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Thesis Defence 05-26-2016

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This thesis presents the development of computational methods and tools using as input three-dimensional structures data of protein-ligand complexes. The tools are useful to mine, profile and predict data from protein-ligand complexes to improve the modeling and the understanding of the protein-ligand recognition. This thesis is divided into five sub-projects. In addition, unpublished results about positioning water molecules in binding pockets are also presented. I developed a statistical model, PockDrug, which combines three properties (hydrophobicity, geometry and aromaticity) to predict the druggability of protein pockets, with results that are not dependent on the pocket estimation methods. The performance of pockets estimated on apo or holo proteins is better than that previously reported in the literature (Publication I). PockDrug is made available through a web server, PockDrug-Server (http://pockdrug.rpbs.univ-paris-diderot.fr), which additionally includes many tools for protein pocket analysis and characterization (Publication II). I developed a customizable computational workflow based on the superimposition of homologous proteins to mine the structural replacements of functional groups in the Protein Data Bank (PDB). Applied to phosphate groups, we identified a surprisingly high number of phosphate non-polar replacements as well as some mechanisms allowing positively charged replacements. In addition, we observed that ligands adopted a U-shape conformation at nucleotide binding pockets across phylogenetically unrelated proteins (Publication III). I investigated the prevalence of salt bridges at protein-ligand complexes in the PDB for five basic functional groups. The prevalence ranges from around 70% for guanidinium to 16% for tertiary ammonium cations, in this latter case appearing to be connected to a smaller volume available for interacting groups. In the absence of strong carboxylate-mediated salt bridges, the environment around the basic functional groups studied appeared enriched in functional groups with acidic properties such as hydroxyl, phenol groups or water molecules (Publication IV). I developed a tool that allows the analysis of binding poses obtained by docking. The tool compares a set of docked ligands to a reference bound ligand (may be different molecule) and provides a graphic output that plots the shape overlap and a Jaccard score based on comparison of molecular interaction fingerprints. The tool was applied to analyse the docking poses of active ligands at the orexin-1 and orexin-2 receptors found as a result of a combined virtual and experimental screen (Publication V). The review of literature focusses on protein-ligand recognition, presenting different concepts and current challenges in drug discovery.

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Thesis Defence 05-26-2016

  1. 1. University of Helsinki Université Paris Diderot Development of Computational Methods to Predict Protein Pocket Druggability and Profile Ligands using Structural Data Alexandre Borrel Defence of doctoral dissertation 26 May 2016 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 1
  2. 2. University of Helsinki Université Paris Diderot 26-05-20162 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Outlines Year 2 Year 1
  3. 3. University of Helsinki Université Paris Diderot 26-05-20163 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Background Development of Computational Methods to Predict Protein Pocket Druggability and Profile Ligands using Structural Data
  4. 4. University of Helsinki Université Paris Diderot 26-05-20164 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Background Development of Computational Methods to Predict Protein Pocket Druggability and Profile Ligands using Structural Data
  5. 5. University of Helsinki Université Paris Diderot 26-05-20165 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Background Development of Computational Methods to Predict Protein Pocket Druggability and Profile Ligands using Structural Data
  6. 6. University of Helsinki Université Paris Diderot 26-05-20166 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Structural data -7.265 20.187 20.701 -4.182 20.865 18.600 Structure of the biological macromolecules (protein) at an atomic level 3D coordinates (x, y, z) element (oxygen, nitrogen, carbon) -6.288 20.665 18.600 -4.288 21.665 15.600 -4.188 20.665 18.600 -3.089 20.665 18.600 -6.288 21.685 18.600 -6.288 20.665 18.600
  7. 7. University of Helsinki Université Paris Diderot 26-05-20167 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Issues with structural data 110 288 proteins structures (1) (May 2016) (1) Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., et al. (2000). Nucleic Acids Res. 28: 235–242. (2) Fersht, A.R. (2008) Nat. Rev. Mol. Cell Biol. 9: 650–654. (3) Tari, L.W. (2012). Structure-Based Drug Discovery (Totowa, NJ: Humana Press). Drug discovery (2-3): • Rationalize drug discovery • Open new trails of development • Reduce the cost and the time • …
  8. 8. University of Helsinki Université Paris Diderot 26-05-20168 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Background Development of Computational Methods to Predict Protein Pocket Druggability and Profile Ligands using Structural Data
  9. 9. University of Helsinki Université Paris Diderot 26-05-20169 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Predict the recognition Holy grail: predict recognition between a ligand and a target using only protein and ligand structure. Computational methodsTarget structure Ligand/drug structure
  10. 10. University of Helsinki Université Paris Diderot 26-05-201610 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Protein-ligand recognition “Lock-and-key”, Emil Fischer in 1894 (60 years before the first 3D structure) Fischer, E. Einfluss. Ber. Dtsch. Chem. Ges. 1894, 27, 2985–2993. Complementarity of shapes between a ligand (key) and a protein (lock).
  11. 11. University of Helsinki Université Paris Diderot 26-05-201611 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Protein-ligand recognition Koshland, D.E. (1958). Proc. Natl. Acad. Sci. U. S. A. 44: 98–104. “Induced-fit model” Daniel Koshland, 1958 Proteins and ligands adapt their conformations for the recognition.
  12. 12. University of Helsinki Université Paris Diderot 26-05-201612 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Challenges Many factors influence the protein-ligand recognition such as molecular interactions, environment (i.e. solvent), … Water ~4.6 water molecules by binding site (1) (1) Lu, Y., Wang, R., Yang, C.-Y., and Wang, S. (2007). J. Chem. Inf. Model. 47: 668–675. H-bond π-π hydrophobe Challenges: model all phenomena which explain the recognition.
  13. 13. University of Helsinki Université Paris Diderot 26-05-201613 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Aims of the thesis Develop computational methods useful for the ligand profiling and contributing in the improvement of the modeling of the protein-ligand recognition. Data analysis Pocket / target space Medicinal chemistry Molecular modeling ?
  14. 14. University of Helsinki Université Paris Diderot 26-05-201614 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Druggability model Year 2 Year 1 Recognition Structural data Protein target http://phdcomics.com/comics.php
  15. 15. University of Helsinki Université Paris Diderot 26-05-201615 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Binding sites A binding site will refer to the atoms of the amino acid at interacting distances (4 to 6 Å) of a bound ligand, and present at the surface of the binding region. Cavity Channel Protein-protein interphase
  16. 16. University of Helsinki Université Paris Diderot 26-05-201616 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Drug-like molecules Drug-like: compound with acceptable Absorption, Distribution, Metabolism, and Excretion – toxicity properties to become orally bioavailable drug (1-2). Rules of five (from 2 200 compounds in the United States Adopted Names directory) in 1997 (1): (1) Lipinski, C.A., Lombardo, F., Dominy, B.W., and Feeney, P.J. (2001). Adv. Drug Deliv. Rev. 46: 3–26. (2) Tian, S., Wang, J., Li, Y., Li, D., Xu, L., and Hou, T. (2015). Adv. Drug Deliv. Rev. 86: 2–10. • Molecular weight ≤ 500 Da • LogP ≤ 5 • H-bond acceptors ≤ 10 • H-bond donors ≤ 5 Ligand drug-like: Bisindolylmaleimide Inhibitor
  17. 17. University of Helsinki Université Paris Diderot 26-05-201617 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Drug-like molecules “Rules of five” are important to prioritize/rationalize the chemical space for virtual screening on the first drug discovery step (12 billion accessible molecules) (1-2) (1) Hann, M.M., and Oprea, T.I. (2004). Curr. Opin. Chem. Biol. 8: 255–263. (2) Ursu, O., Rayan, A., Goldblum, A., and Oprea, T.I. (2011). Rev. Comput. Mol. Sci. 1: 760–781. (3) Perola, E., Herman, L., and Weiss, J. (2012). J. Chem. Inf. Model. 52: 1027–1038. (4) Hopkins, A.A.L., and Groom, C.R.C. (2002). The druggable genome. Nat. Rev. Drug Discov. 1: 727–730. Druggability: “…defined as the ability of a target to bind a drug-like molecule with a therapeutically useful level of affinity.” (3-4)
  18. 18. University of Helsinki Université Paris Diderot 26-05-201618 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Protein druggability Similarly to the rules of five to rationalize the ligand space, druggability models are developed to rationalize the target space Statistical model Druggable ?
  19. 19. University of Helsinki Université Paris Diderot 26-05-201619 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 1. Pocket estimation Prediction of druggability (from properties of the know druggable pockets) 2. Model pockets 3. Statistical model A E TR Protein druggability Similarly to the rules of five to rationalize the ligand space, druggability models are developed to rationalize the target space
  20. 20. University of Helsinki Université Paris Diderot 26-05-201620 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Challenges Pocket estimation Availability « ...different pocket detection methods can assign different sizes and/or numbers of pockets for the same structure. » (1) Gao, M., & Skolnick, J. (2013). Bioinformatics (Oxford, England), 29(5), 597–604 Hajduk’s model SCREEN MAPPOD SiteMap DLID Huang’s model Huang’s model Fpocket DrugPred DoGSite-Scorer CAVITY-Score DrugFEATURE FTMap Druggability models are depending on a pocket estimation method, which limit their availability for pocket differently estimated using visual expertize for example.
  21. 21. University of Helsinki Université Paris Diderot 26-05-201621 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Pockets estimated on a same binding site have a weak average overlap (%) • Prox - Fpocket = 30 % (±14 %) • Prox - DoGSite = 28 % (± 14 %) • Fpocket- DoGSite = 30 % (± 16 %) Step 1: Pocket estimation Develop a druggability model which considers several pocket estimations We used three pocket estimation methods: • Ligand proximity (Prox) • Geometric approach (Fpocket) (1) • Energetic approach (DoGSite) (2) (1) Guilloux, V. Le, Schmidtke, P., and Tuffery, P. (2009). BMC Bioinformatics 10: 168. (2) Volkamer, A., Griewel, A., Grombacher, T., and Rarey, M. (2010). J. Chem. Inf. Model. 50: 2041–2052.
  22. 22. University of Helsinki Université Paris Diderot 26-05-201622 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Step 2: Pocket modeling Pocket are modeled using a set of 52 descriptors implemented Composition (1-2) (atomic and residues) Hydrophobicity (2-4) Geometry (5) (1) Milletti, F., and Vulpetti, A. (2010). J. Chem. Inf. Model. 50: 1418–1431. (2) Kyte, J., and Doolittle, R.F. (1982).J. Mol. Biol. 157: 105–132. (3) Eyrisch, S., and Helms, V. (2007). J. Med. Chem. 50: 3457–3464. (4) Hubbard, SJ and Thornton, J. (1992). NACCESS version 2.1.1. (5) Petitjean, M. (1992). J. Chem. Inf. Model. 32: 331–337. G A DY Aromatic Polar Charged
  23. 23. University of Helsinki Université Paris Diderot 26-05-201623 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Principal component analysis for pocket sets estimated differently (Prox, Fpocket and DoGSite) using a unique dataset of 111 binding sites (NRDLD) (1). Step 3: Pocket spaces (1) Krasowski, A., Muthas, D., Sarkar, A., Schmitt, S., and Brenk, R. (2011). J. Chem. Inf. Model. 51: 2829–2842.
  24. 24. University of Helsinki Université Paris Diderot 26-05-201624 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Druggable pockets Druggable and less druggable pocket spaces are separated in the projection. Volume Polarity Hydrophobicity Aromaticity
  25. 25. University of Helsinki Université Paris Diderot 26-05-201625 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Training phase Parsimonious linear discriminant analysis models (internal validation cross validation 10-folds) Selection of the models performing on different pockets sets estimated differently Consensus model (average of 7 linear discriminate analysis models)
  26. 26. University of Helsinki Université Paris Diderot 26-05-201626 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 External validation + 10% in accuracy + 0.20 in MCC Matthew’s Coefficient Correlation (MCC) (11) Desaphy, J., Azdimousa, K., Kellenberger, E., and Rognan, D. (2012). J. Chem. Inf. Model. 52: 2287–2299. (14) Krasowski, A., Muthas, D., Sarkar, A., Schmitt, S., and Brenk, R. (2011). J. Chem. Inf. Model. 51: 2829–2842. (10) Halgren, T. a (2009). J. Chem. Inf. Model. 49: 377–389. (12) Guilloux, V. Le, Schmidtke, P., and Tuffery, P. (2009). BMC Bioinformatics 10: 168. (15) Volkamer, A., Kuhn, D., Rippmann, F., and Rarey, M. (2012). Bioinformatics 28: 2074–2075.
  27. 27. University of Helsinki Université Paris Diderot 26-05-201627 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Output of PockDrug model Geometry Hydrophobicity Aromaticity Acetylcholinesterase complexed with Huprine 0.82 +/- 0.09 Druggable probability (Average) Confidence (Standard deviation) PockDrug combines three pocket properties i.e. geometry, hydrophobicity and the aromaticity
  28. 28. University of Helsinki Université Paris Diderot 26-05-201628 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 PockDrug model Druggability model developed, named PockDrug: • Robust for different pocket estimation methods • Exhibits better performances that other models in the literature • Define important global properties for the recognition (hydrophobicity, aromaticity and geometry) Borrel, A., Regad, L., Xhaard, H.G., Petitjean, M., and Camproux, A.-C. (2015). PockDrug: a model for predicting pocket druggability that overcomes pocket estimation uncertainties. J. Chem. Inf. Model. 55: 882–895.
  29. 29. University of Helsinki Université Paris Diderot 26-05-201629 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 PockDrug-Server Druggability model developed, named PockDrug: • Robust for different pocket estimation methods • Exhibits better performances that other models in the literature • Define important global properties for the recognition (hydrophobicity, aromaticity and geometry) http://pockdrug.rpbs.univ-paris-diderot.fr/ Borrel, A., Regad, L., Xhaard, H.G., Petitjean, M., and Camproux, A.-C. (2015). PockDrug: a model for predicting pocket druggability that overcomes pocket estimation uncertainties. J. Chem. Inf. Model. 55: 882–895. Hussein, H.A*., Borrel, A.*, Geneix, C., Petitjean, M., Regad, L., and Camproux, A.-C. (2015). PockDrug-Server: a new web server for predicting pocket druggability on holo and apo proteins. Nucleic Acids Res. 1–7.
  30. 30. University of Helsinki Université Paris Diderot 26-05-201630 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 PockDrug-Server
  31. 31. University of Helsinki Université Paris Diderot 26-05-201631 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 To the ligand profiling Which profile of ligands?
  32. 32. University of Helsinki Université Paris Diderot 26-05-201632 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Local structural replacements 2 year 1 year Recognition Structural data Druggability Binding site Ligand http://phdcomics.com/comics.php
  33. 33. University of Helsinki Université Paris Diderot 26-05-201633 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Drug optimization Hann, M.M. (2011). Medchemcomm 2: 349–355. Develop series of chemical modifications to modulate drug properties.
  34. 34. University of Helsinki Université Paris Diderot 26-05-201634 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Bioisosterism (1) Brown, N. (2014). Mol. Inform. 33: 458–462. (2) Southall, N.T., and Ajay (2006). J. Med. Chem. 49: 2103–2109 Example of bioisosteres from the kinase patent space (2) “Bioisosterism is the concept of similarity between functional groups or scaffolds in molecules that exhibit the same shape in terms of their potential biological interactions.”(1) Which replacements are possible?
  35. 35. University of Helsinki Université Paris Diderot Hypothesis: From two superimposed homologue proteins, chemical groups which occupy the same space may be bioisosteric replacements. 26-05-201635 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Local structure replacements Local structural replacement (LSR) Computational methods to extract the local structural replacements
  36. 36. University of Helsinki Université Paris Diderot 26-05-201636 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Study case: phosphate • Attractive target for therapeutic development (1). • 30% of the cellular proteins are phosphoproteins • Phosphate group is charged at biological pH, poorly permeable (2). ATP Phosphate groups (1) Cohen, P. (2000). Trends Biochem. Sci. 25: 596–601. (2) Smith, F.W., Mudge, S.R., Rae, A.L., and Glassop, D. (2003). Plant Soil 248: 71–83.
  37. 37. University of Helsinki Université Paris Diderot 26-05-201637 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Computational workflow
  38. 38. University of Helsinki Université Paris Diderot 26-05-201638 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Computational workflow 15 819 phosphate replacements
  39. 39. University of Helsinki Université Paris Diderot 26-05-201639 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Hierarchical organization • Local structure containing • 16 Protein family (KS = Kinase) • 70 clusters (30% of identity sequences) • LGD (Ligand) • LSR (Local Structure Replacements) • BS (Binding site, 4.5 Å)
  40. 40. University of Helsinki Université Paris Diderot 26-05-201640 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Hierarchical organization
  41. 41. University of Helsinki Université Paris Diderot 26-05-201641 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Phosphate is not replaced by the ligand but by the protein (flexible loop) PDB code: 3JZI – 1DV2 These observations are not quantitative in terms of affinity Mechanisms of replacements
  42. 42. University of Helsinki Université Paris Diderot 26-05-201642 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Congener series U-shape replacements, found in different protein families. Considering the congener series in different families, when the U-shape is destabilized the binding affinity decreases.
  43. 43. University of Helsinki Université Paris Diderot 26-05-201643 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Hydrophobic replacements, favour hydrophobic contacts in binding site. Miscellaneous replacements Positively charged replacement is surprising considering that the phosphate groups are negatively charged.
  44. 44. University of Helsinki Université Paris Diderot 26-05-201644 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Conclusion (LSR) • 15 819 phosphate replacements • Organization based on target and type of replacements • Discussion of some mechanisms for the recognition A. Borrel*; Y. Zhang*; L. Ghemtio; L. Regad; G. Boije af Gennäs; A.-C. Camproux; J. Yli- Kauhaluoma; H. Xhaard. Structural replacements of phosphate groups in the Protein Data Bank (Manuscript) Perspective: • Workflow is fully customizable
  45. 45. University of Helsinki Université Paris Diderot 26-05-201645 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Molecular interactions Year 2 Year 1 Recognition Structural data Molecular interactions Druggability Binding sites Ligand replacements http://phdcomics.com/comics.php
  46. 46. University of Helsinki Université Paris Diderot 26-05-201646 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Molecular interactions H-bond π-π “Intramolecular attractions or repulsions between atoms that are not directly linked to each other, affecting the thermodynamic stability of the chemical species concerned.” (IUPAC)
  47. 47. University of Helsinki Université Paris Diderot 26-05-201647 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Example of H-bond • Geometry (180°), distance criteria • Directionality • Partial charges Hydrogen bond is a non-bonded interaction where two electronegative atoms or group of atoms share a hydrogen.
  48. 48. University of Helsinki Université Paris Diderot 26-05-201648 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Challenges
  49. 49. University of Helsinki Université Paris Diderot 26-05-201649 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Challenges
  50. 50. University of Helsinki Université Paris Diderot 26-05-201650 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Challenges Distance (Å)
  51. 51. University of Helsinki Université Paris Diderot 26-05-201651 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Challenges However, some type of interactions i.e. salt-bridges combines different energy types, influencing the geometry and the strength. Also the environment or/and interaction network influences the binding interaction.
  52. 52. University of Helsinki Université Paris Diderot 26-05-201652 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Hypothesis of ionic groups PDB (~100 000 proteins structures), important diversity of interactions. Data-mining to investigate/model the neighborhood of these interactions. Six different ionic groups have characterized, only primary amine is presented. Qualitative and quantitative description of ionic interactions in the binding site based on their environments.
  53. 53. University of Helsinki Université Paris Diderot 26-05-201653 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Neighborhood 1 632 in protein-ligand interactions 154 979 in intra-protein interactions
  54. 54. University of Helsinki Université Paris Diderot 26-05-201654 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Neighborhood Neighborhood: group of 4 first atoms close to the primary amine Oxygen in carboxylate (Oox) Oxygen in water molecules (Ow) Oxygen in hydroxyl (Oh) Oxygen carbonyl (Oc) Nitrogen in amide (Nam) Nitrogen imidazole (Nim) Nitrogen in guanidinium (Ngu) Nitrogen in lysine (NaI) Carbon sp2 and nitrogen sp2 (aromatic) (Car) Other carbon or sulfur atom (Xot) Oh Oh Oox Oox Ow Ow Oh Oox Oh Nim Oc
  55. 55. University of Helsinki Université Paris Diderot 26-05-201655 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Neighborhood (first neighbors) Combination of the four first neighbors, distance is not considered Environments including a carboxylate (Oox) Quantitative analysis (50% of primary amines are ionized with a carboxylate) Preferential environments and also missing and poor represented environments.
  56. 56. University of Helsinki Université Paris Diderot 26-05-201656 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Modeling the environment Neighbors 1 2 3 4 Type Oox 234 400 320 123 Ow 789 457 690 389 Oh 589 673 590 499 … Contingency table by position 2D projections Correspondence analysis: dependency between the neighbor and the atom type.
  57. 57. University of Helsinki Université Paris Diderot 26-05-201657 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Modeling the environment Two environments are clearly different in terms of neighbors and closest atoms. +++ Carboxylate (Oox) ++ Hydroxyl (Oh) +++ water molecules (Ow) ++ Oxygen carbonyl (Oc) ++ Hydroxyl (Oh)
  58. 58. University of Helsinki Université Paris Diderot 26-05-201658 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Interaction modeling • Similar conclusions for intra-protein interactions and protein-ligand interaction. • Acidic and basic groups are interacting with a counter ion in 45-54% of cases. When functional groups of ionizable character are accounted (Oh, Ow) this number raise to 71%-100% of molecular complexes depending the functional group at hand. • Water molecules play a key role in the stabilization of polar groups especially in absence of salt bridges.
  59. 59. University of Helsinki Université Paris Diderot 26-05-201659 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Perspectives Perspectives • Docking scoring function, i.e. function which considers environment of ligand decomposition substructure. • Quantify the preference or missing environments of the interactions A. Borrel; A.-C. Camproux; H. Xhaard. Interactions of amine, carboxylic acid, imidazole, and guanidinium groups in proteins and protein-ligand complexes (Manuscript)
  60. 60. University of Helsinki Université Paris Diderot 26-05-201660 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Outlines 2 year 1 year Recognition Structural data Druggability Binding sites Ligand replacements Interaction
  61. 61. University of Helsinki Université Paris Diderot 26-05-201661 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 And water molecules 2 year 1 year Recognition Structural data Water molecules Ligand replacements Druggability Binding sites Interactions
  62. 62. University of Helsinki Université Paris Diderot 26-05-201662 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Water molecules • Poorly crystallized • Present only at very high resolution (< 1.5 Å) Method to position water molecules
  63. 63. University of Helsinki Université Paris Diderot 26-05-201663 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 In development Geometric based approach to position water molecules. Preliminary results: 80% of the water molecules are well repositioned.
  64. 64. University of Helsinki Université Paris Diderot 26-05-201664 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Conclusion 2 year 1 year Recognition Structural data Druggability Binding site Ligand replacements Interaction http://phdcomics.com/comics.php
  65. 65. University of Helsinki Université Paris Diderot 26-05-201665 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Conclusion Develop computational methods useful for the ligand profiling and contributing in the improvement of the modeling of the protein-ligand recognition. Data analysis Pocket / target space Medicinal chemistry Molecular modeling ?
  66. 66. University of Helsinki Université Paris Diderot 26-05-201666 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Conclusion Binding sites and the targets: druggability model
  67. 67. University of Helsinki Université Paris Diderot Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Conclusion Ligands: Methods for local structure replacements 26-05-2016 67 Binding sites and the targets: druggability model
  68. 68. University of Helsinki Université Paris Diderot 26-05-201668 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Conclusion Interactions: Methods for local replacements Binding sites and the targets: druggability model Ligands: Methods for local structure replacements
  69. 69. University of Helsinki Université Paris Diderot 26-05-201669 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Conclusion Interactions: Methods for local replacements Environment: positioning of water molecules Binding sites and the targets: druggability model Ligands: Methods for local structure replacements
  70. 70. University of Helsinki Université Paris Diderot H. Abi Hussein Dr. K. Audouze I. Allam Dr. A. Badel H. Borges J. Bécot Dr. D. Flatters C. Geneix Dr. M. Kuenemann Dr. D. Lagorce L. Legall Dr. M. Louet Dr. M. Miteva Dr. M. Petitjean I. Rasolohery Dr. L. Regad 26-05-201670 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Acknowledgements Laboratory MTi (Paris Diderot) Division of pharmaceutical chemistry and technology (Helsinki) Members of the jury Dr. O. Sperandio Prof. O. Taboureau I. Toussies D. Triki Dr. B. Villoutreix Dr. B. Zarzycka D. Brandao K. Culotta Dr. L. Ghemtio L. Kharu A. Legehar Dr. A. Magarkar M. Rinne M. Stepniewski V. Subramanian A. Turku F. Vedovi Dr. G. Wissel Dr. Y. Zhang Dr. N. Brown Prof. A.C. Camproux Prof. C. Etchebest Prof. B. Offmann Prof. A. Poso Dr. H. Xhaard Prof. J. Yli-Kauhaluoma
  71. 71. University of Helsinki Université Paris Diderot 26-05-201671 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Merci Thank you Kiitos
  72. 72. University of Helsinki Université Paris Diderot 26-05-201672 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Annexes
  73. 73. University of Helsinki Université Paris Diderot 26-05-201673 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Classification Dependency of the protein target = classification of different local structure replacements. Meanwell, N.A.N.N. a (2011). J. Med. Chem. 54: 2529–2591. Angiotensin II receptor antagonist analogs cPLAA2α inhibitor analogs + + + - - -
  74. 74. University of Helsinki Université Paris Diderot 26-05-201674 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Congener series “One of two or more substances related to each other by origin, structure, or function.” (IUPAC) Shin, Y., Chen, W., Habel, J., Duckett, D., Ling, Y.Y., Koenig, M., et al. (2009). Bioorganic Med. Chem. Lett. 19: 3344–3347. A group change and the affinity (IC50)
  75. 75. University of Helsinki Université Paris Diderot 26-05-201675 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Drug discovery Drug discovery: the process by which new candidate medications are discovered - Target identification - Affinity - Drug candidate - Lead selection - Lead optimization Kerns, E.; Di, L. Drug-like Properties: Concepts, Structure Design and Methods; Kerns, E., Ed.; Elsevier Inc., 2008. - Manufacturing - Side effects monitoring - Formulation - Phase 1: human safety - Phase 2: human efficiency - Phase 3: large scale efficiency
  76. 76. University of Helsinki Université Paris Diderot 26-05-201676 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Drug discovery Drug discovery: the process by which new candidate medications are discovered Long process ~20 years for a new drug Only 55 drugs approved by the Food and Drug Administration in 2015 Costly process $51.2 billion invested in 2014 in Biopharmaceutical Research Industry Mullard, A. 2015 FDA Drug Approvals. Nat. Publ. Gr. 2016, 15, 73–76 (PhRMA. Profile Biopharmaceutical Research Industry. Pharm. Res. Manuf. Am. 2015, 76..
  77. 77. University of Helsinki Université Paris Diderot 26-05-201677 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 ? Predict the recognition • Profile a ligand for a target (drug design) • Prioritize a research approach • Estimated side effects • Toxicity • … However, the protein-ligand recognition is a complex process which includes many factors difficult to model.
  78. 78. University of Helsinki Université Paris Diderot 26-05-201678 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Structure data, origins In 1958 John Kendrew and Max Perutz published the first high- resolution crystalized protein, sharing the Nobel prize in 1962. It was the first time where protein structure (protein = fundamental element for the biologic processes) was approached with an atomic level. Fersht, A.R. (2008). From the first protein structures to our current knowledge of protein folding: delights and scepticisms. Nat. Rev. Mol. Cell Biol. 9: 650–654. Kendrew, J.C., Bodo, G., Dintzis, H.M., Parrish, R.G., Wyckoff, H., and Phillips, D.C. (1958). A three-dimensional model of the myoglobin molecule obtained by x-ray analysis. Nature 181: 662–666. Perutz, M.F., Rossmann, M.G., Cullis, A.F., Muirhead, H., Will, G., and North, A.C. (1960). Structure of haemoglobin: a three-dimensional Fourier synthesis at 5.5-A. resolution, obtained by X-ray analysis. Nature 185: 416–422.
  79. 79. University of Helsinki Université Paris Diderot 26-05-201679 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Structure-based (and ligand-based) methods based on 3D structures. Protein crystallized X-rays are diffracted by each atoms presented in the crystal structure Structure data, today
  80. 80. University of Helsinki Université Paris Diderot 26-05-201680 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Importance Estimates suggest that around 10-15% of human genome may be druggable (with small molecule approach) and 600-1500 potential targets Druggability is important to: - prioritize potential targets - avert targets that are unlikely to bind small molecules with high affinity (optimize experimental screenings) - Rational the target space Human genome ~30,000 Druggable Genome ~3,000 Diseases modifying Genes ~3,000 Drug targets ~ 600-1,500
  81. 81. University of Helsinki Université Paris Diderot 26-05-201681 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Dataset Non redundant dataset: NRDLD (Non Redundant set of Druggable and Less Druggable binding sites) Adapted from Krasowski, A. et al. (2011). J. Med. Chem Inf, 51(11), 2829–42 Experimental: - HTS - NMR screening Database screening 71 druggable binding sites 44 less druggable binding sites Widely Characterized Apo protein set included in “Druggable Cavity Directory” (139 proteins)
  82. 82. University of Helsinki Université Paris Diderot 26-05-201682 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 2. Compute Linear Discriminant Analysis (LDA) models with n descriptors 1. Define training and test set by pocket estimation methods Learning phase
  83. 83. University of Helsinki Université Paris Diderot 26-05-201683 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 3. select best models with minimal number of descriptors Objective: - parsimonious model - Considering several pocket sets Matthew's Coefficient Correlation Consensus PockDrug Learning phase
  84. 84. University of Helsinki Université Paris Diderot 26-05-201684 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 PockDrug quality Consensus PockDrug prox- test DoGSite- test fpocket- test Acc 95 % 87 % 87 % MCC 0.89 0.73 0.71 Robust on estimations Good performances for different pocket sets fpocket- score DoGSite- Scorer Acc 76 % 76 % MCC 0.51 0.54 Better that other models fpocket- apo DoGSite- apo Acc 91 % 94 % MCC 0.45 0.53 Apo pockets
  85. 85. University of Helsinki Université Paris Diderot 26-05-201685 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 External validation PockDrug model was validated on different pocket test sets and was compared of other druggability models available in the literature Robust performances on different pocket test set.
  86. 86. University of Helsinki Université Paris Diderot 26-05-201686 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Properties Combination of 4 pocket properties Hydrophobicity Geometry Aromaticity Atom type Hydrophobicity ++++ Geometric +++ Atom type (H-bond donor-acceptor) ++ Aromaticity +
  87. 87. University of Helsinki Université Paris Diderot 26-05-201687 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 To the ligand profiling Which profile of ligand? Druggable pocket
  88. 88. University of Helsinki Université Paris Diderot Computational approaches are important to: • To identify LSR • Stock in databases 26-05-201688 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 In silico approaches 3 types of method to identify bioisosteres : • Rational approaches, based on similar compounds (BIOSTER or SwissBioisostere) • Literature searching (limited on precise case) • Chemoinformatics based on a investigation of the chemical space or protein complexes Devereux, M., and Popelier, P.L. a (2010). In silico techniques for the identification of bioisosteric replacements for drug design. Curr. Top. Med. Chem. 10: 657–668. Ujváry, I. (1997). BIOSTER-a database of structurally analogous compounds. Pestic. Sci. 51: 92–95. Wirth, M., Zoete, V., Michielin, O., and Sauer, W.H.B. (2013). SwissBioisostere: A database of molecular replacements for ligand design. Nucleic Acids Res. 41: 1137–1143.
  89. 89. University of Helsinki Université Paris Diderot Chemoinformatic approaches is based on investigation of chemical space or X-ray complexes. 26-05-201689 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Identification of LSR Fingerprint interaction Similarity of binding sites using pharmacophores Desaphy, J., and Rognan, D. (2014). Sc-PDB-Frag: A database of protein-ligand interaction patterns for bioisosteric replacements. J. Chem. Inf. Model. 54: 1908–1918. Wood, D.J., Vlieg, J. De, Wagener, M., and Ritschel, T. (2012). Pharmacophore fingerprint-based approach to binding site subpocket similarity and its application to bioisostere replacement. J. Chem. Inf. Model. 52: 2031–2043. Interaction Pharmacophores
  90. 90. University of Helsinki Université Paris Diderot 26-05-201690 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Molecular recognition Many parameters influence the molecular recognition, such as molecular interaction, flexibility, solvent exposition. A same binding site can host different ligand, modulating molecular interaction or binding site flexibility.
  91. 91. University of Helsinki Université Paris Diderot 26-05-201691 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Mechanisms of replacements Local substructures replace the metal The nitrogen replaces the metal and interactions with the protein are conserved. Phosphate Local replacement Nitrogen replace the Mg2+ PDB code: 3ULI – 4EOM
  92. 92. University of Helsinki Université Paris Diderot 26-05-201692 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Molecular interactions Hydrogen bonds Salt bridges π-πHalogen bonds cation-π anion-π
  93. 93. University of Helsinki Université Paris Diderot 26-05-201693 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Publication IV
  94. 94. University of Helsinki Université Paris Diderot 26-05-201694 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Publication IV
  95. 95. University of Helsinki Université Paris Diderot 26-05-201695 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Polar contacts • A sphere of 3.0 Å radius from point charges carry the majority of the information about polar contacts. • 80% of primary amine are ionized. Open some perspectives in interaction modeling where a distance of 4 Å is usually considered. Most frequent case the amine is ionized. Files, S., Sarthi, P., Gupta, S., Nayek, A., Banerjee, S., Seth, P., et al. (2015). SBION2 : Analyses of Salt Bridges from Multiple Structure Files, Version 2. 11: 2–5.
  96. 96. University of Helsinki Université Paris Diderot 26-05-201696 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Neighborhood analysis Oxygen in carboxylate Oxygen in hydroxyl Oxygen in water molecules Position of each atom type is discussed separately and considering the environment.
  97. 97. University of Helsinki Université Paris Diderot 26-05-201697 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Modeling the environment 2D projection, using a correspondence from the contingency table of all primary amine considered in the dataset. Two type of interactions are considered include a Oxygen carboxylate or not (‘) Consider the fourth neighbors atoms in the both environment Distance between the neighbor position and the type of atom characterize the dependence 1 +++ carboxylate 3 +++ hydroxyl
  98. 98. University of Helsinki Université Paris Diderot 26-05-201698 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Ionizable interactions In the type of the very strong molecular interaction coupling electrostatic interaction carried by the charges and a hydrogen-bond. Focused on a type of strong protein-ligand interaction, well characterized in the intra-protein interaction but poorly characterized in the protein-ligand interaction • Protein stability • Thermo-resistance • Molecular mechanisms (nucleation, enzyme process)
  99. 99. University of Helsinki Université Paris Diderot 26-05-201699 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 Publication V
  100. 100. University of Helsinki Université Paris Diderot 26-05-2016100 Division of pharmaceutical chemistry and technology – Faculty of pharmacy Molécules Thérapeutiques in Silico – INSERM UMRS 973 LDA Linear discriminant analysis (LDA) is a generalization of Fisher's linear discriminant, a method used in statistics, pattern recognition and machine learning to find a linear combination of features that characterizes or separates two or more classes of objects or events. The resulting combination may be used as a linear classifier, or, more commonly, for dimensionality reduction before later classification.

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