Skip to main content

Advertisement

SpringerLink
Log in

Exhaustive search and solvated interaction energy (SIE) for virtual screening and affinity prediction

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

Abstract

We carried out a prospective evaluation of the utility of the SIE (solvation interaction energy) scoring function for virtual screening and binding affinity prediction. Since experimental structures of the complexes were not provided, this was an exercise in virtual docking as well. We used our exhaustive docking program, Wilma, to provide high-quality poses that were rescored using SIE to provide binding affinity predictions. We also tested the combination of SIE with our latest solvation model, first shell of hydration (FiSH), which captures some of the discrete properties of water within a continuum model. We achieved good enrichment in virtual screening of fragments against trypsin, with an area under the curve of about 0.7 for the receiver operating characteristic curve. Moreover, the early enrichment performance was quite good with 50% of true actives recovered with a 15% false positive rate in a prospective calculation and with a 3% false positive rate in a retrospective application of SIE with FiSH. Binding affinity predictions for both trypsin and host–guest complexes were generally within 2 kcal/mol of the experimental values. However, the rank ordering of affinities differing by 2 kcal/mol or less was not well predicted. On the other hand, it was encouraging that the incorporation of a more sophisticated solvation model into SIE resulted in better discrimination of true binders from binders. This suggests that the inclusion of proper Physics in our models is a fruitful strategy for improving the reliability of our binding affinity predictions.

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
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Reddy MR, Erion MD (2001) Free energy calculations in rational drug design. Springer, Berlin

    Google Scholar 

  2. Chodera JD, Mobley DL, Shirts MR, Dixon RW, Branson K, Pande VS (2011) Alchemical free energy methods for drug discovery: progress and challenges. Curr Opin Struct Biol 21(2):150–160

    Article  CAS  Google Scholar 

  3. Gohlke H, Klebe G (2002) Approaches to the description and prediction of the binding affinity of small-molecule ligands to macromolecular receptors. Angew Chem Int Ed 41:2644–2676

    Article  CAS  Google Scholar 

  4. Gilson MK, Zhou HX (2007) Calculation of protein–ligand binding affinities. Annu Rev Biophys Biomol Struct 36(1):21–42

    Article  CAS  Google Scholar 

  5. Ferrara P, Gohlke H, Price DJ, Klebe G, Brooks CL III (2004) Assessing scoring functions for protein–ligand interactions. J Med Chem 47:3032–3047

    Article  CAS  Google Scholar 

  6. Wang R, Lu Y, Fang X, Wang S (2004) An extensive test of 14 scoring functions using the PDB bind refined set of 800 protein–ligand complexes. J Chem Inf Comput Sci 44:2114–2125

    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, Tedesco G, Wall ID, Woolven JM, Peishoff CE, Head MS (2006) A critical assessment of docking programs and scoring functions. J Med Chem 49(20):5912–5931

    Article  CAS  Google Scholar 

  8. Moitessier N, Englebienne P, Lee D, Lawandi J, Corbeil CR (2008) Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go. Br J Pharmacol 153(S1):S7–S26

    Article  CAS  Google Scholar 

  9. Englebienne P, Moitessier N (2009) Docking ligands into flexible and solvated macromolecules. 4. Are popular scoring functions accurate for this class of proteins? J Chem Inf Model 49(6):1568–1580

    Article  CAS  Google Scholar 

  10. Zou X, Sun Y, Kuntz ID (1999) Inclusion of solvation in ligand binding free energy calculations using the generalized-born model. J Am Chem Soc 121:8033–8043

    Article  CAS  Google Scholar 

  11. Kollman PA, Massova I, Reyes C, Kuhn B, Huo S, Chong L, Lee M, Lee T, Duan Y, Wang W, Donini O, Cieplak P, Srinivasan J, Case DA, Cheatham TE (2000) Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. Acc Chem Res 33(12):889–897

    Article  CAS  Google Scholar 

  12. Kuhn B, Gerber P, Schulz-Gasch T, Stahl M (2005) Validation and use of the MM-PBSA approach for drug discovery. J Med Chem 48(12):4040–4048

    Article  CAS  Google Scholar 

  13. Gohlke H, Case DA (2004) Converging free energy estimates: MM-PB(GB)SA studies on the protein–protein complex Ras-Raf. J Comput Chem 25(2):238–250

    Article  CAS  Google Scholar 

  14. Brown SP, Muchmore SW (2009) Large-scale application of high-throughput molecular mechanics with Poisson–Boltzmann surface area for routine physics-based scoring of protein–ligand complexes. J Med Chem 52(10):3159–3165

    Article  CAS  Google Scholar 

  15. Naïm M, Bhat S, Rankin KN, Dennis S, Chowdhury SF, Siddiqi I, Drabik P, Sulea T, Bayly C, Jakalian A, Purisima EO (2007) Solvated interaction energy (SIE) for scoring protein–ligand binding affinities. 1. Exploring the parameter space. J Chem Inf Model 47(1):122–133

    Article  Google Scholar 

  16. Cui Q, Sulea T, Schrag JD, Munger C, Hung M-N, Naïm M, Cygler M, Purisima EO (2008) Molecular dynamics—solvated interaction energy studies of protein–protein interactions: the MP1-p14 scaffolding complex. J Mol Biol 379(4):787–802

    Article  CAS  Google Scholar 

  17. Sulea T, Purisima EO (2011) The solvated interaction energy (SIE) method for scoring binding affinities. In: Baron R (ed) Methods in molecular biology, computer-aided drug design. Humana Press (Springer Publishing Group) (in press)

  18. Wang YT, Su ZY, Hsieh CH, Chen CL (2009) Predictions of binding for dopamine D2 receptor antagonists by the SIE method. J Chem Inf Model 49(10):2369–2375

    Article  CAS  Google Scholar 

  19. Mishra NK, Kríz Z, Wimmerová M, Koca J (2010) Recognition of selected monosaccharides by Pseudomonas aeruginosa lectin II analyzed by molecular dynamics and free energy calculations. Carbohydr Res 345(10):1432–1441

    Article  CAS  Google Scholar 

  20. Rodriguez-Granillo A, Sedlak E, Wittung-Stafshede P (2008) Stability and ATP binding of the nucleotide-binding domain of the Wilson disease protein: effect of the common H1069Q mutation. J Mol Biol 383(5):1097–1111

    Article  CAS  Google Scholar 

  21. Wei C, Mei Y, Zhang D (2010) Theoretical study on the HIV-1 integrase-5CITEP complex based on polarized force fields. Chem Phys Lett 495(1–3):121–124

    Article  CAS  Google Scholar 

  22. Lecaille F, Chowdhury S, Purisima E, Brîmme D, Lalmanach G (2007) The S2 subsites of cathepsins K and L and their contribution to collagen degradation. Protein Sci 16(4):662–670

    Article  CAS  Google Scholar 

  23. Nguyen M, Marcellus RC, Roulston A, Watson M, Serfass L, Murthy Madiraju SR, Goulet D, Viallet J, Belec L, Billot X, Acoca S, Purisima E, Wiegmans A, Cluse L, Johnstone RW, Beauparlant P, Shore GC (2007) Small molecule obatoclax (GX15-070) antagonizes MCL-1 and overcomes MCL-1-mediated resistance to apoptosis. Proc Natl Acad Sci USA 104(49):19512–19517

    Article  CAS  Google Scholar 

  24. Okamoto M, Takayama K, Shimizu T, Muroya A, Furuya T (2010) Structure–activity relationship of novel DAPK inhibitors identified by structure-based virtual screening. Bioorg Med Chem 18(7):2728–2734

    Article  CAS  Google Scholar 

  25. Yang B, Hamza A, Chen G, Wang Y, Zhan C-G (2010) Computational determination of binding structures and free energies of phosphodiesterase-2 with benzo[1,4]diazepin-2-one derivatives. J Phys Chem B 114(48):16020–16028

    Article  CAS  Google Scholar 

  26. Wimmerová M, Mishra N, Pokorn M, Koca J (2009) Importance of oligomerisation on Pseudomonas aeruginosa lectin-II binding affinity. In silico and in vitro mutagenesis. J Mol Model 15(6):673–679

    Article  Google Scholar 

  27. Hamza A, Zhao X, Tong M, Tai H-H, Zhan C-G (2011) Novel human mPGES-1 inhibitors identified through structure-based virtual screening. Bioorg Med Chem 19(20):6077–6086

    Article  CAS  Google Scholar 

  28. Coluccia A, Sabbadin D, Brancale A (2011) Molecular modelling studies on arylthioindoles as potent inhibitors of tubulin polymerization. Eur J Med Chem 46(8):3519–3525

    Article  CAS  Google Scholar 

  29. Anisimov VM, Cavasotto CN (2011) Quantum mechanical binding free energy calculation for phosphopeptide inhibitors of the Lck SH2 domain. J Comput Chem 32(10):2254–2263

    Article  CAS  Google Scholar 

  30. Hartono YD, Lee AN, Lee-Huang S, Zhang D (2011) Computational study of bindings of HL9, a nonapeptide fragment of human lysozyme, to HIV-1 fusion protein gp41. Bioorg Med Chem Lett 21(6):1607–1611

    Article  CAS  Google Scholar 

  31. Duque MD, Ma C, Torres E, Wang J, Naesens L, Juarez-Jimenez J, Camps P, Luque FJ, DeGrado WF, Lamb RA, Pinto LH, Vasquez S (2011) Exploring the size limit of templates for inhibitors of the M2 ion channel of influenza A virus. J Med Chem 54(8):2646–2657

    Article  CAS  Google Scholar 

  32. Dunbar JB, Smith RD, Yang C-Y, Ung PM-U, Lexa KW, Khazanov NA, Stuckey JA, Wang S, Carlson HA (2011) CSAR benchmark exercise of 2010: selection of the protein–ligand complexes. J Chem Inf Model 51(9):2036–2046

    Article  CAS  Google Scholar 

  33. Smith RD, Dunbar JB, Ung PM-U, Esposito EX, Yang C-Y, Wang S, Carlson HA (2011) CSAR benchmark exercise of 2010: combined evaluation across all submitted scoring functions. J Chem Inf Model 51(9):2115–2131

    Article  CAS  Google Scholar 

  34. Sulea T, Cui Q, Purisima EO (2011) Solvated interaction energy (SIE) for scoring protein–ligand binding affinities. 2. Benchmark in the CSAR-2010 scoring exercise. J Chem Inf Model 51:2066–2081

    Article  CAS  Google Scholar 

  35. Skillman G (2008) SAMPL1 at first glance. CUP IX meeting, Santa Fe, NM, 19 March 2008. http://eyesopen.com/2008_cup_presentations/CUP9_Skillman.pdf. Accessed 1 Oct 2011

  36. Hajduk PJ, Greer J (2007) A decade of fragment-based drug design: strategic advances and lessons learned. Nat Rev Drug Discov 6(3):211–219

    Article  CAS  Google Scholar 

  37. Moghaddam S, Yang C, Rekharsky M, Ko YH, Kim K, Inoue Y, Gilson MK (2011) New ultrahigh affinity host–guest complexes of cucurbit[7]uril with bicyclo[2.2.2]octane and adamantane guests: thermodynamic analysis and evaluation of M2 affinity calculations. J Am Chem Soc 133(10):3570–3581

    Article  CAS  Google Scholar 

  38. Moghaddam S, Inoue Y, Gilson MK (2009) Host–guest complexes with protein–ligand-like affinities: computational analysis and design. J Am Chem Soc 131(11):4012–4021

    Article  CAS  Google Scholar 

  39. McInnes C (2007) Virtual screening strategies in drug discovery. Curr Opin Chem Biol 11(5):494–502

    Article  CAS  Google Scholar 

  40. Fennell CJ, Kehoe CW, Dill KA (2011) Modeling aqueous solvation with semi-explicit assembly. Proc Natl Acad Sci USA 108(8):3234–3239

    Article  CAS  Google Scholar 

  41. Fennell CJ, Kehoe C, Dill KA (2009) Oil/water transfer is partly driven by molecular shape, not just size. J Am Chem Soc 132(1):234–240

    Article  Google Scholar 

  42. Corbeil CR, Sulea T, Purisima EO (2010) Rapid prediction of solvation free energy. 2. The first-shell hydration (FiSH) continuum model. J Chem Theory Comput 6(5):1622–1637

    Article  CAS  Google Scholar 

  43. Purisima EO, Corbeil CR, Sulea T (2010) Rapid prediction of solvation free energy. 3. Application to the SAMPL2 challenge. J Comput Aided Mol Des 24:373–383

    Article  CAS  Google Scholar 

  44. 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 

  45. Cornell WD, Cieplak P, Bayly CI, Gould IR, Merz KM Jr, Ferguson DM, Spellmeyer DC, Fox T, Caldwell JW, Kollman PA (1995) A second generation force field for the simulation of proteins, nucleic acids, and organic molecules. J Am Chem Soc 117:5179–5197

    Article  CAS  Google Scholar 

  46. Case DA, Cheatham TE III, Darden T, Gohlke H, Luo R, Merz KM Jr, Onufriev A, Simmerling C, Wang B, Woods RJ (2005) The amber biomolecular simulation programs. J Comput Chem 26(16):1668–1688

    Article  CAS  Google Scholar 

  47. Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA (2004) Development and testing of a general amber force field. J Comput Chem 25(9):1157–1174

    Article  CAS  Google Scholar 

  48. Bayly CI, Cieplak P, Cornell WD, Kollman PA (1993) A well-behaved electrostatic potential based method using charge restraints for deriving atomic charges: the RESP model. J Phys Chem 97:10269–10280

    Article  CAS  Google Scholar 

  49. Cornell WD, Cieplak P, Bayly CI, Kollman PA (1993) Application of RESP charges to calculate conformational energies, hydrogen bond energies, and free energies of solvation. J Am Chem Soc 115:9620–9631

    Article  CAS  Google Scholar 

  50. Jakalian A, Jack DB, Bayly CI (2002) Fast, efficient generation of high-quality atomic charges. AM1-BCC model: II. Parameterization and validation. J Comput Chem 23:1623–1641

    Article  CAS  Google Scholar 

  51. Jakalian A, Bush BL, Jack DB, Bayly CI (2000) Fast, efficient generation of high-quality atomic charges. AM1-BCC model: I. Method. J Comput Chem 21(2):132–146

    Article  CAS  Google Scholar 

  52. Purisima EO (1998) Fast summation boundary element method for calculating solvation free energies of macromolecules. J Comput Chem 19(13):1494–1504

    Article  CAS  Google Scholar 

  53. Purisima EO, Nilar SH (1995) A simple yet accurate boundary element method for continuum dielectric calculations. J Comput Chem 16:681–689

    Article  CAS  Google Scholar 

  54. Chan SL, Purisima EO (1998) A new tetrahedral tessellation scheme for isosurface generation. Comput Graph 22(1):83–90

    Article  Google Scholar 

  55. Chan SL, Purisima EO (1998) Molecular surface generation using marching tetrahedra. J Comput Chem 19(11):1268–1277

    Article  CAS  Google Scholar 

  56. Bhat S, Purisima EO (2006) Molecular surface generation using a variable-radius solvent probe. Proteins Struct Funct Bioinf 62(1):244–261

    Article  CAS  Google Scholar 

  57. Chang CE, Gilson MK (2004) Free energy, entropy, and induced fit in host-guest recognition: calculations with the second-generation mining minima algorithm. J Am Chem Soc 126(40):13156–13164

    Article  CAS  Google Scholar 

  58. Chen W, Chang CE, Gilson MK (2004) Calculation of cyclodextrin binding affinities: energy, entropy, and implications for drug design. Biophys J 87(5):3035–3049

    Article  CAS  Google Scholar 

  59. Ma D, Zavalij PY, Isaacs L (2010) Acyclic cucurbit[n]uril congeners are high affinity hosts. J Org Chem 75(14):4786–4795

    Article  CAS  Google Scholar 

  60. Mobley DL, Barber AE, Fennell CJ, Dill KA (2008) Charge asymmetries in hydration of polar solutes. J Phys Chem B 112(8):2405–2414

    Article  CAS  Google Scholar 

  61. Purisima EO, Sulea T (2009) Restoring charge asymmetry in continuum electrostatics calculations of hydration free energies. J Phys Chem B 113(24):8206–8209

    Article  CAS  Google Scholar 

  62. Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML (1983) Comparison of simple potential functions for simulating liquid water. J Chem Phys 79:926–935

    Article  CAS  Google Scholar 

  63. Floris F, Tomasi J (1989) Evaluation of the dispersion contribution to the solvation energy. A simple computational model in the continuum approximation. J Comput Chem 10(5):616–627

    Article  CAS  Google Scholar 

  64. Lill MA, Thompson JJ (2011) Solvent interaction energy calculations on molecular dynamics trajectories: increasing the efficiency using systematic frame selection. J Chem Inf Model 51(10):2680–2689

    Article  CAS  Google Scholar 

Download references

Acknowledgments

This is National Research Council of Canada publication number 53158.

Author information

Authors and Affiliations

  1. Biotechnology Research Institute, National Research Council of Canada, 6100 Roylamount Avenue, Montreal, QC, H4P 2R2, Canada

    Traian Sulea, Hervé Hogues & Enrico O. Purisima

Authors
  1. Traian Sulea
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hervé Hogues
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Enrico O. Purisima
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Enrico O. Purisima.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOC 1,216 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sulea, T., Hogues, H. & Purisima, E.O. Exhaustive search and solvated interaction energy (SIE) for virtual screening and affinity prediction. J Comput Aided Mol Des 26, 617–633 (2012). https://doi.org/10.1007/s10822-011-9529-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10822-011-9529-7

Keywords

Search

Navigation