Skip to main content

Advertisement

SpringerLink
Log in

Combining in silico and in cerebro approaches for virtual screening and pose prediction in SAMPL4

  • Published:
Journal of Computer-Aided Molecular Design Aims and scope Submit manuscript

Abstract

The SAMPL challenges provide an ideal opportunity for unbiased evaluation and comparison of different approaches used in computational drug design. During the fourth round of this SAMPL challenge, we participated in the virtual screening and binding pose prediction on inhibitors targeting the HIV-1 integrase enzyme. For virtual screening, we used well known and widely used in silico methods combined with personal in cerebro insights and experience. Regular docking only performed slightly better than random selection, but the performance was significantly improved upon incorporation of additional filters based on pharmacophore queries and electrostatic similarities. The best performance was achieved when logical selection was added. For the pose prediction, we utilized a similar consensus approach that amalgamated the results of the Glide-XP docking with structural knowledge and rescoring. The pose prediction results revealed that docking displayed reasonable performance in predicting the binding poses. However, prediction performance can be improved utilizing scientific experience and rescoring approaches. In both the virtual screening and pose prediction challenges, the top performance was achieved by our approaches. Here we describe the methods and strategies used in our approaches and discuss the rationale of their performances.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Heikamp K, Bajorath J (2013) The future of virtual compound screening. Chem Biol Drug Des 81(1):33–40

    Article  CAS  Google Scholar 

  2. Lill M (2013) Virtual screening in drug design. Methods Mol Biol 993:1–12

    Article  CAS  Google Scholar 

  3. Geballe MT, Skillman AG, Nicholls A, Guthrie JP, Taylor PJ (2010) The SAMPL2 blind prediction challenge: introduction and overview. J Comput Aided Mol Des 24(4):259–279

    Article  CAS  Google Scholar 

  4. Guthrie JP (2009) A blind challenge for computational solvation free energies: introduction and overview. J Phys Chem B 113(14):4501–4507

    Article  CAS  Google Scholar 

  5. Nicholls A, Mobley DL, Guthrie JP, Chodera JD, Bayly CI, Cooper MD, Pande VS (2008) Predicting small-molecule solvation free energies: an informal blind test for computational chemistry. J Med Chem 51(4):769–779

    Article  CAS  Google Scholar 

  6. Skillman AG (2012) SAMPL3: blinded prediction of host-guest binding affinities, hydration free energies, and trypsin inhibitors. J Comput Aided Mol Des 26(5):473–474

    Article  CAS  Google Scholar 

  7. Voet AR, Maeyer MD, Debyser Z, Christ F (2009) In search of second-generation HIV integrase inhibitors: targeting integration beyond strand transfer. Future Med Chem 1(7):1259–1274

    Article  CAS  Google Scholar 

  8. Peat TS, Rhodes DI, Vandegraaff N, Le G, Smith JA, Clark LJ, Jones ED, Coates JA, Thienthong N, Newman J, Dolezal O, Mulder R, Ryan JH, Savage GP, Francis CL, Deadman JJ (2012) Small molecule inhibitors of the LEDGF site of human immunodeficiency virus integrase identified by fragment screening and structure based design. PLoS ONE 7(7):e40147

    Article  CAS  Google Scholar 

  9. Busschots K, Voet A, De Maeyer M, Rain JC, Emiliani S, Benarous R, Desender L, Debyser Z, Christ F (2007) Identification of the LEDGF/p75 binding site in HIV-1 integrase. J Mol Biol 365(5):1480–1492

    Article  CAS  Google Scholar 

  10. Hombrouck A, De Rijck J, Hendrix J, Vandekerckhove L, Voet A, De Maeyer M, Witvrouw M, Engelborghs Y, Christ F, Gijsbers R, Debyser Z (2007) Virus evolution reveals an exclusive role for LEDGF/p75 in chromosomal tethering of HIV. PLoS Pathog 3(3):e47

    Article  Google Scholar 

  11. Cavalluzzo C, Christ F, Voet A, Sharma A, Singh BK, Zhang KY, Lescrinier E, De Maeyer M, Debyser Z, Van der Eycken E (2013) Identification of small peptides inhibiting the integrase-LEDGF/p75 interaction through targeting the cellular co-factor. J Pept Sci 19(10):651–658

    Article  CAS  Google Scholar 

  12. Cavalluzzo C, Voet A, Christ F, Singh BK, Sharma A, Debyser Z, Maeyer MD, Van der Eycken E (2012) De novo design of small molecule inhibitors targeting the LEDGF/p75-HIV integrase interaction. RSC Adv 2(3):974

    Article  CAS  Google Scholar 

  13. Christ F, Voet A, Marchand A, Nicolet S, Desimmie BA, Marchand D, Bardiot D, Van der Veken NJ, Van Remoortel B, Strelkov SV, De Maeyer M, Chaltin P, Debyser Z (2010) Rational design of small-molecule inhibitors of the LEDGF/p75-integrase interaction and HIV replication. Nat Chem Biol 6(6):442–448

    Article  CAS  Google Scholar 

  14. Kumar A, Voet A, Zhang KY (2012) Fragment based drug design: from experimental to computational approaches. Curr Med Chem 19(30):5128–5147

    Article  CAS  Google Scholar 

  15. Rhodes DI, Peat TS, Vandegraaff N, Jeevarajah D, Le G, Jones ED, Smith JA, Coates JA, Winfield LJ, Thienthong N, Newman J, Lucent D, Ryan JH, Savage GP, Francis CL, Deadman JJ (2011) Structural basis for a new mechanism of inhibition of HIV-1 integrase identified by fragment screening and structure-based design. Antivir Chem Chemother 21(4):155–168

    Article  CAS  Google Scholar 

  16. Peat TS, Dolezal O, Newman J, Mobley D, Deadman JJ (2014) Interrogating HIV integrase for compounds that bind—a SAMPL challenge. J Comput Aided Mol Des (this issue)

  17. Cherepanov P, Ambrosio AL, Rahman S, Ellenberger T, Engelman A (2005) Structural basis for the recognition between HIV-1 integrase and transcriptional coactivator p75. Proc Natl Acad Sci USA 102(48):17308–17313

    Article  CAS  Google Scholar 

  18. Wolber G, Langer T (2005) LigandScout: 3-D pharmacophores derived from protein–bound ligands and their use as virtual screening filters. J Chem Inf Model 45(1):160–169

    Article  CAS  Google Scholar 

  19. OMEGA, version 2.4.6 OpenEye Scientific Software, Inc., Santa Fe, NM, USA. www.eyesopen.com, 2012

  20. Hawkins PCD, Skillman AG, Warren GL, Ellingson BA, Stahl MT (2010) Conformer generation with OMEGA: algorithm and validation using high quality structures from the protein databank and cambridge structural database. J Chem Inf Model 50(4):572–584

    Article  CAS  Google Scholar 

  21. Hawkins PC, Nicholls A (2012) Conformer generation with OMEGA: learning from the data set and the analysis of failures. J Chem Inf Model 52(11):2919–2936

    Article  CAS  Google Scholar 

  22. Jones G, Willett P, Glen RC, Leach AR, Taylor R (1997) Development and validation of a genetic algorithm for flexible docking. J Mol Biol 267(3):727–748

    Article  CAS  Google Scholar 

  23. Gehlhaar DK, Verkhivker GM, Rejto PA, Sherman CJ, Fogel DB, Fogel LJ, Freer ST (1995) Molecular recognition of the inhibitor AG-1343 by HIV-1 protease: conformationally flexible docking by evolutionary programming. Chem Biol 2(5):317–324

    Article  CAS  Google Scholar 

  24. Molecular Operating Environment (MOE), version 2011.10; Chemical Computing Group Inc.: Montreal, Quebec, Canada, 2010

  25. Voet A, Berenger F, Zhang KYJ (2013) Electrostatic similarities between protein and small molecule ligands facilitate the design of protein–protein interaction inhibitors. PLoS ONE 8(10):e75762

    Article  CAS  Google Scholar 

  26. Glide, version 5.7, Schrödinger, LLC, New York, NY, 2011

  27. Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, Repasky MP, Knoll EH, Shelley M, Perry JK, Shaw DE, Francis P, Shenkin PS (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47(7):1739–1749

    Article  CAS  Google Scholar 

  28. Friesner RA, Murphy RB, Repasky MP, Frye LL, Greenwood JR, Halgren TA, Sanschagrin PC, Mainz DT (2006) Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein–ligand complexes. J Med Chem 49(21):6177–6196

    Article  CAS  Google Scholar 

  29. Halgren TA, Murphy RB, Friesner RA, Beard HS, Frye LL, Pollard WT, Banks JL (2004) Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J Med Chem 47(7):1750–1759

    Article  CAS  Google Scholar 

  30. Maestro, version 9.2, Schrödinger, LLC, New York, NY, 2011

  31. LigPrep, version 2.5, Schrödinger, LLC, New York, NY, 2011

  32. Jorgensen WL, Maxwell DS, Tirado-Rives J (1996) Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids. J Am Chem Soc 118(45):11225–11236

    Article  CAS  Google Scholar 

  33. Prime, version 3.0, Schrödinger, LLC, New York, NY, 2011

  34. Bashford D, Case DA (2000) Generalized Born models of macromolecular solvation effects. Annu Rev Phys Chem 51(1):129–152

    Article  CAS  Google Scholar 

  35. Tsui V, Case DA (2000) Theory and applications of the generalized born solvation model in macromolecular simulations. Biopolymers 56(4):275–291

    Article  CAS  Google Scholar 

  36. Velec HFG, Gohlke H, Klebe G (2005) DrugScoreCSD-knowledge-based scoring function derived from small molecule crystal data with superior recognition rate of near-native ligand poses and better affinity prediction. J Med Chem 48(20):6296–6303

    Article  CAS  Google Scholar 

  37. The PyMOL Molecular Graphics System, version 1.5.0.4 Schrödinger, LLC

  38. Williams T, Kelley C (2012) Gnuplot 4.6: an interactive plotting program

  39. Fry DC (2012) Small-molecule inhibitors of protein–protein interactions: how to mimic a protein partner. Curr Pharm Des 18(30):4679–4684

    Article  CAS  Google Scholar 

  40. Voet A, Banwell EF, Sahu KK, Heddle JG, Zhang KY (2013) Protein interface pharmacophore mapping tools for small molecule protein: protein interaction inhibitor discovery. Curr Top Med Chem 13(9):989–1001

    Article  CAS  Google Scholar 

  41. Voet A, Zhang KY (2012) Pharmacophore modelling as a virtual screening tool for the discovery of small molecule protein–protein interaction inhibitors. Curr Pharm Des 18(30):4586–4598

    Article  CAS  Google Scholar 

  42. Mobley D, Liu S, Lim N, Deng N, Branson K, Perryman A, Forli S, Levy R, Gallicchio E, Olson A (2014) Blind prediction of HIV integrase binding from the SAMPL4 challenge. J Comput Aided Mol Des (this issue)

  43. Voet A, Callewaert L, Ulens T, Vanderkelen L, Vanherreweghe JM, Michiels CW, De Maeyer M (2011) Structure based discovery of small molecule suppressors targeting bacterial lysozyme inhibitors. Biochem Biophys Res Commun 405(4):527–532

    Article  CAS  Google Scholar 

  44. Voet A, Helsen C, Zhang KYJ, Claessens F (2013) The discovery of novel human androgen receptor antagonist chemotypes using a combined pharmacophore screening procedure. Chem Med Chem 8(4):644–651

    CAS  Google Scholar 

  45. Perryman AL, Santiago DN, Forli S, Olson AJ (2013) Virtual Screening with AutoDock Vina and the Common Pharmacophore Engine of a low diversity library of fragments and hits against the three allosteric sites of HIV integrase: participation in the SAMPL4 protein–ligand binding challenge. J Comput Aided Mol Des. doi:10.1007/s1082201497093

  46. Coleman RG, Sterling T, Weiss DR (2013) SAMPL4 & DOCK3.7: Lessons for automated docking procedures. J Comput Aided Mol Des (in press)

Download references

Acknowledgments

We thank RIKEN Integrated Cluster of Clusters (RICC) at RIKEN for the supercomputing resources used for this study. We are grateful to Dr. David Mobley and SAMPL4 participants for sharing the SAMPL4 challenge analyses and prediction data prior to publication. We acknowledge the Initiative Research Unit program from RIKEN, Japan for funding. AV acknowledges the JSPS for a postdoctoral fellowship. Prof. Jeremy Tame is acknowledged for proofreading the manuscript.

Author information

Authors and Affiliations

  1. Zhang Initiative Research Unit, Institute Laboratories, RIKEN, 2-1 Hirosawa, Wakō, Saitama, 351-0198, Japan

    Arnout R. D. Voet, Ashutosh Kumar, Francois Berenger & Kam Y. J. Zhang

Authors
  1. Arnout R. D. Voet
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ashutosh Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Francois Berenger
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kam Y. J. Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kam Y. J. Zhang.

Additional information

Arnout R. D. Voet and Ashutosh Kumar have contributed equally to this work.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Voet, A.R.D., Kumar, A., Berenger, F. et al. Combining in silico and in cerebro approaches for virtual screening and pose prediction in SAMPL4. J Comput Aided Mol Des 28, 363–373 (2014). https://doi.org/10.1007/s10822-013-9702-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10822-013-9702-2

Keywords

Search

Navigation