Advertisement

Modelling the molecular mechanism of protein–protein interactions and their inhibition: CypD–p53 case study

  • 465 Accesses

  • 2 Citations

Abstract

Cyclophilin D (CypD) is an important regulatory protein involved in mitochondrial membrane permeability transition and cell death. Further, the mitochondrial CypD–p53 axis is an important contributor to necroptosis, a form of programmed necrosis, involved in various cardiovascular and neurological disorders. The CypD ligand, Cyclosporin A (CsA), was identified as an inhibitor of this interaction. In this study, using computational methods, we have attempted to model the CypD–p53 interaction in order to delineate their mode of binding and also to disclose the molecular mechanism, by means of which CsA interferes with this interaction. It was observed that p53 binds at the CsA-binding site of CypD. The knowledge obtained from this modelling was employed to identify novel CypD inhibitors through structure-based methods. Further, the identified compounds were tested by a similar strategy, adopted during the modelling process. This strategy could be applied to study the mechanism of protein–protein interaction (PPI) inhibition and to identify novel PPI inhibitors.

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

Access options

Buy single article

Instant unlimited access to the full article PDF.

US$ 39.95

Price includes VAT for USA

Subscribe to journal

Immediate online access to all issues from 2019. Subscription will auto renew annually.

US$ 99

This is the net price. Taxes to be calculated in checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

References

  1. 1.

    Vaseva AV, Marchenko ND, Ji K, Tsirka SE, Holzmann S, Moll UM (2012) p53 opens the mitochondrial permeability transition pore to trigger necrosis. Cell 149:1536–1548. doi:10.1016/j.cell.2012.05.014

  2. 2.

    Baines CP, Kaiser RA, Purcell NH, Blair NS, Osinska H, Hambleton MA, Brunskill EW, Sayen MR, Gottlieb RA, Dorn GW, Robbins J, Molkentin JD (2005) Loss of cyclophilin D reveals a critical role for mitochondrial permeability transition in cell death. Nature 434:658–662. doi:10.1038/nature03434

  3. 3.

    Basso E, Fante L, Fowlkes J, Petronilli V, Forte MA, Bernardi P (2005) Properties of the permeability transition pore in mitochondria devoid of cyclophilin D. J Biol Chem 280:18558–18561. doi:10.1074/jbc.C500089200

  4. 4.

    Kumarswamy R, Chandna S (2009) Putative partners in Bax mediated cytochrome-c release: ANT, CypD, VDAC or none of them? Mitochondrion 9:1–8. doi:10.1016/j.mito.2008.10.003

  5. 5.

    Du H, Yan SS (2010) Mitochondrial permeability transition pore in Alzheimer’s disease: cyclophilin D and amyloid beta. Biochim Biophys Acta 1802:198–204. doi:10.1016/j.bbadis.2009.07.005

  6. 6.

    Baines CP (2007) The mitochondrial permeability transition pore as a target of cardioprotective signaling. Am J Physiol Heart Circ Physiol 293:H903–H904. doi:10.1152/ajpheart.00575.2007

  7. 7.

    Du H, Guo L, Fang F, Chen D, Sosunov AA, McKhann GM, Yan Y, Wang C, Zhang H, Molkentin JD, Gunn-Moore FJ, Vonsattel JP, Arancio O, Chen JX, Yan SD (2008) Cyclophilin D deficiency attenuates mitochondrial and neuronal perturbation and ameliorates learning and memory in Alzheimer’s disease. Nat Med 14:1097–1105. doi:10.1038/nm.1868

  8. 8.

    Martin LJ, Semenkow S, Hanaford A, Wong M (2014) The mitochondrial permeability transition pore regulates Parkinson’s disease development in mutant \(\alpha \)-synuclein transgenic mice. Neurobiol Aging 35:1132–1152. doi:10.1016/j.neurobiolaging.2013.11.008

  9. 9.

    Shirendeb U, Reddy AP, Manczak M, Calkins MJ, Mao P, Tagle DA, Reddy PH (2011) Abnormal mitochondrial dynamics, mitochondrial loss and mutant huntingtin oligomers in Huntington’s disease: implications for selective neuronal damage. Hum Mol Genet 20:1438–1455. doi:10.1093/hmg/ddr024

  10. 10.

    Schinzel AC, Takeuchi O, Huang Z, Fisher JK, Zhou Z, Rubens J, Hetz C, Danial NN, Moskowitz MA, Korsmeyer SJ (2005) Cyclophilin D is a component of mitochondrial permeability transition and mediates neuronal cell death after focal cerebral ischemia. Proc Natl Acad Sci USA 102:12005–12010. doi:10.1073/pnas.0505294102

  11. 11.

    Sullivan PG, Rabchevsky AG, Waldmeier PC, Springer JE (2005) Mitochondrial permeability transition in CNS trauma: cause or effect of neuronal cell death? J Neurosci Res 79:231–239. doi:10.1002/jnr.20292

  12. 12.

    Nakagawa T, Shimizu S, Watanabe T, Yamaguchi O, Otsu K, Yamagata H, Inohara H, Kubo T, Tsujimoto Y (2005) Cyclophilin D-dependent mitochondrial permeability transition regulates some necrotic but not apoptotic cell death. Nature 434:652–658. doi:10.1038/nature03317

  13. 13.

    Martin LJ (2011) An approach to experimental synaptic pathology using green fluorescent protein-transgenic mice and gene knockout mice to show mitochondrial permeability transition pore-driven excitotoxicity in interneurons and motoneurons. Toxicol Pathol 39:220–233. doi:10.1177/0192623310389475

  14. 14.

    Uchino H, Elmér E, Uchino K, Li PA, He QP, Smith ML, Siesjö BK (1998) Amelioration by cyclosporin A of brain damage in transient forebrain ischemia in the rat. Brain Res 812:216–226. doi:10.1016/S0006-8993(98)00902-0

  15. 15.

    Matsuda S, Moriguchi T, Koyasu S, Nishida E (1998) T lymphocyte activation signals for interleukin-2 production involve activation of MKK6-p38 and MKK7-SAPK/JNK signaling pathways sensitive to cyclosporin A. J Biol Chem 273:12378–12382. doi:10.1074/jbc.273.20.12378

  16. 16.

    Liu J, Farmer JD Jr, Lane WS, Friedman J, Weissman I, Schreiber SL (1991) Calcineurin is a common target of cyclophilin-cyclosporin A and FKBP-FK506 complexes. Cell 66:807–815. doi:10.1016/0092-8674(91)90124-H

  17. 17.

    Lee SP, Hwang YS, Kim YJ, Kwon KS, Kim HJ, Kim K, Chae HZ (2001) Cyclophilin a binds to peroxiredoxins and activates its peroxidase activity. J Biol Chem 276:29826–29832. doi:10.1074/jbc.M101822200

  18. 18.

    Matsuda S, Shibasaki F, Takehana K, Mori H, Nishida E, Koyasu S (2000) Two distinct action mechanisms of immunophilin-ligand complexes for the blockade of T-cell activation. EMBO Rep 1:428–434. doi:10.1093/embo-reports/kvd090

  19. 19.

    Margulies S, Hicks R, Combination Therapies for Traumatic Brain Injury Workshop Leaders (2009) Combination therapies for traumatic brain injury: prospective considerations. J Neurotrauma 26:925–939. doi:10.1089/neu.2008-0794

  20. 20.

    Roy A, Kucukural A, Zhang Y (2010) I-TASSER: a unified platform for automated protein structure and function prediction. Nat Protoc 5:725–738. doi:10.1038/nprot.2010.5

  21. 21.

    Schnell JR, Chou JJ (2008) Structure and mechanism of the M2 proton channel of influenza A virus. Nature 451:591–595. doi:10.1038/nature06531

  22. 22.

    Berardi MJ, Shih WM, Harrison SC, Chou JJ (2011) Mitochondrial uncoupling protein 2 structure determined by NMR molecular fragment searching. Nature 476:109–113. doi:10.1038/nature10257

  23. 23.

    OuYang B, Xie S, Berardi MJ, Zhao X, Dev J, Yu W, Sun B, Chou JJ (2013) Unusual architecture of the p7 channel from hepatitis C virus. Nature 498:521–525. doi:10.1038/nature12283

  24. 24.

    Call ME, Schnell JR, Xu C, Lutz RA, Chou JJ, Wucherpfennig KW (2006) The structure of the zetazeta transmembrane dimer reveals features essential for its assembly with the T cell receptor. Cell 127:355–368. doi:10.1016/j.cell.2006.08.044

  25. 25.

    Wang J, Pielak RM, McClintock MA, Chou JJ (2009) Solution structure and functional analysis of the influenza B proton channel. Nat Struct Mol Biol 16:1267–1271. doi:10.1038/nsmb.1707

  26. 26.

    Call ME, Wucherpfennig KW, Chou JJ (2010) The structural basis for intramembrane assembly of an activating immunoreceptor complex. Nat Immunol 11:1023–1029. doi:10.1038/ni.1943

  27. 27.

    Chou KC, Jones D, Heinrikson RL (1997) Prediction of the tertiary structure and substrate binding site of caspase-8. FEBS Lett 419:49–54. doi:10.1016/S0014-5793(97)01246-5

  28. 28.

    Wang SQ, Du QS, Chou KC (2007) Study of drug resistance of chicken influenza A virus (H5N1) from homology-modeled 3D structures of neuraminidases. Biochem Biophys Res Commun 354:634–640. doi:10.1016/j.bbrc.2006.12.235

  29. 29.

    Chou KC, Tomasselli AG, Heinrikson RL (2000) Prediction of the tertiary structure of a caspase-9/inhibitor complex. FEBS Lett 470:249–256. doi:10.1016/S0014-5793(00)01333-8

  30. 30.

    Wang JF, Wei DQ, Li L, Zheng SY, Li YX, Chou KC (2007) 3D structure modeling of cytochrome P450 2C19 and its implication for personalized drug design. Biochem Biophys Res Commun 355:513–519. doi:10.1016/j.bbrc.2007.01.185

  31. 31.

    Chou KC (2005) Coupling interaction between thromboxane A2 receptor and alpha-13 subunit of guanine nucleotide-binding protein. J Proteome Res 4:1681–1686. doi:10.1021/pr050145a

  32. 32.

    Wang SQ, Du QS, Huang RB, Zhang DW, Chou KC (2009) Insights from investigating the interaction of oseltamivir (Tamiflu) with neuraminidase of the 2009 H1N1 swine flu virus. Biochem Biophys Res Commun 386:432–436. doi:10.1016/j.bbrc.2009.06.016

  33. 33.

    Chou KC (2004) Structural bioinformatics and its impact to biomedical science. Curr Med Chem 11:2105–2134. doi:10.2174/0929867043364667

  34. 34.

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

  35. 35.

    Chou KC, Cai YD (2006) Predicting protein-protein interactions from sequences in a hybridization space. J Proteome Res 5:316–322. doi:10.1021/pr050331g

  36. 36.

    Hu L, Huang T, Shi X, Lu WC, Cai YD, Chou KC (2011) Predicting functions of proteins in mouse based on weighted protein-protein interaction network and protein hybrid properties. PLoS One 6:e14556. doi:10.1371/journal.pone.0014556

  37. 37.

    Huang T, Chen L, Cai YD, Chou KC (2011) Classification and analysis of regulatory pathways using graph property, biochemical and physicochemical property, and functional property. PLoS One 6:e25297. doi:10.1371/journal.pone.0025297

  38. 38.

    Li BQ, Huang T, Liu L, Cai YD, Chou KC (2012) Identification of colorectal cancer related genes with mRMR and shortest path in protein-protein interaction network. PLoS One 7:e33393. doi:10.1371/journal.pone.0033393

  39. 39.

    Jia J, Liu Z, Xiao X, Liu B, Chou KC (2015) iPPI-Esml: an ensemble classifier for identifying the interactions of proteins by incorporating their physicochemical properties and wavelet transforms into PseAAC. J Theor Biol 377:47–56. doi:10.1016/j.jtbi.2015.04.011

  40. 40.

    Pierce BG, Wiehe K, Hwang H, Kim BH, Vreven T, Weng Z (2014) ZDOCK server: interactive docking prediction of protein-protein complexes and symmetric multimers. Bioinformatics 30:1771–1773. doi:10.1093/bioinformatics/btu097

  41. 41.

    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

  42. 42.

    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

  43. 43.

    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

  44. 44.

    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

  45. 45.

    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

  46. 46.

    Wang JF, Chou KC (2009) Insight into the molecular switch mechanism of human Rab5a from molecular dynamics simulations. Biochem Biophys Res Commun 390:608–612. doi:10.1016/j.bbrc.2009.10.014

  47. 47.

    Chou KC (1989) Low-frequency resonance and cooperativity of hemoglobin. Trends Biochem Sci 14:212–213. doi:10.1016/0968-0004(89)90026-1

  48. 48.

    Wang JF, Gong K, Wei DQ, Li YX, Chou KC (2009) Molecular dynamics studies on the interactions of PTP1B with inhibitors: from the first phosphate-binding site to the second one. Protein Eng Des Sel 22:349–355. doi:10.1093/protein/gzp012

  49. 49.

    Chou KC (1987) The biological functions of low-frequency phonons: 6. A possible dynamic mechanism of allosteric transition in antibody molecules. Biopolymers 26:285–295. doi:10.1002/bip.360260209

  50. 50.

    Chou KC, Mao B (1988) Collective motion in DNA and its role in drug intercalation. Biopolymers 27:1795–1815. doi:10.1002/bip.360271109

  51. 51.

    Chou KC, Zhang CT, Maggiora GM (1994) Solitary wave dynamics as a mechanism for explaining the internal motion during microtubule growth. Biopolymers 34:143–153. doi:10.1002/bip.360340114

  52. 52.

    Chou KC (1988) Low-frequency collective motion in biomacromolecules and its biological functions. Biophys Chem 30:3–48. doi:10.1016/0301-4622(88)85002-6

  53. 53.

    Lin SX, Lapointe J (2013) Theoretical and experimental biology in one. J Biomed Sci Eng 6:435–442. doi:10.4236/jbise.2013.64054

  54. 54.

    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

  55. 55.

    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

  56. 56.

    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

  57. 57.

    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

  58. 58.

    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

  59. 59.

    Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph 14:33–38. doi:10.1016/0263-7855(96)00018-5

  60. 60.

    Baum N, Schiene-Fischer C, Frost M, Schumann M, Sabapathy K, Ohlenschläger O, Grosse F, Schlott B (2009) The prolyl cis/trans isomerase cyclophilin 18 interacts with the tumor suppressor p53 and modifies its functions in cell cycle regulation and apoptosis. Oncogene 28:3915–3925. doi:10.1038/onc.2009.248

  61. 61.

    Chou KC, Watenpaugh KD, Heinrikson RL (1999) A model of the complex between cyclin-dependent kinase 5 (Cdk5) and the activation domain of neuronal Cdk5 activator. Biochem Biophys Res Commun 259:420–428. doi:10.1006/bbrc.1999.0792

  62. 62.

    Zhang J, Luan CH, Chou KC, Johnson GV (2002) Identification of the N-terminal functional domains of Cdk5 by molecular truncation and computer modeling. Proteins 48:447–453. doi:10.1002/prot.10173

  63. 63.

    Chou KC, Wei DQ, Zhong WZ (2003) Binding mechanism of coronavirus main proteinase with ligands and its implication to drug design against SARS. Biochem Biophys Res Commun 308:148–151. doi:10.1016/S0006-291X(03)01342-1

  64. 64.

    Huang RB, Du QS, Wang CH, Chou KC (2008) An in-depth analysis of the biological functional studies based on the NMR M2 channel structure of influenza A virus. Biochem Biophys Res Commun 377:1243–1247. doi:10.1016/j.bbrc.2008.10.148

  65. 65.

    Chou KC (2004) Molecular therapeutic target for type-2 diabetes. J Proteome Res 3:1284–1288. doi:10.1021/pr049849v

  66. 66.

    Li XB, Wang SQ, Xu WR, Wang RL, Chou KC (2011) Novel inhibitor design for hemagglutinin against H1N1 influenza virus by core hopping method. PLoS One 6:e28111. doi:10.1371/journal.pone.0028111

  67. 67.

    Wang JF, Chou KC (2011) Insights from modeling the 3D structure of New Delhi metallo-beta-lactamase and its binding interactions with antibiotic drugs. PLoS One 6:e18414. doi:10.1371/journal.pone.0018414

  68. 68.

    Wang JF, Chou KC (2012) Insights into the mutation-induced HHH syndrome from modeling human mitochondrial ornithine transporter-1. PLoS One 7:e31048. doi:10.1371/journal.pone.0031048

  69. 69.

    Pielak RM, Schnell JR, Chou JJ (2009) Mechanism of drug inhibition and drug resistance of influenza A M2 channel. Proc Natl Acad Sci USA 106:7379–7384. doi:10.1073/pnas.0902548106

Download references

Author information

Correspondence to G. K. Rajanikant.

Ethics declarations

Conflicts of interest

The authors declare that they have no conflict of interest.

Funding

This study was funded by the Department of Biotechnology, Government of India “Bioinformatics Infrastructure Facility for Biology Teaching through Bioinformatics (BIF-BTBI)” (Grant number: BT/BI/25/001/2006 dated 25/03/2011).

Electronic supplementary material

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Fayaz, S.M., Rajanikant, G.K. Modelling the molecular mechanism of protein–protein interactions and their inhibition: CypD–p53 case study. Mol Divers 19, 931–943 (2015) doi:10.1007/s11030-015-9612-4

Download citation

Keywords

  • Cyclophilin D
  • Cyclosporin A
  • Necroptosis
  • Neurological disorders
  • p53
  • Protein–protein interactions