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Interaction with specific HSP90 residues as a scoring function: validation in the D3R Grand Challenge 2015

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Abstract

Here is reported the development of a novel scoring function that performs remarkably well at identifying the native binding pose of a subset of HSP90 inhibitors containing aminopyrimidine or resorcinol based scaffolds. This scoring function is called PocketScore, and consists of the interaction energy between a ligand and three residues in the binding pocket: Asp93, Thr184 and a water molecule. We integrated PocketScore into a molecular docking workflow, and used it to participate in the Drug Design Data Resource (D3R) Grand Challenge 2015 (GC2015). PocketScore was able to rank 180 molecules of the GC2015 according to their binding affinity with satisfactory performance. These results indicate that the specific residues considered by PocketScore are determinant to properly model the interaction between HSP90 and its subset of inhibitors containing aminopyrimidine or resorcinol based scaffolds. Moreover, the development of PocketScore aimed at improving docking power while neglecting the prediction of binding affinities, suggesting that accurate identification of native binding poses is a determinant factor for the performance of virtual screens.

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References

  1. Sousa S, Ribeiro A, Coimbra J, Neves R, Martins S, Moorthy N, Fernandes P, Ramos M (2013) Curr Med Chem 20(18):2296

    Article  CAS  Google Scholar 

  2. Carlson HA, Smith RD, Damm-Ganamet KL, Stuckey JA, Ahmed A, Convery MA, Somers DO, Kranz M, Elkins PA, Cui G, Peishoff CE, Lambert MH, Dunbar JB Jr (2016) J Chem Inf Model 56(6):1063

  3. Carlson HA (2016) J Chem Inf Model 56(6):951

  4. Drug Design Data Resource (D3R) https://www.drugdesigndata.org/. Accessed 19 May 2016

  5. Cheng T, Li X, Li Y, Liu Z, Wang R (2009) J Chem Inf Model 49(4):1079

    Article  CAS  Google Scholar 

  6. Li Y, Han L, Liu Z, Wang R (2014) J Chem Inf Model 54(6):1717

    Article  CAS  Google Scholar 

  7. Warren GL, Andrews CW, Capelli AM, Clarke B, LaLonde J, Lambert MH, Lindvall M, Nevins N, Semus SF, Senger S et al (2006) J Med Chem 49(20):5912

    Article  CAS  Google Scholar 

  8. Politi R, Convertino M, Popov K, Dokholyan NV, Tropsha A (2016) J Chem Inf Model 56(6):1032

  9. Gabel J, Desaphy J, Rognan D (2014) J Chem Inf Model 54(10):2807

    Article  CAS  Google Scholar 

  10. Anighoro A, Bajorath J (2016) J Chem Inf Model 56(3):580

    Article  CAS  Google Scholar 

  11. Bjerrum EJ (2016) Comput Biol Chem 62(2016):133

  12. Trott O, Olson AJ (2010) J Comput Chem 31(2):455

    CAS  Google Scholar 

  13. Mysinger MM, Carchia M, Irwin JJ, Shoichet BK (2012) J Med Chem 55(14):6582

    Article  CAS  Google Scholar 

  14. http://dude.docking.org/targets/hs90a/P07900/pdb_codes.txt. Accessed 20 May 2016

  15. PDB IDs in the training set: 1uy6, 1uy7, 1uy8, 1uy9, 1uyc, 1uyd, 1uye, 1uyf, 1uyg, 1uyh, 1uyi, 1uyk, 1yc3, 1yc4, 2bsm, 2bt0, 2byh, 2byi, 2bz5, 2ccs, 2cct, 2ccu, 2fwy, 2fwz, 2h55, 2jjc, 2qf6, 2qfo, 2qg0, 2qg2, 2uwd, 2vci, 2vcj, 2wi1, 2wi4, 2wi5, 2wi6, 2wi7, 2xab, 2xdk, 2xdl, 2xds, 2xdx, 2xhr, 2xht, 2xhx, 2xjg, 2xjj, 2xjx, 2xk2, 3bm9, 3bmy, 3d0b, 3eko, 3ekr, 3ft5, 3ft8, 3hek, 3hyy, 3hyz, 3hz5, 3inw, 3inx, 3k97, 3k98, 3k99, 3mnr, 4ykr, 4yky

  16. Koes DR, Baumgartner MP, Camacho CJ (2013) J Chem Inf Model 53(8):1893

    Article  CAS  Google Scholar 

  17. Yan A, Grant GH, Richards WG (2008) J R Soc Interface 5(Suppl 3):199

    Article  Google Scholar 

  18. Gupta SD, Snigdha D, Mazaira GI, Galigniana MD, Subrahmanyam C, Gowrishankar N, Raghavendra N (2014) Biomed Pharmacother 68(3):369

    Article  Google Scholar 

  19. Kung PP, Sinnema PJ, Richardson P, Hickey MJ, Gajiwala KS, Wang F, Huang B, McClellan G, Wang J, Maegley K et al (2011) Bioorg Med Chem Lett 21(12):3557

    Article  CAS  Google Scholar 

  20. Wright L, Barril X, Dymock B, Sheridan L, Surgenor A, Beswick M, Drysdale M, Collier A, Massey A, Davies N et al (2004) Chem Biol 11(6):775

    Article  CAS  Google Scholar 

  21. http://dude.docking.org/targets/hs90a/pdb_analyze.txt. Accessed 20 May 2016

  22. Li H, Leung KS, Wong MH, Ballester PJ (2015) Mol Inform 34(2–3):115

    Article  Google Scholar 

  23. Murphy RB, Repasky MP, Greenwood JR, Tubert-Brohman I, Jerome S, Annabhimoju R, Boyles NA, Schmitz CD, Abel R, Farid R, Friesner RA (2016) J Med Chem 59(9):4364

  24. Barril X, Morley SD (2005) J Med Chem 48(13):4432

    Article  CAS  Google Scholar 

  25. OLBoyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR (2011) J Cheminform 3:33

    Article  Google Scholar 

  26. OBoyle NM, Morley C, Hutchison GR (2008) Chem Cent J 2:5

    Article  Google Scholar 

  27. Jones E, Oliphant T, Peterson P et al (2001) SciPy: open source scientific tools for Python. http://www.scipy.org/. Accessed 13 May 2016

  28. Zhao W, Hevener KE, White SW, Lee RE, Boyett JM (2009) BMC Bioinform 10(1):1

    Article  Google Scholar 

  29. Mysinger MM, Shoichet BK (2010) J Chem Inf Model 50(9):1561

    Article  CAS  Google Scholar 

  30. Novikov FN, Stroylov VS, Stroganov OV, Chilov GG (2010) J Mol Model 16(7):1223

    Article  CAS  Google Scholar 

  31. Jia J, Xu X, Liu F, Guo X, Zhang M, Lu M, Xu L, Wei J, Zhu J, Zhang S et al (2013) PloS One 8(4):e59315

    Article  CAS  Google Scholar 

  32. Sun HP, Jia JM, Jiang F, Xu XL, Liu F, Guo XK, Cherfaoui B, Huang HZ, Pan Y, You QD (2014) Eur J Med Chem 79:399

    Article  CAS  Google Scholar 

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Acknowledgments

I thank Pedro A. Fernandes and António J. M. Ribeiro for critical reading of the manuscript, Maria João Ramos for providing all the material and intellectual conditions that supported this work, and Fundação para a Ciência e Tecnologia for scholarship SFRH/BD/84922/2012

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  1. UCIBIO, REQUIMTE, Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, 4169-007, Porto, Portugal

    Diogo Santos-Martins

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  1. Diogo Santos-Martins
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Correspondence to Diogo Santos-Martins.

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Santos-Martins, D. Interaction with specific HSP90 residues as a scoring function: validation in the D3R Grand Challenge 2015. J Comput Aided Mol Des 30, 731–742 (2016). https://doi.org/10.1007/s10822-016-9943-y

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  • DOI: https://doi.org/10.1007/s10822-016-9943-y

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