS  Google Scholar 

  83. Katchalski-Katzir E (1992) Molecular surface recognition: determination of geometric fit between proteins and their ligands by correlation techniques. Proc Natl Acad Sci 89:2195–2199

    Article  PubMed  CAS  Google Scholar 

  84. Brenke R, Kozakov D, Chuang G-Y et al (2009) Fragment-based identification of druggable “hot spots” of proteins using Fourier domain correlation techniques. Bioinformatics 25:621–627

    Article  PubMed  CAS  Google Scholar 

  85. Doppelt-Azeroual O, Moriaud F, Adcock SA et al (2009) A review of MED-SuMo applications. Infect Disord Drug Targets 9:344–357

    Article  PubMed  CAS  Google Scholar 

  86. Jambon M, Andrieu O, Combet C et al (2005) The SuMo server: 3D search for protein functional sites. Bioinformatics 21:3929–3930

    Article  PubMed  CAS  Google Scholar 

  87. Moriaud F, Doppelt-Azeroual O, Martin L et al (2009) Computational fragment-based approach at PDB scale by protein local similarity. J Chem Inf Model 49:280–294

    Article  PubMed  CAS  Google Scholar 

  88. Doppelt-Azeroual O, Delfaud F, Moriaud F et al (2010) Fast and automated functional classification with MED-SuMo: an application on purine-binding proteins. Protein Sci 19:847–867

    Article  PubMed  CAS  Google Scholar 

  89. Schmitt S, Kuhn D, Klebe G (2002) A new method to detect related function among proteins independent of sequence and fold homology. J Mol Biol 323:387–406

    Article  PubMed  CAS  Google Scholar 

  90. Bartlett S, Beddard GS, Jackson RM et al (2005) Comparison of the ATP binding sites of protein kinases using conformationally diverse bisindolylmaleimides. Methods 127:11699–11708

    CAS  Google Scholar 

  91. Powers R, Copeland JC, Germer K et al (2006) Comparison of protein active site structures for functional annotation of proteins and drug design. Proteins 65:124–135

    Article  PubMed  CAS  Google Scholar 

  92. Kinoshita K, Furui J, Nakamura H (2002) Identification of protein functions from a molecular surface database, eF-site. J Struct Funct Genomics 2:9–22

    Article  PubMed  CAS  Google Scholar 

  93. Skolnick J, Brylinski M (2009) FINDSITE: a combined evolution/structure-based approach to protein function prediction. Brief Bio­inform 10:378–391

    Article  PubMed  CAS  Google Scholar 

  94. Shulman-Peleg A, Shatsky M, Nussinov R et al (2008) MultiBind and MAPPIS: webservers for multiple alignment of protein 3D-binding sites and their interactions. Nucleic Acids Res 36:W260

    Article  PubMed  CAS  Google Scholar 

  95. Minai R, Matsuo Y, Onuki H et al (2008) Method for comparing the structures of protein ligand-binding sites and application for predicting protein–drug interactions. Proteins: Struct Funct Bioinformatics 72:367–381

    Article  CAS  Google Scholar 

  96. Ausiello G, Via A, Helmer-Citterich M (2005) Query3d: a new method for high-throughput analysis of functional residues in protein structures. BMC Bioinformatics 4(Suppl 6):S5

    Article  CAS  Google Scholar 

  97. Schalon C, Surgand JS, Kellenberger E et al (2008) A simple and fuzzy method to align and compare druggable ligand-binding sites. Proteins: Struct Funct Bioinformatics 71:1755–1778

    Article  CAS  Google Scholar 

  98. Brakoulias A, Jackson RM (2004) Towards a structural classification of phosphate binding sites in protein–nucleotide complexes: an automated all-against-all structural comparison using geometric matching. Proteins: Struct Funct Bioinformatics 56:250–260

    Article  CAS  Google Scholar 

  99. Tomei L, Roussel A, Incitti I et al (1999) Crystal structure of the RNA-dependent RNA polymerase of hepatitis C virus. Proc Natl Acad Sci 23:13034–13039

    Google Scholar 

  100. Pons J-L, Labesse G (2009) @TOME-2: a new pipeline for comparative modeling of protein–ligand complexes. Nucleic Acids Res 37:W485–W491

    Article  PubMed  CAS  Google Scholar 

  101. Shulman-Peleg A, Nussinov R, Wolfson HJ (2004) Recognition of functional sites in protein structures. J Mol Biol 339:607–633

    Article  PubMed  CAS  Google Scholar 

  102. Zorn J, Wells J (2010) Turning enzymes on with small molecules. Nat Chem Biol 6:179–188

    Article  PubMed  CAS  Google Scholar 

  103. Hantschel O, Grebien F, Superti-furga G (2011) Targeting allosteric regulatory “Drugging the Undruggable” modules in oncoproteins. Cancer Res 2:829–830

    Google Scholar 

  104. Oberlin D, Scheraga HA (1998) B-spline method for energy minimization in grid-based molecular mechanics calculations. J Comp Chem 19:71–85

    Article  CAS  Google Scholar 

  105. Trosset J-Y, Scheraga HA (1999) Prodock: software package for protein modeling and docking. J Comp Chem 20:412–427

    Article  CAS  Google Scholar 

  106. Trosset J-Y, Scheraga HA (1999) Flexible docking simulations: scaled collective variable Monte Carlo minimization approach using Bezier splines, and comparison with a standard Monte Carlo algorithm. J Comp Chem 20:244–252

    Article  CAS  Google Scholar 

  107. Roisman LC, Piehler J, Trosset JY et al (2001) Structure of the interferon-receptor complex determined by distance constraints from double-mutant cycles and flexible docking. Proc Natl Acad Sci U S A 98:13231–13236

    Article  PubMed  CAS  Google Scholar 

  108. Trosset JY, Scheraga HA (1998) Reaching the global minimum in docking simulations: a Monte Carlo energy minimization approach using Bezier splines. Proc Natl Acad Sci USA 95:8011–8015

    Article  PubMed  CAS  Google Scholar 

  109. McMartin C, Bohacek RS (1997) QXP: powerful, rapid computer algorithms for structure-based drug design. J Comput Aided Mol Des 11:333–344

    Article  PubMed  CAS  Google Scholar 

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Acknowledgment

The author thanks Dr. Fabrice Moriaud for providing picture 5 on the MedSuMo software.

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Authors and Affiliations

  1. Sup’Biotech, Villejuif, France

    Jean-Yves Trosset

  2. Institut National de la Santé et de la Recherche Médicale (INSERM), UMRS 942, Hôpital Lariboisière, Paris, France

    Jean-Yves Trosset & Nicolas Vodovar

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  1. Jean-Yves Trosset
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  2. Nicolas Vodovar
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Editors and Affiliations

  1. Director, Department of Pharmacology, Boehringer Ingelheim RCV GmbH & Co KG, N/A, Vienna, N/A, Austria

    Jurgen Moll

  2. Project and Team Leader, Cell Biology, Nerviano Medical Sciences, Viale Pasteur 10, Nerviano, 20014, Milano, Italy

    Riccardo Colombo

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Trosset, JY., Vodovar, N. (2013). Structure-Based Target Druggability Assessment. In: Moll, J., Colombo, R. (eds) Target Identification and Validation in Drug Discovery. Methods in Molecular Biology, vol 986. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-311-4_10

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  • DOI: https://doi.org/10.1007/978-1-62703-311-4_10

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