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Structure-Based Virtual Screening of Commercially Available Compound Libraries

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High Throughput Screening

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1439))

Abstract

Virtual screening (VS) is an efficient hit-finding tool. Its distinctive strength is that it allows one to screen compound libraries that are not available in the lab. Moreover, structure-based (SB) VS also enables an understanding of how the hit compounds bind the protein target, thus laying ground work for the rational hit-to-lead progression. SBVS requires a very limited experimental effort and is particularly well suited for academic labs and small biotech companies that, unlike pharmaceutical companies, do not have physical access to quality small-molecule libraries. Here, we describe SBVS of commercial compound libraries for Mer kinase inhibitors. The screening protocol relies on the docking algorithm Glide complemented by a post-docking filter based on structural protein-ligand interaction fingerprints (SPLIF).

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References

  1. Meng X-Y, Zhang H-X, Mezei M, Cui M (2011) Molecular docking: a powerful approach for structure-based drug discovery. Curr Comput Aided Drug Des 7(2):146

    Article  CAS  Google Scholar 

  2. Jain AN (2006) Scoring functions for protein-ligand docking. Curr Protein Pept Sci 7(5):407–420

    Article  CAS  Google Scholar 

  3. Kirchmair J, Markt P, Distinto S, Wolber G, Langer T (2008) Evaluation of the performance of 3D virtual screening protocols: RMSD comparisons, enrichment assessments, and decoy selection—what can we learn from earlier mistakes? J Comput Aided Mol Des 22(3):213–228. doi:10.1007/s10822-007-9163-6

    Article  CAS  Google Scholar 

  4. Scior T, Bender A, Tresadern G, Medina-Franco JL, Martínez-Mayorga K, Langer T, Cuanalo-Contreras K, Agrafiotis DK (2012) Recognizing pitfalls in virtual screening: a critical review. J Chem Inf Model 52(4):867–881. doi:10.1021/ci200528d

    Article  CAS  Google Scholar 

  5. Warren G, Andrews C, Capelli A, Clarke B, LaLonde J (2005) A critical assessment of docking programs and scoring functions. J Med Chem 49:5912–5931

    Article  Google Scholar 

  6. Kitchen DB, Decornez H, Furr JR, Bajorath J (2004) Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov 3(11):935–949. doi:10.1038/nrd1549, nrd1549 [pii]

    Article  CAS  Google Scholar 

  7. Ferrara P, Gohlke H, Price D, Klebe G, Brooks CI (2004) Assessing scoring functions for protein-ligand interactions. J Med Chem 47:3032–3047

    Article  CAS  Google Scholar 

  8. Cross JB, Thompson DC, Rai BK, Baber JC, Fan KY, Hu Y, Humblet C (2009) Comparison of several molecular docking programs: pose prediction and virtual screening accuracy. J Chem Inf Model 49(6):1455–1474. doi:10.1021/ci900056c

    Article  CAS  Google Scholar 

  9. Kellenberger E, Rodrigo J, Muller P, Rognan D (2004) Comparative evaluation of eight docking tools for docking and virtual screening accuracy. Proteins 57(2):225–242. doi:10.1002/prot.20149

    Article  CAS  Google Scholar 

  10. Da C, Stashko M, Jayakody C, Wang X, Janzen W, Frye S, Kireev D (2015) Discovery of Mer kinase inhibitors by virtual screening using structural protein–ligand interaction fingerprints. Bioorg Med Chem 23(5):1096–1101. doi:10.1016/j.bmc.2015.01.001

    Article  CAS  Google Scholar 

  11. Da C, Kireev D (2014) Structural protein–ligand interaction fingerprints (SPLIF) for structure-based virtual screening: method and benchmark study. J Chem Inf Model 54(9):2555–2561. doi:10.1021/ci500319f

    Article  CAS  Google Scholar 

  12. Mortier J, Rakers C, Bermudez M, Murgueitio MS, Riniker S, Wolber G (2015) The impact of molecular dynamics on drug design: applications for the characterization of ligand–macromolecule complexes. Drug Discov Today 20(6):686–702. doi:10.1016/j.drudis.2015.01.003

    Article  CAS  Google Scholar 

  13. Gao C, Herold JM, Kireev D (2012) Assessment of free energy predictors for ligand binding to a methyllysine histone code reader. J Comput Chem 33(6):659–665. doi:10.1002/jcc.22888

    Article  CAS  Google Scholar 

  14. Graham DK, Salzberg DB, Kurtzberg J, Sather S, Matsushima GK, Keating AK, Liang X, Lovell MA, Williams SA, Dawson TL, Schell MJ, Anwar AA, Snodgrass HR, Earp HS (2006) Ectopic expression of the proto-oncogene mer in pediatric T-cell acute lymphoblastic leukemia. Clin Cancer Res 12(9):2662–2669. doi:10.1158/1078-0432.ccr-05-2208

    Article  CAS  Google Scholar 

  15. Christoph S, DeRyckere D, Sather S, Wang XD, Kireev D, Janzen W, Liu J, Yang C, van Deusen A, Simpson C, Norris-Drouin J, Frye S, Earp HS, Johnson GL, Graham DK (2011) UNC569 as novel small molecule mer receptor tyrosine kinase inhibitor for treatment of ALL. Blood 118(21):1111–1112

    Google Scholar 

  16. Liu J, Zhang W, Stashko MA, Deryckere D, Cummings CT, Hunter D, Yang C, Jayakody CN, Cheng N, Simpson C, Norris-Drouin J, Sather S, Kireev D, Janzen WP, Earp HS, Graham DK, Frye SV, Wang X (2013) UNC1062, a new and potent Mer inhibitor. Eur J Med Chem 65:83–93. doi:10.1016/j.ejmech.2013.03.035

    Article  CAS  Google Scholar 

  17. Zhang W, McIver AL, Stashko MA, DeRyckere D, Branchford BR, Hunter D, Kireev D, Miley MJ, Norris-Drouin J, Stewart WM, Lee M, Sather S, Zhou Y, Di Paola JA, Machius M, Janzen WP, Earp HS, Graham DK, Frye SV, Wang X (2013) Discovery of Mer specific tyrosine kinase inhibitors for the treatment and prevention of thrombosis. J Med Chem 56(23):9693–9700. doi:10.1021/jm4013888

    Article  CAS  Google Scholar 

  18. Zhang WH, Zhang DH, Stashko MA, DeRyckere D, Hunter D, Kireev D, Miley MJ, Cummings C, Lee M, Norris-Drouin J, Stewart WM, Sather S, Zhou YQ, Kirkpatrick G, Machius M, Janzen WP, Earp HS, Graham DK, Frye SV, Wang XD (2013) Pseudo-cyclization through intramolecular hydrogen bond enables discovery of pyridine substituted pyrimidines as new mer kinase inhibitors. J Med Chem 56(23):9683–9692. doi:10.1021/Jm401387j

    Article  CAS  Google Scholar 

  19. Zhang W, DeRyckere D, Hunter D, Liu J, Stashko MA, Minson KA, Cummings CT, Lee M, Glaros TG, Newton DL, Sather S, Zhang D, Kireev D, Janzen WP, Earp HS, Graham DK, Frye SV, Wang X (2014) UNC2025, a potent and orally bioavailable MER/FLT3 dual inhibitor. J Med Chem 57(16):7031–7041. doi:10.1021/jm500749d

    Article  CAS  Google Scholar 

  20. 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. doi:10.1021/jm0306430

    Article  CAS  Google Scholar 

  21. Lipinski CA (2000) Drug-like properties and the causes of poor solubility and poor permeability. J Pharmacol Toxicol Methods 44(1):235–249, S1056-8719(00)00107-6 [pii]

    Article  CAS  Google Scholar 

  22. Walters WP, Murcko MA (2002) Prediction of ‘drug-likeness’. Adv Drug Deliv Rev 54(3):255–271

    Article  CAS  Google Scholar 

  23. (2009) Pipeline Pilot, ver 85, Accelrys Software Inc

    Google Scholar 

  24. Huang X, Finerty P Jr, Walker JR, Butler-Cole C, Vedadi M, Schapira M, Parker SA, Turk BE, Thompson DA, Dhe-Paganon S (2009) Structural insights into the inhibited states of the Mer receptor tyrosine kinase. J Struct Biol 165(2):88–96. doi:10.1016/j.jsb.2008.10.003, S1047-8477(08)00249-9 [pii]

    Article  CAS  Google Scholar 

  25. Liu J, Yang C, Simpson C, Deryckere D, Van Deusen A, Miley MJ, Kireev D, Norris-Drouin J, Sather S, Hunter D, Korboukh VK, Patel HS, Janzen WP, Machius M, Johnson GL, Earp HS, Graham DK, Frye SV, Wang X (2012) Discovery of novel small molecule mer kinase inhibitors for the treatment of pediatric acute lymphoblastic leukemia. ACS Med Chem Lett 3(2):129–134

    Article  CAS  Google Scholar 

  26. Halgren TA (1996) Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94. J Comput Chem 17(5-6):490–519

    Article  CAS  Google Scholar 

  27. (2009) Maestro Suite, Schrodinger LLC

    Google Scholar 

  28. Bernasconi P, Chen M, Galasinski S, Popa-Burke I, Bobasheva A, Coudurier L, Birkos S, Hallam R, Janzen WP (2007) A chemogenomic analysis of the human proteome: application to enzyme families. J Biomol Screen 12(7):972–982. doi:10.1177/1087057107306759

    Article  CAS  Google Scholar 

  29. Pommereau A, Pap E, Kannt A (2004) Two simple and generic antibody-independent kinase assays: comparison of a bioluminescent and a microfluidic assay format. J Biomol Screen 9(5):409–416. doi:10.1177/1087057104264175

    Article  CAS  Google Scholar 

  30. Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ (2009) Autodock4 and autodocktools4: automated docking with selective receptor flexibility. J Comput Chem 30(16):2785–2791

    Article  CAS  Google Scholar 

  31. Kuhn T, Willighagen EL, Zielesny A, Steinbeck C (2010) CDK-Taverna: an open workflow environment for cheminformatics. BMC Bioinformatics 11(1):159

    Article  Google Scholar 

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Authors and Affiliations

  1. Center for Integrative Chemical Biology and Drug Discovery, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Campus Box 7363, Marsico Hall, room 3205, 125 Mason Farm Road, Chapel Hill, NC, 27599-7363, USA

    Dmitri Kireev

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  1. Dmitri Kireev
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Correspondence to Dmitri Kireev .

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  1. Epizyme, Inc., Cambridge, Massachusetts, USA

    William P. Janzen

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Kireev, D. (2016). Structure-Based Virtual Screening of Commercially Available Compound Libraries. In: Janzen, W. (eds) High Throughput Screening. Methods in Molecular Biology, vol 1439. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3673-1_4

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  • DOI: https://doi.org/10.1007/978-1-4939-3673-1_4

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-3671-7

  • Online ISBN: 978-1-4939-3673-1

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