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Comparing pharmacophore models derived from crystallography and NMR ensembles

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Abstract

NMR and X-ray crystallography are the two most widely used methods for determining protein structures. Our previous study examining NMR versus X-Ray sources of protein conformations showed improved performance with NMR structures when used in our Multiple Protein Structures (MPS) method for receptor-based pharmacophores (Damm, Carlson, J Am Chem Soc 129:8225–8235, 2007). However, that work was based on a single test case, HIV-1 protease, because of the rich data available for that system. New data for more systems are available now, which calls for further examination of the effect of different sources of protein conformations. The MPS technique was applied to Growth factor receptor bound protein 2 (Grb2), Src SH2 homology domain (Src-SH2), FK506-binding protein 1A (FKBP12), and Peroxisome proliferator-activated receptor-γ (PPAR-γ). Pharmacophore models from both crystal and NMR ensembles were able to discriminate between high-affinity, low-affinity, and decoy molecules. As we found in our original study, NMR models showed optimal performance when all elements were used. The crystal models had more pharmacophore elements compared to their NMR counterparts. The crystal-based models exhibited optimum performance only when pharmacophore elements were dropped. This supports our assertion that the higher flexibility in NMR ensembles helps focus the models on the most essential interactions with the protein. Our studies suggest that the “extra” pharmacophore elements seen at the periphery in X-ray models arise as a result of decreased protein flexibility and make very little contribution to model performance.

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References

  1. Sanders MPA, McGuire R, Roumen L, de Esch IJP, de Vlieg J, Klomp JPG, de Graaf C (2012) From the protein’s perspective: the benefits and challenges of protein structure-based pharmacophore modeling. Med Chem Commun 3:28–38

    Article  CAS  Google Scholar 

  2. Qing X, Lee XY, De Raeymaeker J, Tame JRH, Zhang KYJ, De Maeyer M, Voet ARD (2014) Pharmacophore modeling: advances, limitations, and current utility in drug discovery. J Recept Lig Channel Res 7:81–92

    CAS  Google Scholar 

  3. Meslamani J, Rognan D (2015) Protein-ligand pharmacophores: concept, design and applications. CICSJ Bull 33:27–32

    Google Scholar 

  4. Koes DR (2016) Pharmacophore modeling: methods and applications. In: Zhang W (ed) Computer-aided drug discovery. methods in pharmacology and toxicology. Humana Press, New York, pp 167–188

    Google Scholar 

  5. Wieder M, Garon A, Perricone U, Boresch S, Seidel T, Almerico AM, Langer T (2017) Common hits approach: combining pharmacophore modeling and molecular dynamics simulations. J Chem Inf Model 57:365–385

    Article  CAS  Google Scholar 

  6. Zou J, Xie H-Z, Yang S-Y, Chen J-J, Ren J-X, Wei Y-Q (2008) Towards more accurate pharmacophore modeling: multicomplex-based comprehensive pharmacophore map and most-frequent-feature pharmacophore model of CDK2. J Mol Graph Model 27:430–438

    Article  CAS  Google Scholar 

  7. Wu F, Xu T, He G, Ouyang L, Han B, Peng C, Song X, Xiang M (2012) Discovery of novel focal adhesion kinase inhibitors using a hybrid protocol of virtual screening approach based on multicomplex-based pharmacophore and molecular docking. Int J Mol Sci 13:15668–15678

    Article  CAS  Google Scholar 

  8. Rakers C, Schumacher F, Meinl W, Glatt H, Kleuser B, Wolber G (2016) In silico prediction of human sulfotransferase 1E1 activity guided by pharmacophores from molecular dynamics simulations. J Biol Chem 291:58–71

    Article  CAS  Google Scholar 

  9. Tian S, Sun H, Li Y, Pan P, Li D, Hou T (2013) Development and evaluation of an integrated virtual screening strategy by combining molecular docking and pharmacophore searching based on multiple protein structures. J Chem Inf Model 53:2743–2756

    Article  CAS  Google Scholar 

  10. Damm KL, Carlson HA (2007) Exploring experimental sources of multiple protein conformations in structure-based drug design. J Am Chem Soc 129:8225–8235

    Article  CAS  Google Scholar 

  11. Wenbo Y, Lakkaraju SK, Raman EP, MacKerell AD Jr (2014) Site-identification by ligand competitive saturation (SILCS) assisted pharmacophore modeling. J Comput Aided Mol Des 28:491–507

    Article  Google Scholar 

  12. Wenbo Y, Lakkaraju SK, Raman EP, Fang L, MacKerell AD Jr (2015) Pharmacophore modeling using site-identification by ligand competitive saturation (SILCS) with multiple probe molecules. J Chem Inf Model 55:407–420

    Article  Google Scholar 

  13. Spyrakis F, Benedetti P, Decherchi S, Rocchia W, Cavalli A, Alcaro S, Ortuso F, Baroni M, Cruciani G (2015) A pipeline to enhance ligand virtual screening: integrating molecular dynamics and fingerprints for ligand and proteins. J Chem Inf Model 55:2256–2274

    Article  CAS  Google Scholar 

  14. Bowman AL, Makriyannis A (2011) Approximating protein flexibility through dynamic pharmacophore models: application to fatty acid amide hydrolase (FAAH). J Chem Inf Model 51:3247–3253

    Article  CAS  Google Scholar 

  15. Choudhury C, Priyakumar UD, Sastry GN (2015) Dynamics based pharmacophore models for screening potential inhibitors of mycobacterial cyclopropane synthase. J Chem Inf Model 55:848–860

    Article  CAS  Google Scholar 

  16. Baptista SJ, Silva MMC, Moroni E, Meli M, Colombo G, Dinis TCP, Salvador JAR (2017) Novel PARP-1 inhibitor scaffolds disclosed by a dynamic structure-based pharmacophore approach. PLoS ONE 12:e0170846

    Article  Google Scholar 

  17. Carlson HA, Masukawa KM, McCammon JA (1999) Method for including the dynamic fluctuations of a protein in computer-aided drug design. J Phys Chem A 103:10213–10219

    Article  CAS  Google Scholar 

  18. Carlson HA, Masukawa KM. Rubins K, Bushman FD, Jorgensen WL, Lins RD, Briggs JM, McCammon JA (2000) Developing a dynamic pharmacophore model for HIV-1 integrase. J Med Chem 43:2100–2114

    Article  CAS  Google Scholar 

  19. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucleic Acids Res 28:235–242

    Article  CAS  Google Scholar 

  20. Chen VB, Arendall WB 3rd, Headd JJ, Keedy DA, Immormino RM, Kapral GJ, Murray LW, Richardson JS, Richardson DC (2010) MolProbity: all-atom structure validation for macromolecular crystallography. Acta Crystallogr D Biol Crystallogr 66:12–21

    Article  CAS  Google Scholar 

  21. Halgren TA (1996) Merck molecular force field. I. basis, form, scope, parameterization, and performance of MMFF94. J Comput Chem 17:490–519

    Article  CAS  Google Scholar 

  22. Molecular Operating Environment (2010) Chemical Computing Group Inc. Montreal, Canada

  23. The PyMOL Molecular Graphics System (2010) Schrödinger LLC: New York

  24. Jorgensen WL (2000) BOSS. Yale University, New Haven

    Google Scholar 

  25. Lerner MG, Meagher KL, Carlson HA (2008) Automated clustering of probe molecules from solvent mapping of protein surfaces: new algorithms applied to hot-spot mapping and structure-based drug design. J Comput Aided Mol Des 22:727–736

    Article  CAS  Google Scholar 

  26. Damm KL, Carlson HA (2006) Gaussian-weighted RMSD superposition of proteins: a structural comparison for flexible proteins and predicted protein structures. Biophys J 90:4558–4573

    Article  CAS  Google Scholar 

  27. Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, Light Y, McGlinchey S, Michalovich D, Al-Lazikani B, Overington JP (2012) ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 40:D1100–D1107

    Article  CAS  Google Scholar 

  28. Durant JL, Leland BA, Henry DR, Nourse JG (2002) Reoptimization of MDL keys for use in drug discovery. J Chem Inf Model 42:1273–1280

    CAS  Google Scholar 

  29. Comprehensive Medicinal Chemistry Database (2003) MDL Information Systems. San Leandro

  30. Bowman AL, Lerner MG, Carlson HA (2007) Protein flexibility and species specificity in structure-based drug discovery: dihydrofolate reductase as a test system. J Am Chem Soc 129:3634–3640

    Article  CAS  Google Scholar 

  31. OEGraphSim (2010) OpenEye Scientific Software Inc: Santa Fe

  32. OMEGA (2010) OpenEye Scientific Software Inc: Santa Fe

  33. Xu W, Harrison SC, Eck MJ (1997) Three-dimensional structure of the tyrosine kinase c-Src. Nature 385:595–602

    Article  CAS  Google Scholar 

  34. Zhou S, Shoelson SE, Chaudhuri M, Gish G, Pawson T, Haser WG, King F, Roberts T, Ratnofsky S, Lechleider RJ, Neel BG, Birge RB, Fajardo JE, Chou MM, Hanafusa H, Schaffhausen B, Cantley LC (1993) SH2 domains recognize specific phosphopeptide sequences. Cell 72:767–778

    Article  Google Scholar 

  35. Eck MJ, Shoelson SE, Harrison SC (1993) Recognition of a high-affinity phosphotyrosyl peptide by the Src homology-2 domain of p56lck. Nature 362:87–91

    Article  CAS  Google Scholar 

  36. Rahuel J, Gay B, Erdmann D, Strauss A, Garcia-Echeverria C, Furet P, Caravatti G, Fretz H, Schoepfer J, Grutter MG (1996) Structural basis for specificity of GRB2-SH2 revealed by a novel ligand binding mode. Nat Struct Mol Biol 3:586–589

    Article  CAS  Google Scholar 

  37. Brown EJ, Albers MW, Shin TB, Ichikawa K, Keith CT, Lane WS, Schreiber SL (1994) A mammalian protein targeted by G1-arresting rapamycin-receptor complex. Nature 369:756–758

    Article  CAS  Google Scholar 

  38. Liu J, Farmer JD Jr., Lane WS, Friedman J, Weissman I, Schreiber SL (1991) Calcineurin is a common target of cyclophilin-cyclosporin A and FKBP-FK506 complexes. Cell 66:807–815

    Article  CAS  Google Scholar 

  39. Hamilton GS, Steiner JP (1998) Immunophilins: beyond Immunosuppression. J Med Chem 41:5119–5143

    Article  CAS  Google Scholar 

  40. Schreiber SL (1991) Chemistry and biology of the immunophilins and their immunosuppressive ligands. Science 251:283–287

    Article  CAS  Google Scholar 

  41. Tontonoz P, Spiegelman BM (2008) Fat and beyond: the diverse biology of PPARγ. Annu Rev Biochem 77:289–312

    Article  CAS  Google Scholar 

  42. Nolte RT, Wisely GB, Westin S, Cobb JE, Lambert MH, Kurokawa R, Rosenfeld MG, Willson TM, Glass CK, Milburn MV (1998) Ligand binding and co-activator assembly of the peroxisome proliferator-activated receptor-γ. Nature 395:137–143

    Article  CAS  Google Scholar 

  43. Lerner MG, Bowman AL, Carlson HA (2007) Incorporating dynamics in E. Coli dihydrofolate reductase enhances structure-based drug discovery. J Chem Inf Model 47:2358–2365

    Article  CAS  Google Scholar 

  44. Bradshaw JM, Grucza RA, Ladbury JE, Waksman G (1998) Probing the “Two-Pronged Plug Two-Holed Socket” model for the mechanism of binding of the Src SH2 domain to phosphotyrosyl peptides: a thermodynamic study. BioChemistry 37:9083–9090

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We greatly appreciate the generous donation of the MOE software from Chemical Computing Group and OMEGA and OEGraphSim from OpenEye Scientific Software, Inc. This work has been supported by the National Institutes of Health (GM65372).

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Correspondence to Heather A. Carlson.

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Ghanakota, P., Carlson, H.A. Comparing pharmacophore models derived from crystallography and NMR ensembles. J Comput Aided Mol Des 31, 979–993 (2017). https://doi.org/10.1007/s10822-017-0077-7

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