In Silico Pharmacology

, 6:1 | Cite as

In silico quest of selective naphthyl-based CREBBP bromodomain inhibitor

  • Raju Dash
  • Sarmistha Mitra
  • Md. Arifuzzaman
  • S. M. Zahid Hosen
Original Research


The reader proteins like bromodomains have recently gained increased attentions in the area of epigenetic drug discovery, as they are the potent regulators in gene transcription process. Among the other bromodomains, cAMP response element-binding protein (CREB) binding protein or CREBBP bomodomain involved in various cancer progressions and therefore, efforts to develop specific inhibitors of CREBBP bomodomain are of clinical value. In this study, we tried to identify selective CREBBP bromodomain inhibitor, which was accomplished by using molecular docking, free energy calculation and molecular dynamics (MD) simulation studies, considering a series of naphthyl based compounds. The docking procedure was validated by comparing root mean square deviations (RMSDs) of crystallographic complex to docked complex. Favorable electrostatic interactions with the Arg1173 side chain were considered to attain selectivity for CREBBP bromodomain over other human bromodomain subfamilies. We found that naphthyl-based compounds have greater binding affinities towards the CREBBP bromodomain, and formed non-bonded interactions with various side chain residues that are important for bromodomain inhibition. From detailed investigation by induced fit docking, compound 31 was found to have favorable electrostatic interactions with the Arg1173 side chain by forming conventional hydrogen bonds. This result was further confirmed by analyzing hydrogen bond occupancy and bonding distance during the molecular dynamics simulation. We believe that these findings offer useful insight for the designing of target specific new bromodomain inhibitor and also promote further structure guided synthesis of analogues for identification of potent CREBBP bromodomain inhibitors as well as detailed in vitro and in vivo analyses.


Bromodomain CREBBP Selectivity In silico 



Bromo and extra terminal


Cyclic adenosine monophosphate


Cyclic-AMP response element-binding protein


Chemistry at HARvard macromolecular mechanics


cAMP response element-binding brotein (CREB) binding protein


Molecular mechanics energies


Adenoviral E1A binding protein of 300 kDa


Non-polar solvation (GNP)


Solvation model for polar solvation


High throughout virtual screening


Kilocalorie per mol


Molecular dynamics


Molecular mechanics


Molecular mechanics-generalised born and surface area


Nanoscale molecular dynamics


Particle mesh Ewald


Radius of gyration


Root mean square deviation


Root mean square fluctuation


Structure activity relationship


Surface generalized born


The transferable intermolecular potential3 points


Extra precision


Author contributions

RD and SMZH conceived and designed the study. RD and SM preformed the experiments, prepared the figures, and wrote the initial draft. MA and SMZH analyzed the data, guided and supported in the preparation of manuscript. All authors have read and approved the final manuscript.

Compliance with ethical standards

Conflict of interest

All authors declare that they have no competing interests.

Supplementary material

40203_2018_38_MOESM1_ESM.doc (1.8 mb)
Supplementary material 1 (DOC 1893 kb)


  1. Aparna V, Dineshkumar K, Mohanalakshmi N, Velmurugan D, Hopper W (2014) Identification of natural compound inhibitors for multidrug efflux pumps of Escherichia coli and Pseudomonas aeruginosa using in silico high-throughput virtual screening and in vitro validation. PLoS One 9:e101840CrossRefPubMedPubMedCentralGoogle Scholar
  2. Berman HM et al (2000) The protein data bank. Nucleic Acids Res 28:235–242CrossRefPubMedPubMedCentralGoogle Scholar
  3. Conery AR et al (2016) Bromodomain inhibition of the transcriptional coactivators CBP/EP300 as a therapeutic strategy to target the IRF4 network in multiple myeloma. Elife 5:e10483CrossRefPubMedPubMedCentralGoogle Scholar
  4. Das C, Lucia MS, Hansen KC, Tyler JK (2009) CBP/p300-mediated acetylation of histone H3 on lysine 56. Nature 459:113–117CrossRefPubMedPubMedCentralGoogle Scholar
  5. Dash R et al (2015) In silico analysis of indole-3-carbinol and its metabolite DIM as EGFR tyrosine kinase inhibitors in platinum resistant ovarian cancer vis a vis ADME/T property analysis. J App Pharm Sci 5(11):073–078CrossRefGoogle Scholar
  6. Doman TN et al (2002) Molecular docking and high-throughput screening for novel inhibitors of protein tyrosine phosphatase-1B. J Med Chem 45:2213–2221CrossRefPubMedGoogle Scholar
  7. Filippakopoulos P, Knapp S (2014) Targeting bromodomains: epigenetic readers of lysine acetylation. Nat Rev Drug Discov 13:337–356CrossRefPubMedGoogle Scholar
  8. Florence B, Faller DV (2001) You bet-cha: a novel family of transcriptional regulators. Front Biosci 6:D1008–D1018PubMedGoogle Scholar
  9. Friesner RA et al (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47:1739–1749CrossRefPubMedGoogle Scholar
  10. Friesner RA et al (2006) Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein–ligand complexes. J Med Chem 49:6177–6196CrossRefPubMedPubMedCentralGoogle Scholar
  11. Giles RH, Peters DJ, Breuning MH (1998) Conjunction dysfunction: CBP/p300 in human disease. Trends Genet 14:178–183CrossRefPubMedGoogle Scholar
  12. Goodman RH, Smolik S (2000) CBP/p300 in cell growth, transformation, and development. Genes Dev 14:1553–1577PubMedGoogle Scholar
  13. Hammitzsch A et al (2015) CBP30, a selective CBP/p300 bromodomain inhibitor, suppresses human Th17 responses. Proc Natl Acad Sci 112:10768–10773CrossRefPubMedGoogle Scholar
  14. Harmange J-C et al (2008) Naphthamides as novel and potent vascular endothelial growth factor receptor tyrosine kinase inhibitors: design, synthesis, and evaluation. J Med Chem 51:1649–1667CrossRefPubMedGoogle Scholar
  15. Hay DA et al (2014) Discovery and optimization of small-molecule ligands for the CBP/p300 bromodomains. J Am Chem Soc 136:9308–9319CrossRefPubMedPubMedCentralGoogle Scholar
  16. Hewings DS et al (2011) 3, 5-dimethylisoxazoles act as acetyl-lysine-mimetic bromodomain ligands. J Med Chem 54:6761–6770CrossRefPubMedPubMedCentralGoogle Scholar
  17. Hou T, Wang J, Li Y, Wang W (2010) Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations. J Chem Inf Model 51:69–82CrossRefPubMedPubMedCentralGoogle Scholar
  18. Hu X, Hong L, Smith MD, Neusius T, Cheng X, Smith JC (2015) The dynamics of single protein molecules is non-equilibrium and self-similar over thirteen decades in time. Nat Phys 12:171–174CrossRefGoogle Scholar
  19. Ito A, Lai CH, Zhao X, Si Saito, Hamilton MH, Appella E, Yao TP (2001) p300/CBP-mediated p53 acetylation is commonly induced by p53-activating agents and inhibited by MDM2. EMBO J 20:1331–1340CrossRefPubMedPubMedCentralGoogle Scholar
  20. Jacobson RH, Ladurner AG, King DS, Tjian R (2000) Structure and function of a human TAFII250 double bromodomain module. Science 288:1422–1425CrossRefPubMedGoogle Scholar
  21. Jatana N, Sharma A, Latha N (2013) Pharmacophore modeling and virtual screening studies to design potential COMT inhibitors as new leads. J Mol Gr Model 39:145–164CrossRefGoogle Scholar
  22. Jin Q et al (2011) Distinct roles of GCN5/PCAF-mediated H3K9ac and CBP/p300-mediated H3K18/27ac in nuclear receptor transactivation. EMBO J 30:249–262CrossRefPubMedGoogle Scholar
  23. Kuhn B, Gerber P, Schulz-Gasch T, Stahl M (2005) Validation and use of the MM-PBSA approach for drug discovery. J Med Chem 48:4040–4048CrossRefPubMedGoogle Scholar
  24. Labute P (2008) The generalized Born/volume integral implicit solvent model: estimation of the free energy of hydration using London dispersion instead of atomic surface area. J Comput Chem 29:1693–1698CrossRefPubMedGoogle Scholar
  25. Li M et al (2011a) High expression of transcriptional coactivator p300 correlates with aggressive features and poor prognosis of hepatocellular carcinoma. J Transl Med 9:1CrossRefPubMedGoogle Scholar
  26. Li Y, Yang H-X, Luo R-Z, Zhang Y, Li M, Wang X, Jia W-H (2011b) High expression of p300 has an unfavorable impact on survival in resectable esophageal squamous cell carcinoma. Ann Thorac Surg 91:1531–1538CrossRefPubMedGoogle Scholar
  27. Mujtaba S et al (2004) Structural mechanism of the bromodomain of the coactivator CBP in p53 transcriptional activation. Mol Cell 13:251–263CrossRefPubMedGoogle Scholar
  28. Müller S, Knapp S (2014) Discovery of BET bromodomain inhibitors and their role in target validation. MedChemComm 5:288–296CrossRefGoogle Scholar
  29. Muller S, Filippakopoulos P, Knapp S (2011) Bromodomains as therapeutic targets. Expert Rev Mol Med 13:e29CrossRefPubMedPubMedCentralGoogle Scholar
  30. Phillips JC et al (2005) Scalable molecular dynamics with NAMD. J Comput Chem 26:1781–1802. CrossRefPubMedPubMedCentralGoogle Scholar
  31. Picaud S et al (2015) Generation of a selective small molecule inhibitor of the CBP/p300 bromodomain for leukemia therapy. Can Res 75:5106–5119CrossRefGoogle Scholar
  32. Poplawski A et al (2014) Molecular insights into the recognition of N-terminal histone modifications by the BRPF1 bromodomain. J Mol Biol 426:1661–1676. CrossRefPubMedGoogle Scholar
  33. Priya Doss CG, Chakraborty C, Chen L, Zhu H (2014) Integrating in silico prediction methods, molecular docking, and molecular dynamics simulation to predict the impact of ALK missense mutations in structural perspective. BioMed Res Int 2014:895831CrossRefPubMedCentralGoogle Scholar
  34. Rastelli G, Rio AD, Degliesposti G, Sgobba M (2010) Fast and accurate predictions of binding free energies using MM-PBSA and MM-GBSA. J Comput Chem 31:797–810PubMedGoogle Scholar
  35. Romero FA, Taylor AM, Crawford TD, Tsui V, Côté A, Magnuson S (2015) Disrupting acetyl-lysine recognition: progress in the development of bromodomain inhibitors. J Med Chem 59:1271–1298CrossRefPubMedGoogle Scholar
  36. Rooney TP et al (2014) A series of potent CREBBP bromodomain ligands reveals an induced-fit pocket stabilized by a cation–π interaction. Angew Chem Int Ed 53:6126–6130CrossRefGoogle Scholar
  37. Schrödinger S (2012) Induced fit docking protocol; glide version 5.8, prime version 3.1. Schrödinger LLC, New YorkGoogle Scholar
  38. Strahl BD, Allis CD (2000) The language of covalent histone modifications. Nature 403:41–45CrossRefPubMedGoogle Scholar
  39. Talele TT, McLaughlin ML (2008) Molecular docking/dynamics studies of Aurora A kinase inhibitors. J Mol Gr Model 26:1213–1222CrossRefGoogle Scholar
  40. Tanwar H, Sneha P, Kumar DT, Siva R, Walter CEJ, Doss CGP (2017) Chapter five-A computational approach to identify the biophysical and structural aspects of methylenetetrahydrofolate reductase (MTHFR) mutations (A222V, E429A, and R594Q) leading to schizophrenia. Adv Protein Chem Struct Biol 108:105–125CrossRefPubMedGoogle Scholar
  41. Taylor AM et al (2016) Fragment-based discovery of a selective and cell-active benzodiazepinone CBP/EP300 bromodomain inhibitor (CPI-637). ACS Med Chem Lett 7(5):531–536CrossRefPubMedPubMedCentralGoogle Scholar
  42. Unzue A, Xu M, Dong J, Wiedmer L, Spiliotopoulos D, Caflisch A, Nevado C (2015) Fragment-based design of selective nanomolar ligands of the CREBBP bromodomain. J Med Chem 59:1350–1356CrossRefPubMedGoogle Scholar
  43. Unzue A et al (2016) The “Gatekeeper” residue influences the mode of binding of acetyl indoles to bromodomains. J Med Chem 59:3087–3097CrossRefPubMedGoogle Scholar
  44. Vanommeslaeghe K et al (2010) CHARMM general force field (CGenFF): a force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. J Comput Chem 31:671–690. CrossRefPubMedPubMedCentralGoogle Scholar
  45. Vidler LR, Brown N, Knapp S, Hoelder S (2012) Druggability analysis and structural classification of bromodomain acetyl-lysine binding sites. J Med Chem 55:7346–7359CrossRefPubMedPubMedCentralGoogle Scholar
  46. Weis A, Katebzadeh K, Söderhjelm P, Nilsson I, Ryde U (2006) Ligand affinities predicted with the MM/PBSA method: dependence on the simulation method and the force field. J Med Chem 49:6596–6606CrossRefPubMedGoogle Scholar
  47. Wichapong K, Rohe A, Platzer C, Slynko I, Erdmann F, Schmidt M, Sippl W (2014) Application of docking and QM/MM-GBSA rescoring to screen for novel Myt1 kinase inhibitors. J Chem Inf Model 54:881–893CrossRefPubMedGoogle Scholar
  48. Xu M, Unzue A, Dong J, Spiliotopoulos D, Nevado C, Caflisch A (2015) Discovery of CREBBP bromodomain inhibitors by high-throughput docking and hit optimization guided by molecular dynamics. J Med Chem 59:1340–1349CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Raju Dash
    • 1
    • 2
  • Sarmistha Mitra
    • 3
  • Md. Arifuzzaman
    • 2
  • S. M. Zahid Hosen
    • 1
  1. 1.Molecular Modeling and Drug Design Laboratory, Pharmacology Research DivisionBangladesh Council of Scientific and Industrial ResearchChittagongBangladesh
  2. 2.Department of Biochemistry and BiotechnologyUniversity of Science and TechnologyChittagongBangladesh
  3. 3.Department of PharmacyUniversity of ChittagongChittagongBangladesh

Personalised recommendations