Journal of Computer-Aided Molecular Design

, Volume 28, Issue 4, pp 327–345

Blind prediction of HIV integrase binding from the SAMPL4 challenge

  • David L. Mobley
  • Shuai Liu
  • Nathan M. Lim
  • Karisa L. Wymer
  • Alexander L. Perryman
  • Stefano Forli
  • Nanjie Deng
  • Justin Su
  • Kim Branson
  • Arthur J. Olson
Article

Abstract

Here, we give an overview of the protein-ligand binding portion of the Statistical Assessment of Modeling of Proteins and Ligands 4 (SAMPL4) challenge, which focused on predicting binding of HIV integrase inhibitors in the catalytic core domain. The challenge encompassed three components—a small “virtual screening” challenge, a binding mode prediction component, and a small affinity prediction component. Here, we give summary results and statistics concerning the performance of all submissions at each of these challenges. Virtual screening was particularly challenging here in part because, in contrast to more typical virtual screening test sets, the inactive compounds were tested because they were thought to be likely binders, so only the very top predictions performed significantly better than random. Pose prediction was also quite challenging, in part because inhibitors in the set bind to three different sites, so even identifying the correct binding site was challenging. Still, the best methods managed low root mean squared deviation predictions in many cases. Here, we give an overview of results, highlight some features of methods which worked particularly well, and refer the interested reader to papers in this issue which describe specific submissions for additional details.

Keywords

HIV integrase Binding mode Virtual screening Pose prediction Affinity SAMPL4 

Supplementary material

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GZ (47,988 KB)

References

  1. 1.
    Abram ME, Hluhanich RM, Goodman DD, Andreatta KN, Margot NA, Ye L, Niedziela-Majka A, Barnes TL, Novikov N, Chen X, Svarovskaia ES, McColl DJ, White KL, Miller MD (2013) Impact of primary Elvitegravir resistance-associated mutations in HIV-1 integrase on drug susceptibility and viral replication fitness. Antimicrob Agents Chemother 57(6):2654–2663CrossRefGoogle Scholar
  2. 2.
    Benson ML, Faver JC, Ucisik MN, Dashti DS, Zheng Z, Merz KM Jr (2012) Prediction of trypsin/molecular fragment binding affinities by free energy decomposition and empirical scores. J Comput Aided Mol Des 26(5):647–659CrossRefGoogle Scholar
  3. 3.
    Blow DM (2002) Rearrangement of Cruickshank’s formulae for the diffraction-component precision index. Acta Crystallogr Sect D Biol Crystallogr 58(5):792–797CrossRefGoogle Scholar
  4. 4.
    Cherepanov P, Ambrosio ALB, 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):17,308–17,313CrossRefGoogle Scholar
  5. 5.
    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):150–160CrossRefGoogle Scholar
  6. 6.
    Christ F, Shaw S, Demeulemeester J, Desimmie BA, Marchand A, Butler S, Smets W, Chaltin P, Westby M, Debyser Z, Pickford C (2012) Small-molecule inhibitors of the LEDGF/p75 binding site of integrase block HIV replication and modulate integrase multimerization. Antimicrob Agents Chemother 56(8):4365–4374CrossRefGoogle Scholar
  7. 7.
    Christ F, Voet A, Marchand A, Nicolet S, Desimmie BA, Marchand D, Bardiot D, Vander 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 Meth 6(6):442–448CrossRefGoogle Scholar
  8. 8.
    Coleman RG, Sterling T, Weiss DR (2014) SAMPL4 & DOCK3.7: lessons for automated docking procedures. J Comput Aided Mol Des. doi:10.1007/s10822-014-9722-6
  9. 9.
    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):1622–1637CrossRefGoogle Scholar
  10. 10.
    De Clercq E (1999) Perspectives of non-nucleoside reverse transcriptase inhibitors (NNRTIs) in the therapy of HIV-1 infection. Il Farmaco 54(1–2):26–45CrossRefGoogle Scholar
  11. 11.
    Dewdney TG, Wang Y, Kovari IA, Reiter SJ, Kovari LC (2013) Reduced HIV-1 integrase flexibility as a mechanism for raltegravir resistance. J Struct Biol 184:245–250CrossRefGoogle Scholar
  12. 12.
    Engelman A, Cherepanov P (2012) The structural biology of HIV-1: mechanistic and therapeutic insights. Nat Rev Microbiol 10(4):279–290CrossRefGoogle Scholar
  13. 13.
    Ewing TJ, Makino S, Skillman AG, Kuntz ID (2001) DOCK 4.0: search strategies for automated molecular docking of flexible molecule databases. J Comput Aided Mol Des 15(5):411–428CrossRefGoogle Scholar
  14. 14.
    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 proteinligand complexes. J Med Chem 49(21):6177–6196CrossRefGoogle Scholar
  15. 15.
    Gallicchio E, Deng N, He P, Perryman AL, Santiago DN, Forli S, Olson AJ, Levy RM (2014) Virtual screening of integrase inhibitors by large scale binding free energy calculations. J Comput Aided Mol Des. doi:10.1007/s10822-014-9711-9
  16. 16.
    Gallicchio E, Lapelosa M, Levy RM (2010) The binding energy distribution analysis method (BEDAM) for the estimation of protein-ligand binding affinities. J Chem Theory Comput 6(9):2961–2977CrossRefGoogle Scholar
  17. 17.
    Geretti AM, Armenia D, Ceccherini-Silberstein F (2012) Emerging patterns and implications of HIV-1 integrase inhibitor resistance. Curr Opin Infect Dis 25(6):677–686. doi:10.1097/QCO.0b013e32835a1de7 CrossRefGoogle Scholar
  18. 18.
    Greenwald J, Le V, Butler SL, Bushman FD, Choe S (1999) The mobility of an HIV-1 integrase active site loop is correlated with catalytic activity. Biochemistry 38(28):8892–8898CrossRefGoogle Scholar
  19. 19.
    Hare S, Maertens GN, Cherepanov P (2012) 3[prime]-Processing and strand transfer catalysed by retroviral integrase in crystallo. EMBO J 31(13):3020–3028CrossRefGoogle Scholar
  20. 20.
    Hare S, Smith SJ, Métifiot M, Jaxa-Chamiec A, Pommier Y, Hughes SH, Cherepanov P (2011) Structural and functional analyses of the second-generation integrase strand transfer inhibitor dolutegravir (S/GSK1349572). Mol Pharmacol 80(4):565–572CrossRefGoogle Scholar
  21. 21.
    Hare S, Vos AM, Clayton RF, Thuring JW, Cummings MD, Cherepanov P (2010) Molecular mechanisms of retroviral integrase inhibition and the evolution of viral resistance. Proc Natl Acad Sci 107(46):20,057–20,062CrossRefGoogle Scholar
  22. 22.
    Hawkins PCD, Nicholls A (2012) Conformer generation with OMEGA: learning from the data set and the analysis of failures. J Chem Inf Model 52(11):2919–2936CrossRefGoogle Scholar
  23. 23.
    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):572–584CrossRefGoogle Scholar
  24. 24.
    Hogues H, Sulea T, Purisima EO (2014) Exuastive docking and solvated interaction energy scoring: lessons learned from the SAMPL4 challenge. J Comput Aided Mol Des. doi:10.1007/s10822-014-9715-5
  25. 25.
    Japrung D, Leartsakulpanich U, Chusacultanachai S, Yuthavong Y (2007) Conflicting requirements of Plasmodium falciparum dihydrofolate reductase mutations conferring resistance to pyrimethamine-WR99210 combination. Antimicrob Agents Chemother 51(12):4356–4360CrossRefGoogle Scholar
  26. 26.
    Jurado KA, Wang H, Slaughter A, Feng L, Kessl JJ, Koh Y, Wang W, Ballandras-Colas A, Patel PA, Fuchs JR, Kvaratskhelia M, Engelman A (2013) Allosteric integrase inhibitor potency is determined through the inhibition of HIV-1 particle maturation. Proc Natl Acad Sci 110(21):8690–8695CrossRefGoogle Scholar
  27. 27.
    Kessl JJ, Jena N, Koh Y, Taskent-Sezgin H, Slaughter A, Feng L, de Silva S, Wu L, Le Grice SFJ, Engelman A, Fuchs JR, Kvaratskhelia M (2012) Multimode, cooperative mechanism of action of allosteric HIV-1 integrase inhibitors. J Biol Chem 287(20):16,801–16,811CrossRefGoogle Scholar
  28. 28.
    Krishnan L, Engelman A (2012) Retroviral integrase proteins and HIV-1 DNA integration. J Biol Chem 287(49):40,858–40,866CrossRefGoogle Scholar
  29. 29.
    Kuhn B, Kollman PA (2000) Binding of a diverse set of ligands to avidin and streptavidin: an accurate quantitative prediction of their relative affinities by a combination of molecular mechanics and continuum solvent models. J Med Chem 43(20):3786–3791CrossRefGoogle Scholar
  30. 30.
    Kulp JL, Blumenthal SN, Wang Q, Bryan RL, Guarnieri F (2012) A fragment-based approach to the SAMPL3 challenge. J Comput Aided Mol Des 26(5):583–594CrossRefGoogle Scholar
  31. 31.
    Kumar A, Zhang KYJ (2012) Computational fragment-based screening using RosettaLigand: the SAMPL3 challenge. J Comput Aided Mol Des 26(5):603–616CrossRefGoogle Scholar
  32. 32.
    Kuntz ID, Chen K, Sharp KA, Kollman PA (1999) The maximal affinity of ligands. Proc Natl Acad Sci 96(18):9997–10,002CrossRefGoogle Scholar
  33. 33.
    Lindorff-Larsen K, Piana S, Palmo K, Maragakis P, Klepeis JL, Dror RO, Shaw DE (2010) Improved side-chain torsion potentials for the Amber ff99SB protein force field. Proteins pp NA–NA 78(8):1950–1958Google Scholar
  34. 34.
    Maertens GN, Hare S, Cherepanov P (2010) The mechanism of retroviral integration from X-ray structures of its key intermediates. Nature 468(7321):326–329CrossRefGoogle Scholar
  35. 35.
    MarvinSketch version 5.8.2 (2013) ChemAxon. http://www.chemaxon.com/products/marvin/marvinsketch/
  36. 36.
    Métifiot M, Maddali K, Johnson BC, Hare S, Smith SJ, Zhao XZ, Marchand C, Burke TR, Hughes SH, Cherepanov P, Pommier Y (2013) Activities, crystal structures, and molecular dynamics of dihydro-1H-isoindole derivatives, inhibitors of HIV-1 integrase. ACS Chem Biol 8(1):209–217CrossRefGoogle Scholar
  37. 37.
    Mobley DL, Wymer KL, Lim NM (2014) Blind prediction of solvation free energies from the SAMPL4 challenge. J Comput Aided Mol DesGoogle Scholar
  38. 38.
    Naïm M, Bhat S, Rankin KN, Dennis S, Chowdhury SF, Siddiqi I, Drabik P, Sulea T, Bayly CI, Jakalian A (2007) Solvated interaction energy (SIE) for scoring protein-ligand binding affinities. 1. Exploring the parameter space. J Chem Inf Model 47(1):122–133CrossRefGoogle Scholar
  39. 39.
    Newman J, Dolezal O, Fazio V, Caradoc-Davies T, Peat TS (2012) The DINGO dataset: a comprehensive set of data for the SAMPL challenge. J Comput Aided Mol Des 26(5):497–503CrossRefGoogle Scholar
  40. 40.
    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):769–779CrossRefGoogle Scholar
  41. 41.
    OpenEye Python Toolkits. http://www.eyesopen.com (2013)
  42. 42.
    Peat TS, Dolezal O, Newman J, Mobley DL, Deadman JJ (2014) Interrogating HIV integrase for compounds that bind—a SAMPL4 challenge. J Comput Aided Mol Des. doi:10.1007/s10822-014-9721-7
  43. 43.
    Peat TS, Warren G (2013) Personal Communication. E-mail exchangeGoogle Scholar
  44. 44.
    Perryman AL, Forli S, Morris GM, Burt C, Cheng Y, Palmer MJ, Whitby K, McCammon JA, Phillips C, Olson AJ (2010) A dynamic model of HIV integrase inhibition and drug resistance. J Mol Biol 397(2):600–615CrossRefGoogle Scholar
  45. 45.
    Quashie PK, Mesplède T, Han YS, Veres T, Osman N, Hassounah S, Sloan R, Xu HT, Wainberg MA (2013) Biochemical analysis of the role of G118R-linked dolutegravir drug resistance substitutions in HIV-1 integrase. Antimicrob Agents Chemother 57(12):6223–6235Google Scholar
  46. 46.
    Quashie PK, Mesplède T, Wainberg MA (2013) Evolution of HIV integrase resistance mutations. Curr Opin Infect Dis 26(1):43–49. doi:10.1097/QCO.0b013e32835ba81c Google Scholar
  47. 47.
    Skillman AG, Warren GL, Nicholls A (2008) SAMPL at first glance: So much data, so little time…. http://www.eyesopen.com/2008_cup_presentations/CUP9_Skillman.pdf
  48. 48.
    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(9):2066–2081CrossRefGoogle Scholar
  49. 49.
    Sulea T, Hogues H, Purisima EO (2012) Exhaustive search and solvated interaction energy (SIE) for virtual screening and affinity prediction. J Comput Aided Mol Des 26(5):617–633CrossRefGoogle Scholar
  50. 50.
    Surpateanu G, Iorga BI (2012) Evaluation of docking performance in a blinded virtual screening of fragment-like trypsin inhibitors. J Comput Aided Mol Des 26(5):595–601CrossRefGoogle Scholar
  51. 51.
    Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31(2):455–461Google Scholar
  52. 52.
    Truchon J, Bayly CI (2007) Evaluating virtual screening methods: good and bad metrics for the “early recognition” problem. J Chem Inf Model 47(2):488–508CrossRefGoogle Scholar
  53. 53.
    Tsiang M, Jones GS, Niedziela-Majka A, Kan E, Lansdon EB, Huang W, Hung M, Samuel D, Novikov N, Xu Y, Mitchell M, Guo H, Babaoglu K, Liu X, Geleziunas R, Sakowicz R (2012) New class of HIV-1 integrase (IN) inhibitors with a dual mode of action. J Biol Chem 287(25):21,189–21,203CrossRefGoogle Scholar
  54. 54.
    Voet ARD, Kumar A, Berenger F, Zhang KYJ (2014) Combining in cerebra and in silico approaches for virtual screening and pose prediction in SAMPL4. J Comput Aided Mol Des. doi:10.1007/s10822-013-9702-2
  55. 55.
    Wainberg MA, Mesplède T, Quashie PK (2012) The development of novel HIV integrase inhibitors and the problem of drug resistance. Curr Opin Virol 2(5):656–662CrossRefGoogle Scholar
  56. 56.
    Wang J, Wolf R, Caldwell J, Kollman P, Case D (2004) Development and testing of a general amber force field. J Comput Chem 25(9):1157–1174CrossRefGoogle Scholar
  57. 57.
    Wang R, Liu L, Lai L, Tang Y (1998) SCORE: a new empirical method for estimating the binding affinity of a protein-ligand complex. J Mol Model 4(12):379–394CrossRefGoogle Scholar
  58. 58.
    Zhang J, Adrian FJ, Jahnke W, Cowan-Jacob SW, Li AG, Iacob RE, Sim T, Powers J, Dierks C, Sun F, Guo GR, Ding Q, Okram B, Choi Y, Wojciechowski A, Deng X, Liu G, Fendrich G, Strauss A, Vajpai N, Grzesiek S, Tuntland T, Liu Y, Bursulaya B, Azam M, Manley PW, Engen JR, Daley GQ, Warmuth M, Gray NS (2010) Targeting Bcr-Abl by combining allosteric with ATP-binding-site inhibitors. Nature 463(7280):501–506CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • David L. Mobley
    • 1
    • 2
  • Shuai Liu
    • 1
  • Nathan M. Lim
    • 1
  • Karisa L. Wymer
    • 1
  • Alexander L. Perryman
    • 3
    • 6
  • Stefano Forli
    • 3
  • Nanjie Deng
    • 4
  • Justin Su
    • 1
  • Kim Branson
    • 5
  • Arthur J. Olson
    • 3
  1. 1.Department of Pharmaceutical Sciences and Department of ChemistryUniversity of California, IrvineIrvineUSA
  2. 2.Department of ChemistryUniversity of New OrleansNew OrleansUSA
  3. 3.Department of Integrative Structural and Computational BiologyThe Scripps Research InstituteLa JollaUSA
  4. 4.Department of Chemistry and Chemical Biology RutgersThe State University of New JerseyPiscatawayUSA
  5. 5.Hessian InformaticsEmerald HillsUSA
  6. 6.Department of Medicine, Division of Infectious DiseasesRutgers University-NJ Medical SchoolNewarkUSA

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