Role of water in the determination of protonation states of titratable residues

Abstract

Water is the fundamental unit for living being, and its contribution in variety of crucial cellular functions is widely accepted. The presence of water molecules in protein’s environment also accounts for structural optimization, in which highly conserved water molecules ensure structural stability of the biomolecule by providing protein-water (solute-solvent) hydrogen-bond interaction networks. Similarly, protonation states and pKa values of individual amino acid residues are also influenced by neighboring water molecules present in the protein’s vicinity. In the present study, we have highlighted the role of water molecules in hydrogen-bond optimization, in determining pKa values and protonation states of titratable residues in JH2 domain of JAK2 apo protein. We found that inclusion or exclusion of water molecules while calculating pKa and assigning protonation states to amino acid residues during the molecular system build-up step resulted in slight differences in pKa values of few titratable residues and alternative protonation states of a certain residue. Accordingly, different protonation states of ionizable residues offer differing interaction patterns. Thus, we inferred that the presence of water optimizes the hydrogen-bond interactions by forming direct protein-water interactions and by linking via protein-protein bridging interactions. However, in the absence of water, the interaction pattern is somewhat disrupted. We assume that water molecules could modulate the plausibility of a particular protonation state of titratable residues on the basis of its fit with the local environment, by utilizing some particular hydrogen-bond contacts that would remain unexploited in the absence of water.

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References

  1. 1.

    Durrant JD, McCammon JA (2011) Molecular dynamics simulations and drug discovery. BMC Biol 9:71. https://doi.org/10.1186/1741-7007-9-71

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Lemkul JA, Huang J, Roux B, Mackerell AD (2016) An empirical polarizable force field based on the classical Drude oscillator model: development history and recent applications. Chem Rev 116:4983–5013. https://doi.org/10.1021/acs.chemrev.5b00505

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Kirby BJ, Jungwirth P (2019) Charge scaling manifesto: a way of reconciling the inherently macroscopic and microscopic natures of molecular simulations. J Phys Chem Lett 10:7531–7536. https://doi.org/10.1021/acs.jpclett.9b02652

    CAS  Article  PubMed  Google Scholar 

  4. 4.

    Dror RO, Dirks RM, Grossman JP et al (2012) Biomolecular simulation: a computational microscope for molecular biology. Annu Rev Biophys 41:429–452. https://doi.org/10.1146/annurev-biophys-042910-155245

    CAS  Article  PubMed  Google Scholar 

  5. 5.

    Van Duin ACT, Dasgupta S, Lorant F, Goddard WA (2001) ReaxFF: a reactive force field for hydrocarbons. J Phys Chem A 105:9396–9409. https://doi.org/10.1021/jp004368u

    CAS  Article  Google Scholar 

  6. 6.

    Lepšík M, Řezáč J, Kolář M et al (2013) The semiempirical quantum mechanical scoring function for in silico drug design. Chempluschem 78:921–931

    Article  Google Scholar 

  7. 7.

    Mongan J, Case DA (2005) Biomolecular simulations at constant pH. Curr Opin Struct Biol 15:157–163

    CAS  Article  Google Scholar 

  8. 8.

    Stern HA (2007) Molecular simulation with variable protonation states at constant pH. J Chem Phys 126:164112. https://doi.org/10.1063/1.2731781

    CAS  Article  PubMed  Google Scholar 

  9. 9.

    Radak BK, Chipot C, Suh D et al (2017) Constant-pH molecular dynamics simulations for large biomolecular systems. J Chem Theory Comput 13:5933–5944. https://doi.org/10.1021/acs.jctc.7b00875

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Donnini S, Tegeler F, Groenhof G, Grubmüller H (2011) Constant pH molecular dynamics in explicit solvent with λ-dynamics. J Chem Theory Comput 7:1962–1978. https://doi.org/10.1021/ct200061r

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Hollingsworth SA, Dror RO (2018) Molecular dynamics simulation for all. Neuron 99:1129–1143. https://doi.org/10.1016/j.neuron.2018.08.011

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Martínez-Rosell G, Giorgino T, De Fabritiis G (2017) PlayMolecule ProteinPrepare: a web application for protein preparation for molecular dynamics simulations. J Chem Inf Model 57:1511–1516. https://doi.org/10.1021/acs.jcim.7b00190

    CAS  Article  PubMed  Google Scholar 

  13. 13.

    Pace CN, Grimsley GR, Scholtz JM (2009) Protein ionizable groups: pK values and their contribution to protein stability and solubility. J Biol Chem 284:13285–13289

    CAS  Article  Google Scholar 

  14. 14.

    Kim MO, Nichols SE, Wang Y, McCammon JA (2013) Effects of histidine protonation and rotameric states on virtual screening of M. tuberculosis RmlC. J Comput Aided Mol Des 27:235–246. https://doi.org/10.1007/s10822-013-9643-9

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  15. 15.

    De Beer SBA, Vermeulen NPE, Oostenbrink C, Oostenbrick C (2010) The role of water molecules in computational drug design. Curr Top Med Chem 10:55–66. https://doi.org/10.2174/156802610790232288

    Article  PubMed  Google Scholar 

  16. 16.

    Bernstein FC, Koetzle TF, Williams GJB et al (1977) The protein data bank: a computer-based archival file for macromolecular structures. J Mol Biol 112:535–542. https://doi.org/10.1016/S0022-2836(77)80200-3

    CAS  Article  PubMed  Google Scholar 

  17. 17.

    Berman HM, Westbrook J, Feng Z et al (2000) The Protein Data Bank. Nucleic Acids Res 28:235–242

    CAS  Article  Google Scholar 

  18. 18.

    Newton AS, Deiana L, Puleo DE et al (2017) JAK2 JH2 fluorescence polarization assay and crystal structures for complexes with three small molecules. ACS Med Chem Lett 8:614–617. https://doi.org/10.1021/acsmedchemlett.7b00154

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Puleo DE, Kucera K, Hammarén HM et al (2017) Identification and characterization of JAK2 pseudokinase domain small molecule binders. ACS Med Chem Lett 8:618–621. https://doi.org/10.1021/acsmedchemlett.7b00153

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Bandaranayake RM, Ungureanu D, Shan Y et al (2012) Crystal structures of the JAK2 pseudokinase domain and the pathogenic mutant V617F. Nat Struct Mol Biol 19:754–759. https://doi.org/10.1038/nsmb.2348

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Kiss R, Sayeski PP, Keseru GM (2010) Recent developments on JAK2 inhibitors: a patent review. Expert Opin Ther Pat 20:471–495

    CAS  Article  Google Scholar 

  22. 22.

    Silvennoinen O, Ungureanu D, Niranjan Y et al (2013) New insights into the structure and function of the pseudokinase domain in JAK2. Biochem Soc Trans 41:1002–1007. https://doi.org/10.1042/BST20130005

    CAS  Article  PubMed  Google Scholar 

  23. 23.

    Qamar K, Saboor M (2018) Jak 2 and Stat proteins; a mini review. Biomedica 34:232–235

    Google Scholar 

  24. 24.

    The GNU operating system and the free software movement. https://www.gnu.org/. Accessed 22 Feb 2020

  25. 25.

    Søndergaard CR, Olsson MHM, Rostkowski M, Jensen JH (2011) Improved treatment of ligands and coupling effects in empirical calculation and rationalization of p K a values. J Chem Theory Comput 7:2284–2295. https://doi.org/10.1021/ct200133y

    CAS  Article  PubMed  Google Scholar 

  26. 26.

    Olsson MHM, SØndergaard CR, Rostkowski M, Jensen JH (2011) PROPKA3: consistent treatment of internal and surface residues in empirical p K a predictions. J Chem Theory Comput 7:525–537. https://doi.org/10.1021/ct100578z

    CAS  Article  PubMed  Google Scholar 

  27. 27.

    Dolinsky TJ, Czodrowski P, Li H et al (2007) PDB2PQR: expanding and upgrading automated preparation of biomolecular structures for molecular simulations. Nucleic Acids Res 35:W522–W525. https://doi.org/10.1093/nar/gkm276

    Article  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Bash - GNU Project - free software foundation. https://www.gnu.org/software/bash/. Accessed 22 Feb 2020

  29. 29.

    Stutz M (2006) Get started with GAWK: AWK language fundamentals begin learning AWK with the open source GAWK implementation Skill Level: Intermediate

  30. 30.

    Tcl Developer Site. https://www.tcl.tk/. Accessed 22 Feb 2020

  31. 31.

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

    CAS  Article  PubMed  Google Scholar 

  32. 32.

    Baker NA, Sept D, Joseph S et al (2001) Electrostatics of nanosystems: application to microtubules and the ribosome. Proc Natl Acad Sci U S A 98:10037–10041. https://doi.org/10.1073/pnas.181342398

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Dolinsky TJ, Nielsen JE, McCammon JA, Baker NA (2004) PDB2PQR: an automated pipeline for the setup of Poisson-Boltzmann electrostatics calculations. Nucleic Acids Res 32. https://doi.org/10.1093/nar/gkh381

  34. 34.

    Unni S, Huang Y, Hanson RM et al (2011) Web servers and services for electrostatics calculations with APBS and PDB2PQR. J Comput Chem 32:1488–1491. https://doi.org/10.1002/jcc.21720

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Gordon JC, Myers JB, Folta T et al (2005) H++: a server for estimating pKas and adding missing hydrogens to macromolecules. Nucleic Acids Res 33:W368–W371. https://doi.org/10.1093/nar/gki464

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Anandakrishnan R, Aguilar B, Onufriev AV (2012) H++ 3.0: automating pK prediction and the preparation of biomolecular structures for atomistic molecular modeling and simulations. Nucleic Acids Res 40:W537–W541. https://doi.org/10.1093/nar/gks375

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Pearlman DA, Case DA, Caldwell JW et al (1995) AMBER, a package of computer programs for applying molecular mechanics, normal mode analysis, molecular dynamics and free energy calculations to simulate the structural and energetic properties of molecules. Comput Phys Commun 91:1–41. https://doi.org/10.1016/0010-4655(95)00041-D

    CAS  Article  Google Scholar 

  38. 38.

    Case DA, Cheatham TE, Darden T et al (2005) The Amber biomolecular simulation programs. J Comput Chem 26:1668–1688. https://doi.org/10.1002/jcc.20290

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Wickstrom L, Okur A, Simmerling C (2009) Evaluating the performance of the FF99SB force field based on NMR scalar coupling data. Biophys J 97:853–856. https://doi.org/10.1016/j.bpj.2009.04.063

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Davidchack RL, Handel R, Tretyakov MV (2009) Langevin thermostat for rigid body dynamics. J Chem Phys 130:234101. https://doi.org/10.1063/1.3149788

    CAS  Article  PubMed  Google Scholar 

  41. 41.

    Allen MP, Tildesley DJ (1989) Computer simulation of liquids. Clarendon Press

  42. 42.

    Berendsen HJC, Postma JPM, Van Gunsteren WF et al (1984) Molecular dynamics with coupling to an external bath. J Chem Phys 81:3684–3690. https://doi.org/10.1063/1.448118

    CAS  Article  Google Scholar 

  43. 43.

    Wan X, Ma Y, McClendon CL et al (2013) Ab initio modeling and experimental assessment of Janus kinase 2 (JAK2) kinase-pseudokinase complex structure. PLoS Comput Biol 9:e1003022. https://doi.org/10.1371/journal.pcbi.1003022

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Ayaz P, Hammarén H, Raivola J et al (2019) Structural models of full-length JAK2 kinase. bioRxiv:727727. https://doi.org/10.1101/727727

  45. 45.

    Giordanetto F, Kroemer RT (2002) Prediction of the structure of human Janus kinase 2 (JAK2) comprising JAK homology domains 1 through 7. Protein Eng 15:727–737. https://doi.org/10.1093/protein/15.9.727

    CAS  Article  PubMed  Google Scholar 

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Acknowledgments

The author would like to thank Dr. Martin Lepšík for his valuable suggestions, and for providing critical review on the manuscript.

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Correspondence to Syeda Rehana Zia.

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Zia, S.R. Role of water in the determination of protonation states of titratable residues. J Mol Model 27, 61 (2021). https://doi.org/10.1007/s00894-021-04677-5

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Keywords

  • Janus kinase 2 (JAK2)
  • Pseudokinase JAK homology 2 (JH2) domain
  • Water
  • Titratable residue
  • Protonation state
  • pKa