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Visualizing protein–ligand binding with chemical energy-wise decomposition (CHEWD): application to ligand binding in the kallikrein-8 S1 Site

  • Saad Raza
  • Kara E. Ranaghan
  • Marc W. van der Kamp
  • Christopher J. Woods
  • Adrian J. MulhollandEmail author
  • Syed Sikander AzamEmail author
Article
  • 46 Downloads

Abstract

Kallikrein-8, a serine protease, is a target for structure-based drug design due to its therapeutic potential in treating Alzheimer’s disease and is also useful as a biomarker in ovarian cancer. We present a binding assessment of ligands to kallikrein-8 using a residue-wise decomposition of the binding energy. Binding of four putative inhibitors of kallikrein-8 is investigated through molecular dynamics simulation and ligand binding energy evaluation with two methods (MM/PBSA and WaterSwap). For visualization of the residue-wise decomposition of binding energies, chemical energy-wise decomposition or CHEWD is introduced as a plugin to UCSF Chimera and Pymol. CHEWD allows easy comparison between ligands using individual residue contributions to the binding energy. Molecular dynamics simulations indicate one ligand binds stably to the kallikrein-8 S1 binding site. Comparison with other members of the kallikrein family shows that residues responsible for binding are specific to kallikrein-8. Thus, ZINC02927490 is a promising lead for development of novel kallikrein-8 inhibitors.

Keywords

Kallikrein 8 Molecular dynamics simulation Binding energy WaterSwap Chemical energy-wise decomposition 

Notes

Acknowledgement

SSA and SR would like to acknowledge International Science Foundation (IFS) for the grant IFS-5546, and Higher Education Commission (HEC), Pakistan for their financial assistance and IRSIP funding. CW would like to thank the EPSRC for funding via an EPSRC RSE Fellowship (EP/N018591/1). MWvdK is a BBSRC David Phillips Fellow and thanks BBSRC for funding (BB/M026280/1). AJM would like to thank EPSRC (Grant No. EP/M022609/1, CCP-BioSim (ccpbiosim.ac.uk)) and with KER thanks the BBSRC for funding (Grant No. BB/M000354/1). This work was carried out using the computational facilities of the Advanced Computing Research Centre at the University of Bristol http://www.bris.ac.uk/acrc.

Supplementary material

10822_2019_200_MOESM1_ESM.docx (2.7 mb)
Supplementary material 1 (DOCX 2777 kb)

References

  1. 1.
    Anderson AC (2003) The process of structure-based drug design. Chem Biol 10:787–797CrossRefGoogle Scholar
  2. 2.
    Connor MO, Deeks HM, Dawn E et al (2018) Sampling molecular conformations and dynamics in a multi-user virtual reality framework. Sci Adv 4(6):eaat2731CrossRefGoogle Scholar
  3. 3.
    Jorgensen WL (2009) Efficient drug lead discovery and optimization. Acc Chem Res 42:724–733CrossRefGoogle Scholar
  4. 4.
    Amaro RE, Mulholland AJ (2018) Multiscale methods in drug design bridge chemical and biological complexity in the search for cures. Nat Rev Chem 2:148CrossRefGoogle Scholar
  5. 5.
    Huggins DJ, Biggin PC, Dämgen MA et al (2018) Biomolecular simulations: from dynamics and mechanisms to computational assays of biological activity. Wiley Interdiscip Rev Comput Mol Sci.  https://doi.org/10.1002/wcms.1393 Google Scholar
  6. 6.
    Slynko I, Schmidtkunz K, Rumpf T et al (2016) Identification of highly potent protein kinase C-related kinase 1 inhibitors by virtual screening, binding free energy rescoring, and in vitro testing. ChemMedChem 11:2084–2094CrossRefGoogle Scholar
  7. 7.
    Grüneberg S, Stubbs MT, Klebe G (2002) Successful virtual screening for novel inhibitors of human carbonic anhydrase: strategy and experimental confirmation. J Med Chem 45:3588–3602CrossRefGoogle Scholar
  8. 8.
    Hain AUP, Miller AS, Levitskaya J, Bosch J (2016) Virtual screening and experimental validation identify novel inhibitors of the Plasmodium falciparum Atg8–Atg3 protein–protein interaction. ChemMedChem 11:900–910CrossRefGoogle Scholar
  9. 9.
    Michel J, Essex JW (2010) Prediction of protein–ligand binding affinity by free energy simulations: assumptions, pitfalls and expectations. J Comput Aided Mol Des 24:639–658CrossRefGoogle Scholar
  10. 10.
    Baron R, McCammon JA (2013) Molecular recognition and ligand association. Annu Rev Phys Chem 64:151–175CrossRefGoogle Scholar
  11. 11.
    Calabro G, Woods CJ, Powlesland F et al (2016) Elucidation of non-additive effects in protein-ligand binding energies: thrombin as a case study. J. Phys. Chem. B 120:5340–5350CrossRefGoogle Scholar
  12. 12.
    Zhao H, Caflisch A (2015) Molecular dynamics in drug design. Eur J Med Chem 91:4–14CrossRefGoogle Scholar
  13. 13.
    Ge Y, van der Kamp M, Malaisree M et al (2017) Identification of the quinolinedione inhibitor binding site in Cdc25 phosphatase B through docking and molecular dynamics simulations. J Comput Aided Mol Des 31:995–1007CrossRefGoogle Scholar
  14. 14.
    Ahmad S, Raza S, Uddin R, Azam SS (2017) Binding mode analysis, dynamic simulation and binding free energy calculations of the MurF ligase from Acinetobacter baumannii. J Mol Graph Model 77:72–85CrossRefGoogle Scholar
  15. 15.
    Ahmad S, Raza S, Abbasi SW, Azam SS (2018) Identification of natural inhibitors against Acinetobacter baumanniid-alanine-d-alanine ligase enzyme: a multi-spectrum in silico approach. J Mol Liq 262:460–475CrossRefGoogle Scholar
  16. 16.
    Kollman PA, Massova I, Reyes C et al (2000) Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. Acc Chem Res 33:889–897CrossRefGoogle Scholar
  17. 17.
    Genheden S, Ryde U (2015) The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin Drug Discov 10:449–461CrossRefGoogle Scholar
  18. 18.
    Woods CJ, Malaisree M, Hannongbua S, Mulholland AJ (2011) A water-swap reaction coordinate for the calculation of absolute protein–ligand binding free energies. J Chem Phys 134:54114CrossRefGoogle Scholar
  19. 19.
    Pettersen EF, Goddard TD, Huang CC et al (2004) UCSF Chimera—a visualization system for exploratory research and analysis. J Comput Chem 25:1605–1612CrossRefGoogle Scholar
  20. 20.
    DeLano WL (2002) PyMOLGoogle Scholar
  21. 21.
    Kraut H, Frey EK, Werle E (1930) Der Nachweis eines Kreislaufhormons in der Pankreasdrüse. (IV. Mitteilung über dieses Kreislaufhormon.). Hoppe Seyler’s Z für Physiol Chem 189:97–106CrossRefGoogle Scholar
  22. 22.
    Eissa A, Amodeo V, Smith CR, Diamandis EP (2011) Kallikrein-related peptidase-8 (KLK8) is an active serine protease in human epidermis and sweat and is involved in a skin barrier proteolytic cascade. J Biol Chem 286:687–706CrossRefGoogle Scholar
  23. 23.
    Shaw JLV, Diamandis EP (2007) Distribution of 15 human kallikreins in tissues and biological fluids. Clin Chem 53:1423–1432CrossRefGoogle Scholar
  24. 24.
    Oka T, Akisada M, Okabe A et al (2002) Extracellular serine protease neuropsin (KLK8) modulates neurite outgrowth and fasciculation of mouse hippocampal neurons in culture. Neurosci Lett 321:141–144CrossRefGoogle Scholar
  25. 25.
    Kuwae K, Matsumoto-Miyai K, Yoshida S et al (2002) Epidermal expression of serine protease, neuropsin (KLK8) in normal and pathological skin samples. Mol Pathol 55:235–241CrossRefGoogle Scholar
  26. 26.
    Borgoño CA, Kishi T, Scorilas A et al (2006) Human kallikrein 8 protein is a favorable prognostic marker in ovarian cancer. Clin Cancer Res 12:1487–1493CrossRefGoogle Scholar
  27. 27.
    Komatsu N, Saijoh K, Kuk C et al (2007) Aberrant human tissue kallikrein levels in the stratum corneum and serum of patients with psoriasis: dependence on phenotype, severity and therapy. Br J Dermatol 156:875–883CrossRefGoogle Scholar
  28. 28.
    Herring A, Münster Y, Akkaya T et al (2016) Kallikrein-8 inhibition attenuates Alzheimer’s disease pathology in mice. Alzheimer’s Dement 12:1273–1287CrossRefGoogle Scholar
  29. 29.
    Prassas I, Eissa A, Poda G, Diamandis EP (2015) Unleashing the therapeutic potential of human kallikrein-related serine proteases. Nat Rev Drug Discov 14:183CrossRefGoogle Scholar
  30. 30.
    De Vita E, Schüler P, Lovell S et al (2018) Depsipeptides featuring a neutral P1 are potent inhibitors of kallikrein-related peptidase 6 with on-target cellular activity. J Med Chem 61(8859):8874Google Scholar
  31. 31.
    Tatsuta K, Mikami N, Fujimoto K et al (1973) The structure of chymostatin, a chymotrypsin inhibitor. J Antibiot (Tokyo) 26:625–646CrossRefGoogle Scholar
  32. 32.
    Azam SS, Raza S (2014) Structure modeling and hybrid virtual screening study of Alzheimer’s associated protease kallikrein 8 for the identification of novel inhibitors. Med Chem Res 23:3516–3527CrossRefGoogle Scholar
  33. 33.
    Case DA, Darden T, Iii TEC, et al (2014) Amber 14. Univ California, San Fr.  https://doi.org/10.1007/s13398-014-0173-7.2
  34. 34.
    Maier JA, Martinez C, Kasavajhala K et al (2015) ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB. J Chem Theory Comput 11:3696–3713CrossRefGoogle Scholar
  35. 35.
    Wang J, Wolf RM, Caldwell JW et al (2004) Development and testing of a general amber force field. J Comput Chem 25:1157–1174CrossRefGoogle Scholar
  36. 36.
    Jorgensen WL, Chandrasekhar J, Madura JD et al (1983) Comparison of simple potential functions for simulating liquid water. J Chem Phys 79:926–935CrossRefGoogle Scholar
  37. 37.
    Ryckaert J-P, Ciccotti G, Berendsen HJC (1977) Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J Comput Phys 23:327–341CrossRefGoogle Scholar
  38. 38.
    Pastor RW, Brooks BR, Szabo A (1988) An analysis of the accuracy of Langevin and molecular dynamics algorithms. Mol Phys 65:1409–1419CrossRefGoogle Scholar
  39. 39.
    Andersen HC (1980) Molecular dynamics simulations at constant pressure and/or temperature. J Chem Phys 72:2384–2393CrossRefGoogle Scholar
  40. 40.
    Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph 14:33–38CrossRefGoogle Scholar
  41. 41.
    Watkins DW, Jenkins JMX, Grayson KJ et al (2017) Construction and in vivo assembly of a catalytically proficient and hyperthermostable de novo enzyme. Nat Commun 8:358CrossRefGoogle Scholar
  42. 42.
    Woods CJ, Michel JM (2016) Sire: an advanced, multiscale, molecular simulation frameworkGoogle Scholar
  43. 43.
    Ahmad S, Raza S, Abro A et al (2018) Toward novel inhibitors against KdsB: a highly specific and selective broad-spectrum bacterial enzyme. J Biomol Struct Dyn 37:1326–1345CrossRefGoogle Scholar
  44. 44.
    Rinaldi S, van der Kamp M, Ranaghan KE et al (2018) Understanding complex mechanisms of enzyme reactivity: the case of Limonene-1, 2-epoxide hydrolases. ACS Catal 8(7):5698–5707CrossRefGoogle Scholar
  45. 45.
    Ester M, Kriegel H-P, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. Kdd 96:226–231Google Scholar
  46. 46.
    Zwanzig RW (1954) High-temperature equation of state by a perturbation method. I. nonpolar gases. J Chem Phys 22:1420–1426CrossRefGoogle Scholar
  47. 47.
    Oostenbrink BC, Pitera JW, van Lipzig MMH et al (2000) Simulations of the estrogen receptor ligand-binding domain: affinity of natural ligands and xenoestrogens. J Med Chem 43:4594–4605CrossRefGoogle Scholar
  48. 48.
    Bennett CH (1976) Efficient estimation of free energy differences from Monte Carlo data. J Comput Phys 22:245–268CrossRefGoogle Scholar
  49. 49.
    Visualizer DS (2012) Release 3.5. Accelrys Inc, San Diego, CA, USAGoogle Scholar
  50. 50.
    Ranaghan KE, Masgrau L, Scrutton NS et al (2007) Analysis of classical and quantum paths for deprotonation of methylamine by methylamine dehydrogenase. ChemPhysChem 8:1816–1835CrossRefGoogle Scholar
  51. 51.
    Miller BR III, McGee TD Jr, Swails JM et al (2012) MMPBSA. py: an efficient program for end-state free energy calculations. J Chem Theory Comput 8:3314–3321CrossRefGoogle Scholar
  52. 52.
    Woods CJ, Malaisree M, Michel J et al (2014) Rapid decomposition and visualisation of protein–ligand binding free energies by residue and by water. Faraday Discuss 169:477–499CrossRefGoogle Scholar
  53. 53.
    Irwin JJ, Shoichet BK (2005) ZINC- a free database of commercially available compounds for virtual screening. J Chem Inf Model 45:177–182CrossRefGoogle Scholar
  54. 54.
    Hedstrom L (2002) Serine protease mechanism and specificity. Chem Rev 102:4501–4524CrossRefGoogle Scholar
  55. 55.
    Lobanov MY, Bogatyreva NS, Galzitskaya OV (2008) Radius of gyration as an indicator of protein structure compactness. Mol Biol 42:623–628CrossRefGoogle Scholar
  56. 56.
    Daze K, Hof F (2016) Molecular interaction and recognition. In: Wang Z (ed) Encyclopedia of physical organic chemistry. Wiley, Hoboken, pp 1–51Google Scholar
  57. 57.
    Debela M, Magdolen V, Skala W et al (2018) Structural determinants of specificity and regulation of activity in the allosteric loop network of human KLK8/neuropsin. Sci Rep 8:10705CrossRefGoogle Scholar
  58. 58.
    Pavlopoulou A, Pampalakis G, Michalopoulos I, Sotiropoulou G (2010) Evolutionary history of tissue kallikreins. PLoS ONE 5:e13781CrossRefGoogle Scholar
  59. 59.
    Evers A, Klabunde T (2005) Structure-based drug discovery using GPCR homology modeling: successful virtual screening for antagonists of the alpha1A adrenergic receptor. J Med Chem 48:1088–1097CrossRefGoogle Scholar
  60. 60.
    Wang J, Morin P, Wang W, Kollman PA (2001) Use of MM-PBSA in reproducing the binding free energies to HIV-1 RT of TIBO derivatives and predicting the binding mode to HIV-1 RT of efavirenz by docking and MM-PBSA. J Am Chem Soc 123:5221–5230CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Saad Raza
    • 1
    • 2
  • Kara E. Ranaghan
    • 2
  • Marc W. van der Kamp
    • 2
    • 3
    • 4
  • Christopher J. Woods
    • 2
    • 4
  • Adrian J. Mulholland
    • 2
    • 4
    Email author
  • Syed Sikander Azam
    • 1
    Email author
  1. 1.Computational Biology Lab, National Center for BioinformaticsQuaid-i-Azam University IslamabadIslamabadPakistan
  2. 2.Centre for Computational Chemistry, School of ChemistryUniversity of BristolBristolUK
  3. 3.School of Biochemistry, Biomedical Sciences BuildingUniversity of BristolBristolUK
  4. 4.BrisSynBio Synthetic Biology Research Centre, Life Sciences BuildingUniversity of BristolBristolUK

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