Skip to main content
Log in

Ensemble pharmacophore meets ensemble docking: a novel screening strategy for the identification of RIPK1 inhibitors

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

Abstract

Programmed cell death has been a fascinating area of research since it throws new challenges and questions in spite of the tremendous ongoing research in this field. Recently, necroptosis, a programmed form of necrotic cell death, has been implicated in many diseases including neurological disorders. Receptor interacting serine/threonine protein kinase 1 (RIPK1) is an important regulatory protein involved in the necroptosis and inhibition of this protein is essential to stop necroptotic process and eventually cell death. Current structure-based virtual screening methods involve a wide range of strategies and recently, considering the multiple protein structures for pharmacophore extraction has been emphasized as a way to improve the outcome. However, using the pharmacophoric information completely during docking is very important. Further, in such methods, using the appropriate protein structures for docking is desirable. If not, potential compound hits, obtained through pharmacophore-based screening, may not have correct ranks and scores after docking. Therefore, a comprehensive integration of different ensemble methods is essential, which may provide better virtual screening results. In this study, dual ensemble screening, a novel computational strategy was used to identify diverse and potent inhibitors against RIPK1. All the pharmacophore features present in the binding site were captured using both the apo and holo protein structures and an ensemble pharmacophore was built by combining these features. This ensemble pharmacophore was employed in pharmacophore-based screening of ZINC database. The compound hits, thus obtained, were subjected to ensemble docking. The leads acquired through docking were further validated through feature evaluation and molecular dynamics simulation.

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

Access this article

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

Instant access to the full article PDF.

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
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Bramlett HM, Dietrich WD (2004) Pathophysiology of cerebral ischemia and brain trauma: similarities and differences. J Cereb Blood Flow Metab 24:133–150. doi:10.1097/01.WCB.0000111614.19196.04

    Article  Google Scholar 

  2. Tsujimoto Y, Shimizu S (2005) Another way to die: autophagic programmed cell death. Cell Death Differ S2:1528–1534. doi:10.1038/sj.cdd.4401777

    Article  Google Scholar 

  3. Thornton C, Rousset CI, Kichev A, Miyakuni Y, Vontell R, Baburamani AA, Fleiss B, Gressens P, Hagberg H (2012) Molecular mechanisms of neonatal brain injury. Neurol Res Int 2012:506320. doi:10.1155/2012/506320

    Google Scholar 

  4. Zhang DW, Shao J, Lin J, Zhang N, Lu BJ, Lin SC, Dong MQ, Han J (2009) RIP3, an energy metabolism regulator that switches TNF-induced cell death from apoptosis to necrosis. Science 325:332–336. doi:10.1126/science.1172308

    Article  CAS  Google Scholar 

  5. He S, Wang L, Miao L, Wang T, Du F, Zhao L, Wang X (2009) Receptor interacting protein kinase-3 determines cellular necrotic response to TNF-alpha. Cell 137:1100–1111. doi:10.1016/j.cell.2009.05.021

    Article  CAS  Google Scholar 

  6. Pinheiro da Silva F, Nizet V (2009) Cell death during sepsis: integration of disintegration in the inflammatory response to overwhelming infection. Apoptosis 14:509–521. doi:10.1007/s10495-009-0320-3

    Article  Google Scholar 

  7. Fayaz SM, Suvanish Kumar V, Rajanikant GK (2014) Necroptosis: who knew there were so many interesting ways to die? CNS Neurol Disord: Drug Targets 13:42–51. doi:10.2174/18715273113126660189

    Article  CAS  Google Scholar 

  8. Linkermann A, Hackl MJ, Kunzendorf U, Walczak H, Krautwald S, Jevnikar AM (2013) Necroptosis in immunity and ischemia-reperfusion injury. Am J Transplant 13:2797–2804. doi:10.1111/ajt.12448

    Article  CAS  Google Scholar 

  9. Mehta SL, Manhas N, Raghubir R (2007) Molecular targets in cerebral ischemia for developing novel therapeutics. Brain Res Rev 54:34–66. doi:10.1016/j.brainresrev.2006.11.003

    Article  CAS  Google Scholar 

  10. Chan FK, Shisler J, Bixby JG, Felices M, Zheng L, Appel M, Orenstein J, Moss B, Lenardo MJ (2003) A role for tumor necrosis factor receptor-2 and receptor-interacting protein in programmed necrosis and antiviral responses. J Biol Chem 278:51613–51621. doi:10.1074/jbc.M305633200

    Article  CAS  Google Scholar 

  11. Holler N, Zaru R, Micheau O, Thome M, Attinger A, Valitutti S, Bodmer JL, Schneider P, Seed B, Tschopp J (2000) Fas triggers an alternative, caspase-8-independent cell death pathway using the kinase RIP as effector molecule. Nat Immunol 1:489–495. doi:10.1038/82732

    Article  CAS  Google Scholar 

  12. Lin Y, Choksi S, Shen HM, Yang QF, Hur GM, Kim YS, Tran JH, Nedospasov SA, Liu ZG (2004) Tumor necrosis factor-induced nonapoptotic cell death requires receptor-interacting protein-mediated cellular reactive oxygen species accumulation. J Biol Chem 279:10822–10828. doi:10.1074/jbc.M313141200

    Article  CAS  Google Scholar 

  13. Cusson-Hermance N, Khurana S, Lee TH, Fitzgerald KA, Kelliher MA (2005) Rip1 mediates the Trif-dependent toll-like receptor 3- and 4-induced NF-kB activation but does not contribute to interferon regulatory factor 3 activation. J Biol Chem 280:36560–36566. doi:10.1074/jbc.M506831200

    Article  CAS  Google Scholar 

  14. Hsu H, Huang J, Shu HB, Baichwal V, Goeddel DV (1996) TNF dependent recruitment of the protein kinase RIP to the TNF receptor-1 signaling complex. Immunity 4:387–396. doi:10.1016/S1074-7613(00)80252-6

    Article  CAS  Google Scholar 

  15. Meylan E, Burns K, Hofmann K, Blancheteau V, Martinon F, Kelliher M, Tschopp J (2004) RIP1 is an essential mediator of Toll-like receptor3-induced NF-kB activation. Nat Immunol 5:503–507. doi:10.1038/ni1061

    Article  CAS  Google Scholar 

  16. Ting AT, Pimentel-Muinos FX, Seed B (1996) RIP mediates tumor necrosis factor receptor 1 activation of NF-kappaB but not Fas/APO-1-initiated apoptosis. EMBO J 15:6189–6196

    CAS  Google Scholar 

  17. Degterev A, Huang Z, Boyce M, Li Y, Jagtap P, Mizushima N, Cuny GD, Mitchison TJ, Moskowitz MA, Yuan J (2005) Chemical inhibitor of nonapoptotic cell death with therapeutic potential for ischemic brain injury. Nat Chem Biol 1:112–119. doi:10.1038/nchembio711

    Article  CAS  Google Scholar 

  18. Degterev A, Hitomi J, Germscheid M, Ch’en IL, Korkina O, Teng X, Abbott D, Cuny GD, Yuan C, Wagner G, Hedrick SM, Gerber SA, Lugovskoy A, Yuan J (2008) Identification of RIP1 kinase as a specific cellular target of necrostatins. Nat Chem Biol 5:313–321. doi:10.1038/nchembio.83

    Article  Google Scholar 

  19. Ch’en IL, Beisner DR, Degterev A, Lynch C, Yuan J, Hoffmann A, Hedrick SM (2008) Antigen-mediated T cell expansion regulated by parallel pathways of death. Proc Natl Acad Sci USA 105:17463–17468. doi:10.1073/pnas.0808043105

    Article  Google Scholar 

  20. Smith CC, Davidson SM, Lim SY, Simpkin JC, Hothersall JS, Yellon DM (2007) Necrostatin: a potentially novel cardioprotective agent? Cardiovasc Drugs Ther 21:227–233. doi:10.1007/s10557-007-6035-1

    Article  CAS  Google Scholar 

  21. Xu X, Chua CC, Kong J, Kostrzewa RM, Kumaraguru U, Hamdy RC, Chua BH (2007) Necrostatin-1 protects against glutamate-induced glutathione depletion and caspase-independent cell death in HT-22 cells. J Neurochem 103:2004–2014. doi:10.1111/j.1471-4159.2007.04884.x

    Article  CAS  Google Scholar 

  22. Bao L, Li Y, Deng SX, Landry D, Tabas I (2006) Sitosterol-containing lipoproteins trigger free sterol-induced caspase-independent death in ACAT-competent macrophages. J Biol Chem 281:33635–33649. doi:10.1074/jbc.M606339200

    Article  CAS  Google Scholar 

  23. Hong Q, Hsu LJ, Schultz L, Pratt N, Mattison J, Chang NS (2007) Zfra affects TNF-mediated cell death by interacting with death domain protein TRADD and negatively regulates the activation of NF-kappaB, JNK1, p53 and WOX1 during stress response. BMC Mol Biol 8:50. doi:10.1186/1471-2199-8-50

    Article  Google Scholar 

  24. Declercq W, VandenBerghe T, Vandenabeele P (2009) RIP kinases at the crossroads of cell death and survival. Cell 23:229–232. doi:10.1016/j.cell.2009.07.006

    Article  Google Scholar 

  25. Wermuth CG (2006) Pharmacophores: historical perspective and viewpoint from a medicinal chemist. In: Langer T, Hoffmann RD (eds) Pharmacophores and pharmacophore searches, 1st edn. Wiley, New York, pp 1–13

    Chapter  Google Scholar 

  26. Ekins S, Mestres J, Testa B (2007) In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling. Br J Pharmacol 152:9–20. doi:10.1038/sj.bjp.0707305

    Article  CAS  Google Scholar 

  27. Nair SB, Fayaz SM, Krishnamurthy RG (2012) In silico prediction of novel inhibitors of the DNA binding activity of FoxG1. Med Chem 8:1155–1162. doi:10.2174/1573406411208061155

    CAS  Google Scholar 

  28. Yang SY (2010) Pharmacophore modeling and applications in drug discovery: challenges and recent advances. Drug Discov Today 15:444–450. doi:10.1016/j.drudis.2010.03.013

    Article  CAS  Google Scholar 

  29. Dror O, Shulman-Peleg A, Nussinov R, Wolfson HJ (2004) Predicting molecular interactions in silico: I. A guide to pharmacophore identification and its applications to drug design. Curr Med Chem 11:71–90. doi:10.2174/0929867043456287

    Article  CAS  Google Scholar 

  30. Khedkar SA, Malde AK, Coutinho EC, Srivastava S (2007) Pharmacophore modeling in drug discovery and development: an overview. Med Chem 3:187–197. doi:10.2174/157340607780059521

    Article  CAS  Google Scholar 

  31. Wolber G, Seidel T, Bendix F, Langer T (2008) Molecule-pharmacophore superpositioning and pattern matching in computational drug design. Drug Discov Today 13:23–29. doi:10.1016/j.drudis.2007.09.007

    Article  CAS  Google Scholar 

  32. Kirchmair J, Laggner C, Wolber G, Langer T (2005) Comparative analysis of protein-bound ligand conformations with respect to catalyst’s conformational space subsampling algorithms. J Chem Inf Model 45:422–430. doi:10.1021/ci049753l

    Article  CAS  Google Scholar 

  33. Kirchmair J, Wolber G, Laggner C, Langer T (2006) Comparative performance assessment of the conformational model generators omega and catalyst: a large-scale survey on the retrieval of protein-bound ligand conformations. J Chem Inf Model 46:1848–1861. doi:10.1021/ci060084g

    Article  CAS  Google Scholar 

  34. Kristam R, Gillet VJ, Lewis RA, Thorner D (2005) Comparison of conformational analysis techniques to generate pharmacophore hypotheses using catalyst. J Chem Inf Model 45:461–476. doi:10.1021/ci049731z

    Article  CAS  Google Scholar 

  35. Hecker EA, Duraiswami C, Andrea TA, Diller DJ (2002) Use of catalyst pharmacophore models for screening of large combinatorial libraries. J Chem Inf Comput Sci 42:1204–1211. doi:10.1021/ci020368a

    Article  CAS  Google Scholar 

  36. Toba S, Srinivasan J, Maynard AJ, Sutter J (2006) Using pharmacophore models to gain insight into structural binding and virtual screening: an application study with CDK2 and human DHFR. J Chem Inf Model 46:728–735. doi:10.1021/ci050410c

    Article  CAS  Google Scholar 

  37. Vadivelan S, Sinha BN, Irudayam SJ, Jagarlapudi SA (2007) Virtual screening studies to design potent CDK2-cyclin A inhibitors. J Chem Inf Model 47:1526–1535. doi:10.1021/ci7000742

    Article  CAS  Google Scholar 

  38. Kurogi Y, Güner OF (2001) Pharmacophore modeling and three-dimensional database searching for drug design using catalyst. Curr Med Chem 8:1035–1055. doi:10.2174/0929867013372481

    Article  CAS  Google Scholar 

  39. Güner OF (2002) History and evolution of the pharmacophore concept in computer-aided drug design. Curr Top Med Chem 2:1321–1332. doi:10.2174/1568026023392940

    Article  Google Scholar 

  40. Xiao Z, Varma S, Xiao YD, Tropsha A (2004) Modeling of p38 mitogen-activated protein kinase inhibitors using the Catalyst HypoGen and k-nearest neighbor QSAR methods. J Mol Graph Model 23:129–138. doi:10.1016/j.jmgm.2004.05.001

    Article  CAS  Google Scholar 

  41. Saxena S, Devi PB, Soni V, Yogeeswari P, Sriram D (2014) Identification of novel inhibitors against Mycobacterium tuberculosis l-alanine dehydrogenase (MTB-AlaDH) through structure-based virtual screening. J Mol Graph Model 47:37–43. doi:10.1016/j.jmgm.2013.08.005

    Article  CAS  Google Scholar 

  42. Nair SB, Fayaz SM, Rajanikant GK (2013) A novel multi-target drug screening strategy directed against key proteins of DAPk family. Comb Chem High Throughput Screen 16:449–457. doi:10.2174/1386207311316060005

    Article  CAS  Google Scholar 

  43. Zou J, Xie HZ, Yang SY, Chen JJ, Ren JX, Wei YQ (2008) Towards more accurate pharmacophore modeling: multicomplex based comprehensive pharmacophore map and most-frequent-feature pharmacophore model of CDK2. J Mol Graph Model 27:430–438. doi:10.1016/j.jmgm.2008.07.004

    Article  CAS  Google Scholar 

  44. Hein M, Zilian D, Sotriffer CA (2010) Docking compared to 3D pharmacophores: the scoring function challenge. Drug Discov Today Technol 7:229–236. doi:10.1016/j.ddtec.2010.12.003

    Article  Google Scholar 

  45. Drwal MN, Agama K, Pommier Y, Griffith R (2013) Development of purely structure-based pharmacophores for the topoisomerase I-DNA-ligand binding pocket. J Comput Aided Mol Des 27:1037–1049. doi:10.1007/s10822-013-9695-x

    Article  CAS  Google Scholar 

  46. Thangapandian S, John S, Sakkiah S, Lee KW (2010) Docking-enabled pharmacophore model for histone deacetylase 8 inhibitors and its application in anti-cancer drug discovery. J Mol Graph Model 29:382–395. doi:10.1016/j.jmgm.2010.07.007

    Article  Google Scholar 

  47. 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 47:1739–1749. doi:10.1021/jm0306430

    Article  CAS  Google Scholar 

  48. Salam NK, Nuti R, Sherman W (2009) Novel method for generating structure-based pharmacophores using energetic analysis. J Chem Inf Model 49:2356–2368. doi:10.1021/ci900212v

    Article  CAS  Google Scholar 

  49. Irwin JJ, Shoichet BK (2005) ZINC—a free database of commercially available compounds for virtual screening. J Chem Inf Model 45:177–182. doi:10.1021/ci049714

    Article  CAS  Google Scholar 

  50. Verdonk ML, Mortenson PN, Hall RJ, Hartshorn MJ, Murray CW (2008) Protein-ligand docking against non-native protein conformers. J Chem Inf Model 48:2214–2225. doi:10.1021/ci8002254

    Article  CAS  Google Scholar 

  51. Cavasotto CN (2012) Normal mode-based approaches in receptor ensemble docking. Methods Mol Biol 819:157–168. doi:10.1007/978-1-61779-465-0_11

    Article  CAS  Google Scholar 

  52. Laskowski RA, Swindells MB (2011) LigPlot + : multiple ligand-protein interaction diagrams for drug discovery. J Chem Inf Model 51:2778–2786. doi:10.1021/ci200227u

    Article  CAS  Google Scholar 

  53. Van Der Spoel D, Lindahl E, Hess B, Groenhof G, Mark AE, Berendsen HJ (2005) GROMACS: fast, flexible, and free. J Comput Chem 26:1701–1718. doi:10.1002/jcc.20291

    Article  Google Scholar 

  54. Hess B, Kutzner C, van der Spoel D, Lindahl E (2008) GROMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation. J Chem Theory Comput 4:435–447. doi:10.1021/ct700301q

    Article  CAS  Google Scholar 

  55. Oostenbrink C, Villa A, Mark AE, van Gunsteren WF (2004) A biomolecular force field based on the free enthalpy of hydration and solvation: the GROMOS force-field parameter sets 53A5 and 53A6. J Comput Chem 25:1656–1676. doi:10.1002/jcc.20090

    Article  CAS  Google Scholar 

  56. van Aalten DM, Bywater R, Findlay JB, Hendlich M, Hooft RW, Vriend G (1996) PRODRG, a program for generating molecular topologies and unique molecular descriptors from coordinates of small molecules. J Comput Aided Mol Des 10:255–262. doi:10.1007/BF00355047

    Article  Google Scholar 

  57. Darden T, York D, Pedersen L (1993) Particle mesh Ewald—an N. log(N) method for Ewald sums in large systems. J Chem Phys 98:10089–10093. doi:10.1063/1.464397

    Article  CAS  Google Scholar 

  58. Berendsen HJC, Postma JPM, Van Gunsteren WF, DiNola A, Haak JR (1984) Molecular dynamics with coupling to an external bath. J Chem Phys 81:3684–3691. doi:10.1063/1.448118

    Article  CAS  Google Scholar 

  59. Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph 14:33–38

    Article  CAS  Google Scholar 

  60. Degterev A, Hitomi J, Germscheid M, Ch’en IL, Korkina O, Teng X, Abbott D, Cuny GD, Yuan C, Wagner G, Hedrick SM, Gerber SA, Lugovskoy A, Yuan J (2008) Identification of RIP1 kinase as a specific cellular target of necrostatins. Nat Chem Biol 4:313–321. doi:10.1038/nchembio.83

    Article  CAS  Google Scholar 

  61. Maki JL, Smith EE, Teng X, Ray SS, Cuny GD, Degterev A (2012) Fluorescence polarization assay for inhibitors of the kinase domain of receptor interacting protein 1. Anal Biochem 427:164–174. doi:10.1016/j.ab.2012.05.019

    Article  CAS  Google Scholar 

  62. Xie T, Peng W, Liu Y, Yan C, Maki J, Degterev A, Yuan J, Shi Y (2013) Structural basis of RIP1 inhibition by necrostatins. Structure 21:493–499. doi:10.1016/j.str.2013.01.016

    Article  CAS  Google Scholar 

  63. Wu Z, Li Y, Cai Y, Yuan J, Yuan C (2013) A novel necroptosis inhibitor-necrostatin-21 and its SAR study. Bioorg Med Chem Lett 23:4903–4906. doi:10.1016/j.bmcl.2013.06.073

    Article  CAS  Google Scholar 

  64. Harris PA, Bandyopadhyay D, Berger SB, Campobasso N, Capriotti CA, Cox JA, Dare L, Finger JN, Hoffman SJ, Kahler KM, Lehr R, Lich JD, Rakesh N, Nolte RT, Ouellette MT, Pao CS, Schaeffer MC, Smallwood A, Sun HH, Swift BA, Totoritis RD, Ward P, Marquis RW, Bertin J, Gough PJ (2013) Discovery of small molecule RIP1 kinase inhibitors for the treatment of pathologies associated with necroptosis. ACS Med Chem Lett 4:1238–1243. doi:10.1021/ml400382p

    Article  CAS  Google Scholar 

  65. Bender A, Glen RC (2005) A discussion of measures of enrichment in virtual screening: comparing the information content of descriptors with increasing levels of sophistication. J Chem Inf Model 45:1369–1375. doi:10.1021/ci0500177

    Article  CAS  Google Scholar 

  66. Jain AN, Nicholls A (2008) Recommendations for evaluation of computational methods. J Comput Aided Mol Des 22:133–139. doi:10.1007/s10822-008-9196-5

    Article  CAS  Google Scholar 

  67. Hamza A, Wei NN, Zhan CG (2012) Ligand-based virtual screening approach using a new scoring function. J Chem Inf Model 52:963–974. doi:10.1021/ci200617d

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to G. K. Rajanikant.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fayaz, S.M., Rajanikant, G.K. Ensemble pharmacophore meets ensemble docking: a novel screening strategy for the identification of RIPK1 inhibitors. J Comput Aided Mol Des 28, 779–794 (2014). https://doi.org/10.1007/s10822-014-9771-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10822-014-9771-x

Keywords

Navigation