Guide to Virtual Screening: Application to the Akt Phosphatase PHLPP

Part of the Methods in Molecular Biology book series (MIMB, volume 819)


We present an example-based description of virtual screening (VS) techniques used to identify new regulators of the Akt phosphatase PHLPP (PH domain Leucine repeat Protein Phosphatase). This enzyme opposes the effects of two kinases, Akt and PKC, which play a major role in cell growth and survival. Therefore, PHLPP is a potential therapeutic target in pathophysiologies where these pathways are either repressed, such as in diabetes and cardiovascular diseases, or over-activated as in cancer. To the best of our knowledge, no PHLPP inhibitors have been reported so far in the literature. In this study, we used a combination of chemical and virtual screening techniques that led to the identification of a number of inhibiting compounds with diverse scaffolds. These compounds bind PHLPP and inhibit cell death when tested in cellular assays. We employed GLIDE docking software to screen a library of more than 40,000 compounds selected from the NCI open depository (250,000 compounds) by similarity searches. We compare the efficiency at which we determined binding compounds from the chemical screen, and compare enrichment factors of the virtually discovered compounds over chemical screening.

Key words

Docking Virtual screening PHLPP Akt phosphatase Drug discovery Computer aided drug design 



We thank Professor Alexandra C. Newton for stimulating discussions. This work was supported in part by the Molecular Biophysics Training grant GM08326 (W.S.), the NSF grant MCB-0506593, the NIH grant GM31749, NBCR, CTBP, HHMI, the NSF Supercomputer Centers (J.A.M.), and the Juvenile Diabetes Research Foundation grant 3-2008-478 (E.S.).


  1. 1.
    Jorgensen, W. L. (2004) The many roles of computation in drug discovery, Science (New York, N.Y.) 303, 1813–1818.Google Scholar
  2. 2.
    Zoete, V., Grosdidier, A., and Michielin, O. (2009) Docking, virtual high throughput screening and in silico fragment-based drug design, Journal of Cellular and Molecular Medicine 13, 238–248.PubMedCrossRefGoogle Scholar
  3. 3.
    Gao, T., Furnari, F., and Newton, A. C. (2005) PHLPP: A Phosphatase that Directly Dephosphorylates Akt, Promotes Apoptosis, and Suppresses Tumor Growth, Molecular Cell 18, 13–24.PubMedCrossRefGoogle Scholar
  4. 4.
    Brognard, J., and Newton, A. C. (2008) PHLiPPing the switch on Akt and protein kinase C signaling, Trends Endocrinol Metab 19, 223–230.PubMedCrossRefGoogle Scholar
  5. 5.
    Ouillette, P., Erba, H., Kujawski, L., Kaminski, M., Shedden, K., and Malek, S. N. (2008) Integrated Genomic Profiling of Chronic Lymphocytic Leukemia Identifies Subtypes of Deletion 13q14, Cancer Res 68, 1012–1021.PubMedCrossRefGoogle Scholar
  6. 6.
    Olaf, J. C. H., Jan-Peer, R., Legrehndem, E. A., Andreas, M. L., Christian, E., Sascha, A., Hendrik, I., Markus, G., Hartwig, H., and Thorsten, S. (2008) A comprehensive analysis of transcript signatures of the phosphatidylinositol-3 kinase/protein kinase B signal-transduction pathway in prostate cancer, BJU International 101, 1454–1460.CrossRefGoogle Scholar
  7. 7.
    Qiao, M., Iglehart, J. D., and Pardee, A. B. (2007) Metastatic Potential of 21 T Human Breast Cancer Cells Depends on Akt/Protein Kinase B Activation, Cancer Res 67, 5293–5299.PubMedCrossRefGoogle Scholar
  8. 8.
    Liu, J., Weiss, H. L., Rychahou, P., Jackson, L. N., Evers, B. M., and Gao, T. (2008) Loss of PHLPP expression in colon cancer: role in proliferation and tumorigenesis, Oncogene 28, 994–1004.PubMedCrossRefGoogle Scholar
  9. 9.
    Hirano, I., Nakamura, S., Yokota, D., Ono, T., Shigeno, K., Fujisawa, S., Shinjo, K., and Ohnishi, K. (2009) Depletion of Pleckstrin Homology Domain Leucine-rich Repeat Protein Phosphatases 1 and 2 by Bcr-Abl Promotes Chronic Myelogenous Leukemia Cell Proliferation through Continuous Phosphorylation of Akt Isoforms, J. Biol. Chem. 284, 22155–22165.PubMedCrossRefGoogle Scholar
  10. 10.
    Armstrong, S. C. (2004) Protein kinase activation and myocardial ischemia/reperfusion injury, Cardiovasc Res 61, 427–436.PubMedCrossRefGoogle Scholar
  11. 11.
    Zdychova, J., and Komers, R. (2005) Emerging role of Akt kinase/protein kinase B signaling in pathophysiology of diabetes and its complications, Physiol Res 54, 1–16.PubMedGoogle Scholar
  12. 12.
    Mumby, M. C., and Walter, G. (1993) Protein serine/threonine phosphatases: structure, regulation, and functions in cell growth, Physiological Reviews 73, 673–699.PubMedGoogle Scholar
  13. 13.
  14. 14.
    Sierecki, E., Sinko, W., McCammon, J. A., and Newton, A. C. Discovery of small molecule inhibitors of the PH domain leucine-rich repeat protein phosphatase (PHLPP) by chemical and virtual screening, J Med Chem 53, 6899–6911.Google Scholar
  15. 15.
    Mayr, L. M., and Bojanic, D. (2009) Novel trends in high-throughput screening, Curr Opin Pharmacol 9, 580–588.PubMedCrossRefGoogle Scholar
  16. 16.
    Hertzberg, R. P., and Pope, A. J. (2000) High-throughput screening: new technology for the 21st century, Current Opinion in Chemical Biology 4, 445–451.PubMedCrossRefGoogle Scholar
  17. 17.
    Maestro, version 9.1, Schrödinger LLC: New York, NY, 2010.Google Scholar
  18. 18.
  19. 19.
    Eswar, N., Webb, B., Marti-Renom, M. A., Madhusudhan, M., Eramian, D., Shen, M. y., Pieper, U., and Sali, A. (2001) Comparative Protein Structure Modeling Using MODELLER, John Wiley & Sons, Inc.Google Scholar
  20. 20.
    Das, A. K., Helps, N. R., Cohen, P. T., and Barford, D. (1996) Crystal structure of the protein serine/threonine phosphatase 2 C at 2.0 A resolution, The EMBO journal 15, 6798–6809.PubMedGoogle Scholar
  21. 21.
    Rogers, J. P., Beuscher, A. E. T., Flajolet, M., McAvoy, T., Nairn, A. C., Olson, A. J., and Greengard, P. (2006) Discovery of protein phosphatase 2 C inhibitors by virtual screening, Journal of medicinal chemistry 49, 1658–1667.PubMedCrossRefGoogle Scholar
  22. 22.
    Larkin, M. A., Blackshields, G., Brown, N. P., Chenna, R., McGettigan, P. A., McWilliam, H., Valentin, F., Wallace, I. M., Wilm, A., Lopez, R., Thompson, J. D., Gibson, T. J., and Higgins, D. G. (2007) Clustal W and Clustal X version 2.0, Bioinformatics 23, 2947–2948.PubMedCrossRefGoogle Scholar
  23. 23.
    Schrödinger Suite 2010 Protein Preparation Wizard; Epik version 2.1, Schrödinger, LLC, New York, NY, 2010; Impact version 5.6, Schrödinger, LLC, New York, NY, 2010; Prime version 2.2, Schrödinger, LLC, New York, NY, 2010.Google Scholar
  24. 24.
    MacroModel, version 9.8, Schrödinger LLC: New York, NY, 2010.Google Scholar
  25. 25.
  26. 26.
  27. 27.
    LigPrep, version 2.4 Schrödinger LLC: New York, NY, 2010.Google Scholar
  28. 28.
    Friesner, R. A., Banks, J. L., Murphy, R. B., Halgren, T. A., Klicic, J. J., Mainz, D. T., Repasky, M. P., Knoll, E. H., Shelley, M., Perry, J. K., Shaw, D. E., Francis, P., and Shenkin, P. S. (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy, Journal of medicinal chemistry 47, 1739–1749.PubMedCrossRefGoogle Scholar
  29. 29.
    Goodsell, D. S., Morris, G. M., and Olson, A. J. (1996) Automated docking of flexible ligands: applications of AutoDock, J Mol Recognit 9, 1–5.PubMedCrossRefGoogle Scholar
  30. 30.
    Trott, O., and Olson, A. J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading, Journal of Computational Chemistry 31, 455–461.Google Scholar
  31. 31.
    Jones, G., Willett, P., Glen, R. C., Leach, A. R., and Taylor, R. (1997) Development and validation of a genetic algorithm for flexible docking, J Mol Biol 267, 727–748.PubMedCrossRefGoogle Scholar
  32. 32.
    Zsoldos, Z., Reid, D., Simon, A., Sadjad, S., and Johnson, P. (2007) eHiTS: A new fast, exhaustive flexible ligand docking system, Journal of Molecular Graphics and Modelling 26, 198–212.PubMedCrossRefGoogle Scholar
  33. 33.
    Jain, A. N. (2003) Surflex: A Fully Automatic Flexible Molecular Docking Using a Molecular Similarity-Based Search Engine, Journal of medicinal chemistry 46, 499–511.PubMedCrossRefGoogle Scholar
  34. 34.
    Lin, J. H., Perryman, A. L., Schames, J. R., and McCammon, J. A. (2002) Computational drug design accommodating receptor flexibility: the relaxed complex scheme, Journal of the American Chemical Society 124, 5632–5633.PubMedCrossRefGoogle Scholar
  35. 35.
    Lin, J. H., Perryman, A. L., Schames, J. R., and McCammon, J. A. (2003) The relaxed complex method: Accommodating receptor flexibility for drug design with an improved scoring scheme, Biopolymers 68, 47–62.PubMedCrossRefGoogle Scholar
  36. 36.
    Amaro, R. E., Baron, R., and McCammon, J. A. (2008) An improved relaxed complex scheme for receptor flexibility in computer-aided drug design, Journal of computer-aided molecular design 22, 693–705.PubMedCrossRefGoogle Scholar
  37. 37.
    Hamelberg, D., de Oliveira, C. A., and McCammon, J. A. (2007) Sampling of slow diffusive conformational transitions with accelerated molecular dynamics, The Journal of chemical physics 127, 155102.PubMedCrossRefGoogle Scholar
  38. 38.
    Hamelberg, D., Mongan, J., and McCammon, J. A. (2004) Accelerated molecular dynamics: a promising and efficient simulation method for biomolecules, The Journal of chemical physics 120, 11919–11929.PubMedCrossRefGoogle Scholar
  39. 39.
    Hamelberg, D., and McCammon, J. A. (2005) Fast peptidyl cis-trans isomerization within the flexible Gly-rich flaps of HIV-1 protease, J Am Chem Soc 127, 13778–13779.PubMedCrossRefGoogle Scholar
  40. 40.
    Hamelberg, D., Shen, T., and Andrew McCammon, J. (2005) Relating kinetic rates and local energetic roughness by accelerated molecular-dynamics simulations, J Chem Phys 122, 241103.PubMedCrossRefGoogle Scholar
  41. 41.
    de Oliveira, C. A., Hamelberg, D., and McCammon, J. A. (2006) On the application of accelerated molecular dynamics to liquid water simulations, J Phys Chem B 110, 22695–22701.PubMedCrossRefGoogle Scholar
  42. 42.
    de Oliveira, C. A., Hamelberg, D., and McCammon, J. A. (2007) Estimating kinetic rates from accelerated molecular dynamics simulations: alanine dipeptide in explicit solvent as a case study, J Chem Phys 127, 175105.PubMedCrossRefGoogle Scholar
  43. 43.
    de Oliveira, C. A., Hamelberg, D., and McCammon, J. A. (2008) Coupling Accelerated Molecular Dynamics Methods with Thermodynamic Integration Simulations, J Chem Theory Comput 4, 1516–1525.PubMedCrossRefGoogle Scholar
  44. 44.
    Amaro, R. E., and Li, W. W. (2009) Emerging Methods for Ensemble-Based Virtual Screening, Current topics in medicinal chemistry.Google Scholar
  45. 45.
    Arnold, K., Bordoli, L., Kopp, J., and Schwede, T. (2006) The SWISS-MODEL workspace: a web-based environment for protein structure homology modelling, Bioinformatics 22, 195–201.PubMedCrossRefGoogle Scholar
  46. 46.
    Lammers, T., and Lavi, S. (2007) Role of type 2C protein phosphatases in growth regulation and in cellular stress signaling, Crit Rev Biochem Mol Biol 42, 437–461.PubMedCrossRefGoogle Scholar
  47. 47.
    Schweighofer, A., Hirt, H., and Meskiene, I. (2004) Plant PP2C phosphatases: emerging functions in stress signaling, Trends Plant Sci 9, 236–243.PubMedCrossRefGoogle Scholar
  48. 48.
    Irwin, J. J., and Shoichet, B. K. (2004) ZINC- A Free Database of Commercially Available Compounds for Virtual Screening, Journal of Chemical Information and Modeling 45, 177–182.CrossRefGoogle Scholar
  49. 49.
    Eldridge, M. D., Murray, C. W., Auton, T. R., Paolini, G. V., and Mee, R. P. (1997) Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes, J Comput Aided Mol Des 11, 425–445.PubMedCrossRefGoogle Scholar
  50. 50.
    Friesner, R. A., Murphy, R. B., Repasky, M. P., Frye, L. L., Greenwood, J. R., Halgren, T. A., Sanschagrin, P. C., and Mainz, D. T. (2006) Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes, Journal of medicinal chemistry 49, 6177–6196.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of Chemistry and Biochemistry, Center for Theoretical Biological PhysicsUniversity of California, San DiegoLa JollaUSA
  2. 2.Department of PharmacologyUniversity of California, San DiegoLa JollaUSA
  3. 3.Department of Chemistry and Biochemistry, Center for Theoretical Biological Physics, Howard Hughes Medical InstituteUniversity of CaliforniaLa JollaUSA
  4. 4.Howard Hughes Medical Institute, Departments of Chemistry and Biochemistry and Pharmacology, Center for Theoretical Biological PhysicsUniversity of California, San DiegoLa JollaUSA

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