Abstract
The active site conservation among protein kinases makes it a real challenge to design selective inhibitors. In this regard, detailed understanding of structural features responsible for functional behavior of different protein kinases is an important challenge in structure-based drug design. Amino acid decomposition analysis (ADA) is a powerful method to recognize and evaluate possible binding loci (hot spots) in ligand–receptor interaction. These hot spots could be used as a tool to differentiate selectivity profiles among similar protein kinases. p38α is a prominent target in the development of new anti-inflammatory agents. Type ΙΙ p38α inhibitors bind to DFG-out conformation of p38α in its inactive form. We performed a computational approach including MD simulations and ab initio method to evaluate a type ΙΙ p38α inhibitor. MD simulation was used to evaluate the binding pattern between ligand and p38α active site residues. Penetration of ligand thorough lipid bilayer was assessed by MD simulation using DPPC as a lipid bilayer model. Further conformational analysis was applied to determine induced ligand conformational instability due to binding to the receptor. ADA provided interesting results for pharmacophore discrimination. According to obtained results, residues Asp168, Leu167, Met109 and Glu71 had most contribution in binding to ligand. Conformational analysis showed that diffusion of ligand through lipid bilayer is done almost in nearly optimum structure. The obtained results could reveal some information on molecular basis of p38α inhibition, while being in good agreement with proposed pharmacophore in the literature.
Similar content being viewed by others
References
Hanks SK, Hunter T (1995) Protein kinases 6. The eukaryotic protein kinase superfamily: kinase (catalytic) domain structure and classification. FASEB J 9(8):576–596
Manning G, Whyte DB, Martinez R, Hunter T, Sudarsanam S (2002) The protein kinase complement of the human genome. Sci Signal 298(5600):1912–1934
Cohen P (2002) Protein kinases-the major drug targets of the twenty-first century? Nat Rev Drug Discov 1(4):309–315
Noble MEM, Endicott JA, Johnson LN (2004) Protein kinase inhibitors: insights into drug design from structure. Sci Signal 303(5665):1800–1805
Weinmann H, Metternich R (2005) Editorial: drug discovery process for kinease inhibitors. ChemBioChem 6(3):455–459
Taylor SS, Kim C, Vigil D, Haste NM, Yang J, Wu J, Anand GS (2005) Dynamics of signaling by PKA. Biochim Biophys Acta 1754(1):25–37
Johnson LN, Lowe ED, Noble MEM, Owen DJ (1998) The structural basis for substrate recognition and control by protein kinases. FEBS Lett 430(1–2):1–11
Knighton DR, Zheng J, Ten Eyck LF, Ashford VA, Xuong NH, Taylor SS, Sowadski JM (1991) Crystal structure of the catalytic subunit of cyclic adenosine monophosphate-dependent protein kinase. Science (New York, NY) 253(5018):407–414
Taylor SS, Knighton DR, Zheng J, Sowadski JM, Gibbs CS, Zoller MJ (1993) A template for the protein kinase family. Trends Biochem Sci 18(3):84–89
Nolen B, Taylor S, Ghosh G (2004) Regulation of protein kinases: controlling activity through activation segment conformation. Mol Cell 15(5):661–675
Goldsmith EJ, Akella R, Min X, Zhou T, Humphreys JM (2007) Substrate and docking interactions in serine/threonine protein kinases. Chem Rev 107(11):5065–5081
Fischer P (2004) The design of drug candidate molecules as selective inhibitors of therapeutically relevant protein kinases. Curr Med Chem 11(12):1563–1583
Li B, Liu Y, Uno T, Gray N (2004) Creating chemical diversity to target protein kinases. Comb Chem High Throughput Screen 7(5):453–472
Scapin G (2006) Protein kinase inhibition: different approaches to selective inhibitor design. Curr Drug Targets 7(11):1443–1454
Zhang J, Yang PL, Gray NS (2009) Targeting cancer with small molecule kinase inhibitors. Nat Rev Cancer 9(1):28–39
Kracht M, Saklatvala J (2002) Transcriptional and post-transcriptional control of gene expression in inflammation. Cytokine 20(3):91–106
Clark AR, Dean JLE, Saklatvala J (2003) Post-transcriptional regulation of gene expression by mitogen-activated protein kinase p38. FEBS Lett 546(1):37–44
Reddy AS, Pati SP, Kumar PP, Pradeep H, Sastry GN (2007) Virtual screening in drug discovery-a computational perspective. Curr Protein Pept Sci 8(4):329–351
Badrinarayan P, Narahari Sastry G (2011) Virtual high throughput screening in new lead identification. Comb Chem High Throughput Screen 14(10):840–860
Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (2012) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 64:4–17
Congreve M, Carr R, Murray C, Jhoti H (2003) A’rule of three’for fragment-based lead discovery? Drug Discov Today 8(19):876–877
Badrinarayan P, Sastry GN (2012) Virtual screening filters for the design of type II p38 MAP kinase inhibitors: a fragment based library generation approach. J Mol Graph Model 34:89–100
Hensen C, Hermann JC, Nam K, Ma S, Gao J, Höltje HD (2004) A combined QM/MM approach to protein–ligand interactions: polarization effects of the HIV-1 protease on selected high affinity inhibitors. J Med Chem 47(27):6673–6680
Thangapandian S, John S, Lee KW (2012) Molecular dynamics simulation study explaining inhibitor selectivity in different class of histone deacetylases. J Biomol Struct Dyn 29(4):677–698
de Brito MA, Rodrigues CR, Cirino JJV, Araújo JQ, Honório T, Cabral LM, de Alencastro RB, Castro HC, Albuquerque MG (2012) Residue–ligand interaction energy (ReLIE) on a receptor-dependent 3D-QSAR analysis of S-and NH-DABOs as non-nucleoside reverse transcriptase inhibitors. Molecules 17(7):7666–7694
Breneman CM, Wiberg KB (1990) Determining atom-centered monopoles from molecular electrostatic potentials. The need for high sampling density in formamide conformational analysis. J Comput Chem 11(3):361–373
Angell RM, Angell TD, Bamborough P, Bamford MJ, Chung C, Cockerill SG, Flack SS, Jones KL, Laine DI, Longstaff T (2008) Biphenyl amide p38 kinase inhibitors 4: DFG-in and DFG-out binding modes. Bioorg Med Chem Lett 18(15):4433–4437
Wallace AC, Laskowski RA, Thornton JM (1995) LIGPLOT: a program to generate schematic diagrams of protein–ligand interactions. Protein Eng 8(2):127–134
Aalten DMF, Bywater R, Findlay J, Hendlich M, Hooft R, 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(3):255–262
Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph 14(1):33–38
Berger O, Edholm O, Jähnig F (1997) Molecular dynamics simulations of a fluid bilayer of dipalmitoylphosphatidylcholine at full hydration, constant pressure, and constant temperature. Biophys J 72(5):2002–2013
Tieleman D, Berendsen H (1996) Molecular dynamics simulations of a fully hydrated dipalmitoylphosphatidylcholine bilayer with different macroscopic boundary conditions and parameters. J Chem Phys 105:4871–4880
Van Der Spoel D, Lindahl E, Hess B, Groenhof G, Mark AE, Berendsen HJC (2005) GROMACS: fast, flexible, and free. J Comput Chem 26(16):1701–1718
Rivail L, Chipot C, Maigret B, Bestel I, Sicsic S, Tarek M (2007) Large-scale molecular dynamics of a G protein-coupled receptor, the human 5-HT4 serotonin receptor, in a lipid bilayer. J Mol Struct (Thoechem) 817(1):19–26
Bussi G, Donadio D, Parrinello M (2008) Canonical sampling through velocity-rescaling. arXiv preprint arXiv:08034060
Berendsen HJC, Postma JPM, van Gunsteren WF, DiNola A, Haak J (1984) Molecular dynamics with coupling to an external bath. J Chem Phys 81(8):3684–3690
Hess B, Bekker H, Berendsen HJC, Fraaije JGEM (1997) LINCS: a linear constraint solver for molecular simulations. J Comput Chem 18(12):1463–1472
Klamt A, Schüürmann G (1993) COSMO: a new approach to dielectric screening in solvents with explicit expressions for the screening energy and its gradient. J Chem Soc Perkin Trans 2(5):799–805
Neese F (2003) An improvement of the resolution of the identity approximation for the formation of the Coulomb matrix. J Comput Chem 24(14):1740–1747
Eichkorn K, Treutler O, Öhm H, Häser M, Ahlrichs R (1995) Auxiliary basis sets to approximate Coulomb potentials. Chem Phys Lett 240(4):283–290
Neese F, Wennmohs F, Hansen A, Becker U (2009) Efficient, approximate and parallel Hartree–Fock and hybrid DFT calculations. A ‘chain-of-spheres’ algorithm for the Hartree–Fock exchange. Chem Phys 356(1):98–109
Neese F (2011) ORCA—an ab initio, density functional and semiempirical program package, Version 2.8.0 University of Bonn
Pargellis C, Tong L, Churchill L, Cirillo PF, Gilmore T, Graham AG, Grob PM, Hickey ER, Moss N, Pav S (2002) Inhibition of p38 MAP kinase by utilizing a novel allosteric binding site. Nat Struct Mol Biol 9(4):268–272
Wagner G, Laufer S (2006) Small molecular anti cytokine agents. Med Res Rev 26(1):1–62
Acknowledgments
Financial supports of this project by research council of Hamadan University of Medical Sciences are acknowledged.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ebadi, S.A., Razzaghi-Asl, N., Khoshneviszadeh, M. et al. Detailed atomistic molecular modeling of a potent type ΙΙ p38α inhibitor. Struct Chem 26, 1125–1137 (2015). https://doi.org/10.1007/s11224-015-0568-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11224-015-0568-x