Journal of Molecular Modeling

, Volume 17, Issue 10, pp 2639–2649 | Cite as

The effect of a Pro28Thr point mutation on the local structure and stability of human galactokinase enzyme—a theoretical study

  • Balázs Jójárt
  • Milán Szőri
  • Róbert Izsák
  • István Marsi
  • Aranka László
  • Imre G. Csizmadia
  • Béla Viskolcz
Original Paper


Galactokinase is responsible for the phosphorylation of α-d-galactose, which is an important step in the metabolism of the latter. Malfunctioning of galactokinase due to a single point mutation causes cataracts and, in serious cases, blindness. This paper reports a study of the Pro28Thr point mutation using a variety of theories including molecular dynamics (MD), MM-PBSA/GBSA calculations and AIM analysis. Altered H-bonding networks were detected based on geometric and electron density criteria that resulted in local unfolding of the β-sheet secondary structure. Another consequence was the decrease in stability (5–7 kcal mol−1) around this region, as confirmed by ΔGbind calculations for the extracted part of the whole system. Local unfolding was verified by several other MD simulations performed with different duration, initial velocities and force field. Based on the results, we propose a possible mechanism for the unfolding caused by the Pro28Thr point mutation.


The role of Thr28 and a water molecule in the local unfolding process around the point mutation of human galactokinase


Human galactokinase Molecular dynamics AIM MMPBSA/GBSA Local unfolding β-sheet stability 



Molecular dynamics


Wild-type form of galactokinase


Point-mutated form of galactokinase




Chain A of wild-type galactokinase


Chain B of wild-type galactokinase


Chain A of point-mutated galactokinase


Chain B of point-mutated galactokinase


Atoms in molecules theory


Atomic unit


Bond critical points


Point mutation


Binding free energy


Shorter (6 ns long) MD simulation of the MTA using different initial velocities


Not possible


Relative binding free energy



This work was supported by “Társadalmi Megújulás Operatív Program” (TÁMOP-4.2.1/B-09/1/KONV-2010-0005). The authors thank M. Labádi for technical support at the High Performance Computing Centre of the University of Szeged. The help of Methos L. Müller in the preparation of the graphics is acknowledged.

Supplementary material

894_2011_958_MOESM1_ESM.doc (2 mb)
ESM1 (DOC 2088 kb)


  1. 1.
    Bordner AJ, Abagyan RA (2004) Large-scale prediction of protein geometry and stability changes for arbitrary single point mutations. Proteins Struct Func Bioinf 57:400–413CrossRefGoogle Scholar
  2. 2.
    Gilis D, Rooman M (1999) Prediction of stability changes upon single-site mutations using database-derived potentials. Theor Chem Acc 101:46–50Google Scholar
  3. 3.
    Carlsson P, Koehler KF, Nilsson L (2005) Glucocorticoid receptor point mutation V571M facilitates coactivator and ligand binding by structural rearrangement and stabilization. Mol Endocrinol 19:1960–1977CrossRefGoogle Scholar
  4. 4.
    Sneddon SF, Tobias DJ (1992) The role of packing interactions in stabilizing folded proteins. Biochemistry 31:2842–2846CrossRefGoogle Scholar
  5. 5.
    Dang LX, Merz KM, Kollman PA (1989) Free-energy calculations on protein stability: Thr-1573Val-157 mutation of T4 lysozyme. J Am Chem Soc 111:8505–8508CrossRefGoogle Scholar
  6. 6.
    Piana S, Laio A, Marinelli F, Van Troys M, Bourry D, Ampe Ch, Martins JC (2008) Predicting the effect of a point mutation on a protein fold: the villin and advillin headpieces and their Pro62Ala mutants. J Mol Biol 375:460–470CrossRefGoogle Scholar
  7. 7.
    Sahai MA, Viskolcz B, Pai EF, Csizmadia IG (2007) Quantifying the intrinsic effects of two point mutation models of proline proline diamino acid diamide: a first-principle computational study. J Phys Chem B 111:11592–11602CrossRefGoogle Scholar
  8. 8.
    Gitzelmann R, Hansen RG (1974) Galactose biogenesis and disposal in galactosemics. Biochim Biophys Acta 372:374–378Google Scholar
  9. 9.
    Holden HM, Thoden JB, Timson DJ, Reece RJ (2004) Galactokinase: structure, function and role in type II galactosemia. Cell Mol Life Sci 61:2471–2484CrossRefGoogle Scholar
  10. 10.
    Kinoshita JH, Dikmak E, Satoh K, Merola L (1962) Osmotic changes caused by accumulation of dulcitol in lenses of rats fed With galactose. Nature 194:1085–1087CrossRefGoogle Scholar
  11. 11.
    Tsakiris S, Schulpis KH, Marinou K, Behrakis P (2004) Protective effect of l-cysteine and glutathione on the modulated suckling rat brain Na+, K + ATPase and Mg2 + ATPase activities induced by the in vitro galactosaemia. Pharmacol Res 49:475–479CrossRefGoogle Scholar
  12. 12.
    Thoden JB, Timson DJ, Reece RJ, Holden HM (2005) Molecular structure of human galactokinase—implications for type II galactosemia. J Biol Chem 280:9662–9670CrossRefGoogle Scholar
  13. 13.
    Timson DJ, Reece RJ (2003) Functional analysis of disease-causing mutations in human galactokinase. Eur J Biochem 270:1767–1774CrossRefGoogle Scholar
  14. 14.
    Nowicka A, Mackiewicz P, Dudkiewicz M, Mackiewicz D, Kowalczuk M, Cebrat S, Dudek MR (2003) In: Sloot PMA et al (eds) Correlation between mutation pressure, selection pressure, and occurrence of amino acids. ICCS, LNCS 2658:650–657Google Scholar
  15. 15.
    Kalaydjieva LV, Perez-Lezaun A, Angelicheva D, Onengut S, Dye D, Bosshard NU, Jordanova A, Savov A, Yanakiev P, Kremensky I, Radeva B, Hallmayer J, Markov A, Nedkova V, Tournev I, Aneva L, Gitzelmann R (1999) A founder mutation in the GK1 gene is responsible for galactokinase deficiency in Roma (Gypsies). Am J Hum Genet 65:1299–1307CrossRefGoogle Scholar
  16. 16.
    Hunter M, Heyer E, Austerlitz F, Angelicheva D, Nedkova V, Briones P, Gata A, De Pablo R, Laszlo A, Bosshard L, Gitzelmann R, Tordai A, Kalmar L, Szalai C, Balogh I, Lupu C, Corches A, Popa G, Perez-Lezaun A, Kalaydjieva LV (2002) The P28 mutation in the GALK1 gene accounts for galactokinase deficiency in Roma (Gypsy) patients across Europe. Pediatr Res 51:602–606CrossRefGoogle Scholar
  17. 17.
    Capriotti E, Fariselli P, Casadio R (2005) I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Res 33:W306–W310CrossRefGoogle Scholar
  18. 18.
    Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The Protein Data Bank. Nucleic Acids Res 28:235–242CrossRefGoogle Scholar
  19. 19.
    Berman H, Henrick K, Nakamura H (2003) Announcing the Worldwide Protein Data Bank. Nat Struct Biol 10:980CrossRefGoogle Scholar
  20. 20.
    Molecular Operating Environment (2007) C.C.G. Inc, Montreal, Quebec, CanadaGoogle Scholar
  21. 21.
    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:W665–W667CrossRefGoogle Scholar
  22. 22.
    Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML (1983) Comparison of Simple Potential Functions for Simulating Liquid Water. J Chem Phys 79:926–935CrossRefGoogle Scholar
  23. 23.
    MacKerell A, Bashford D, Bellott M, Dunbrack RL, Evanseck JD, Field MJ, Fischer S, Gao J, Guo H, Ha S, Joseph-McCarthy D, Kuchnir L, Kuczera K, Lau FTK, Mattos C, Michnick S, Ngo T, Nguyen DT, Prodhom B, Reiher WE, Roux B, Schlenkrich M, Smith JC, Stote R, Straub J, Watanabe M, Wiorkiewicz-Kuczera J, Yin D, Karplus M (1998) All-atom empirical potential for molecular modeling and dynamics studies of proteins. J Phys Chem B 102:3586–3616CrossRefGoogle Scholar
  24. 24.
    Mackerell AD, Feig M, Brooks CL (2004) Extending the treatment of backbone energetics in protein force fields: limitations of gas-phase quantum mechanics in reproducing protein conformational distributions in molecular dynamics simulations. J Comput Chem 25:1400–1415CrossRefGoogle Scholar
  25. 25.
    Beglov D, Roux B (1994) Finite representation of an infinite bulk system—solvent boundary potential for computer-simulations. J Chem Phys 100:9050–9063CrossRefGoogle Scholar
  26. 26.
    Bowers KJ, Chow E, Xu H, Dror RO, Eastwood MP, Gregersen BA, Klepeis JL, Kolossvary I, Moraes MA, Sacerdoti FD, Salmon JK, Y, Shaw DE (2006) Scalable algorithms for molecular dynamics simulations on commodity clusters. Proceedings of the 2006 ACM/IEEE Conference on Supercomputing, SC'06Google Scholar
  27. 27.
    Berendsen HJC, Postma JPM, Van Gunsteren WF, Dinola A, Haak JR (1984) Molecular dynamics with coupling to an external bath. J Chem Phys 81:3684–3690CrossRefGoogle Scholar
  28. 28.
    Darden T, York D, Pedersen L (1993) Particle mesh Ewald—an N × Log(N) method for Ewald sums in large systems. J Chem Phys 98:10089–10092CrossRefGoogle Scholar
  29. 29.
    Caves LSD, Evanseck JD, Karplus M (1998) Locally accessible conformations of proteins: multiple molecular dynamics simulations of crambin. Protein Sci 7:649–666Google Scholar
  30. 30.
    Kaminski GA, Friesner RA, Tirado-Rives J, Jorgensen WL (2001) Evaluation and reparametrization of the OPLS-AA force field forpProteins via comparison with accurate quantum chemical calculations on peptides. J Phys Chem B 105:6474–6487CrossRefGoogle Scholar
  31. 31.
    Bader RFW (1985) Atoms in molecules. Acc Chem Res 18:9–15CrossRefGoogle Scholar
  32. 32.
    Bader RFW (1991) A quantum-theory of molecular-structure and its applications. Chem Rev 91:893–928CrossRefGoogle Scholar
  33. 33.
    Srinivasan J, Cheatham TE, Cieplak P, Kollman PA, Case DA (1998) Continuum solvent studies of the stability of DNA, RNA and phosphoramidate-DNA helices. J Am Chem Soc 120:9401–9409CrossRefGoogle Scholar
  34. 34.
    Kollman PA, Massova I, Reyes C, Kuhn B, Huo S, Chong L, Lee M, Lee T, Duan Y, Wang W, Donini O, Cieplak P, Srinivasan J, Case DA, Cheatham TE III (2000) Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. Acc Chem Res 33:889–897CrossRefGoogle Scholar
  35. 35.
    Case DA, Darden TA, Cheatham TE III, Simmerling CL, Wang J, Duke RE, Luo R, Merz KM, Pearlman DA, Crowley M, Walker RC, Zhang W, Wang B, Hayik S, Roitberg A, Seabra G, Wong KF, Paesani F, Wu X, Brozell S, Tsui V, Gohlke H, Yang L, Tan C, Mongan J, Hornak V, Cui G, Beroza P, Mathews DH, Schafmeister C, Ross WS, Kollman PA (2006) AMBER 9. University of California, San FranciscoGoogle Scholar
  36. 36.
    Brooks BR, Janežič D, Karplus M (1995) Harmonic analysis of large system. I. Methodology. J Comput Chem 16:1522–1542CrossRefGoogle Scholar
  37. 37.
    Humphrey W, Dalke A, Schulten K (1996) VMD: Visual molecular dynamics. J Mol Graph 14:33–38CrossRefGoogle Scholar
  38. 38.
    Kabsch W, Sander C (1983) Dictionary of protein secondary structure—pattern-recognition of hydrogen-bonded and geometrical features. Biopolymers 22:2577–2637CrossRefGoogle Scholar
  39. 39.
    Mezei M (2010) Simulaid: a simulation facilitator and analysis program. J Comput Chem 31:2658–2668CrossRefGoogle Scholar
  40. 40.
    Tobias DJ, Sneddon SF, Brooks CL (1992) Stability of a model beta-sheet in water. J Mol Biol 227:1244–1252CrossRefGoogle Scholar
  41. 41.
    Sneddon SF, Tobias DJ, Brooks CL (1989) Thermodynamics of amide hydrogen-bond formation in polar and apolar solvents. J Mol Biol 209:817–820CrossRefGoogle Scholar
  42. 42.
    Seshasayee A (2005) High-temperature unfolding of a Trp-cage mini-protein: a molecular dynamics simulation study. Theor Biol Med Model 2:7–11CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Balázs Jójárt
    • 1
  • Milán Szőri
    • 1
  • Róbert Izsák
    • 1
  • István Marsi
    • 1
  • Aranka László
    • 2
  • Imre G. Csizmadia
    • 1
    • 3
  • Béla Viskolcz
    • 1
  1. 1.Department of Chemical InformaticsUniversity of SzegedSzegedHungary
  2. 2.Department of Pediatrics, Albert Szent-Györgyi Medical CenterUniversity of SzegedSzegedHungary
  3. 3.Department of ChemistryUniversity of TorontoTorontoCanada

Personalised recommendations