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.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
Durrant JD, McCammon JA (2011) Molecular dynamics simulations and drug discovery. BMC Biol 9:71. https://doi.org/10.1186/1741-7007-9-71
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
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
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
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
Lepšík M, Řezáč J, Kolář M et al (2013) The semiempirical quantum mechanical scoring function for in silico drug design. Chempluschem 78:921–931
Mongan J, Case DA (2005) Biomolecular simulations at constant pH. Curr Opin Struct Biol 15:157–163
Stern HA (2007) Molecular simulation with variable protonation states at constant pH. J Chem Phys 126:164112. https://doi.org/10.1063/1.2731781
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
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
Hollingsworth SA, Dror RO (2018) Molecular dynamics simulation for all. Neuron 99:1129–1143. https://doi.org/10.1016/j.neuron.2018.08.011
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
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
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
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
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
Berman HM, Westbrook J, Feng Z et al (2000) The Protein Data Bank. Nucleic Acids Res 28:235–242
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
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
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
Kiss R, Sayeski PP, Keseru GM (2010) Recent developments on JAK2 inhibitors: a patent review. Expert Opin Ther Pat 20:471–495
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
Qamar K, Saboor M (2018) Jak 2 and Stat proteins; a mini review. Biomedica 34:232–235
The GNU operating system and the free software movement. https://www.gnu.org/. Accessed 22 Feb 2020
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
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
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
Bash - GNU Project - free software foundation. https://www.gnu.org/software/bash/. Accessed 22 Feb 2020
Stutz M (2006) Get started with GAWK: AWK language fundamentals begin learning AWK with the open source GAWK implementation Skill Level: Intermediate
Tcl Developer Site. https://www.tcl.tk/. Accessed 22 Feb 2020
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
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
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
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
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
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
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
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
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
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
Allen MP, Tildesley DJ (1989) Computer simulation of liquids. Clarendon Press
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
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
Ayaz P, Hammarén H, Raivola J et al (2019) Structural models of full-length JAK2 kinase. bioRxiv:727727. https://doi.org/10.1101/727727
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
The author would like to thank Dr. Martin Lepšík for his valuable suggestions, and for providing critical review on the manuscript.
Conflict of interest
The author declares that there is no conflict of interest.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
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
- Janus kinase 2 (JAK2)
- Pseudokinase JAK homology 2 (JH2) domain
- Titratable residue
- Protonation state