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Folding of pig gastric mucin non-glycosylated domains: a discrete molecular dynamics study

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Abstract

Mucin glycoproteins consist of tandem-repeating glycosylated regions flanked by non-repetitive protein domains with little glycosylation. These non-repetitive domains are involved in polymerization of mucin and play an important role in the pH-dependent gelation of gastric mucin, which is essential for protecting the stomach from autodigestion. We examine folding of the non-repetitive sequence of PGM-2X (242 amino acids) and the von Willebrand factor vWF-C1 domain (67 amino acids) at neutral and low pH using discrete molecular dynamics (DMD) in an implicit solvent combined with a four-bead peptide model. Using the same implicit solvent parameters, folding of both domains is simulated at neutral and low pH. In contrast to vWF-C1, PGM-2X folding is strongly affected by pH as indicated by changes in the contact order, radius of gyration, free-energy landscape, and the secondary structure. Whereas the free-energy landscape of vWF-C1 shows a single minimum at both neutral and low pH, the free-energy landscape of PGM-2X is characterized by multiple minima that are more numerous and shallower at low pH. Detailed structural analysis shows that PGM-2X partially unfolds at low pH. This partial unfolding is facilitated by the C-terminal region GLU236-PRO242, which loses contact with the rest of the domain due to effective “mean-field” repulsion among highly positively charged N- and C-terminal regions. Consequently, at low pH, hydrophobic amino acids are more exposed to the solvent. In vWF-C1, low pH induces some structural changes, including an increased exposure of CYS at position 67, but these changes are small compared to those found in PGM-2X. For PGM-2X, the DMD-derived average β-strand propensity increases from 0.26 ± 0.01 at neutral pH to 0.38 ± 0.01 at low pH. For vWF-C1, the DMD-derived average β-strand propensity is 0.32 ± 0.02 at neutral pH and 0.35 ± 0.02 at low pH. The DMD-derived structural information provides insight into pH-induced changes in the folding of two distinct mucin domains and suggests plausible mechanisms of the aggregation/gelation of mucin.

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References

  1. Bansil, R., Turner, B.S.: Mucin structure, aggregation, physiological functions and biomedical applications. Curr. Opin. Colloid Interface Sci. 11, 164–170 (2006)

    Article  Google Scholar 

  2. Strous, G.J., Dekker, J.: Mucin-type glycoproteins. Crit. Rev. Biochem. Mol. Biol. 27, 57–92 (1992)

    Article  Google Scholar 

  3. Perez-Vilar, J., Hill, R.L.: The structure and assembly of secreted mucins. J. Biol. Chem. 274, 31751–31754 (1999)

    Article  Google Scholar 

  4. Turner, B.S.: Expression of cysteine-rich pig gastric mucin (MUC5AC) domains in Pichia pastoris and their pH-dependent properties. Dissertation, Boston University (2012)

  5. Bhaskar, K.R., Gong, D., Bansil, R., Pajevic, S., Hamilton, J.A., Turner, B.S., Lamont, J.T.: Profound increase in viscosity and aggregation of pig gastric mucin at low pH. Am. J. Physiol. 261, G827–G833 (1991)

    Google Scholar 

  6. Cao, X.X., Bansil, R., Bhaskar, K.R., Turner, B.S., LaMont, J.T., Niu, N., Afdhal, N.H.: pH-dependent conformational change of gastric mucin leads to sol-gel transition. Biophys. J. 76, 1250–1258 (1999)

    Article  Google Scholar 

  7. Celli, J.P., Turner, B.S., Afdhal, N.H., Ewoldt, R.H., McKinley, G.H., Bansil, R., Erramilli, S.: Rheology of gastric mucin exhibits a pH-dependent sol-gel transition. Biomacromolecules 8, 1580–1586 (2007)

    Article  Google Scholar 

  8. Hollander, F.: The two-component mucous barrier; its activity in protecting the gastroduodenal mucosa against peptic ulceration. AMA Arch. Intern. Med. 93, 107–120 (1954)

    Article  Google Scholar 

  9. Hong, Z., Chasan, B., Bansil, R., Turner, B.S., Bhaskar, K.R., Afdhal, N.H.: Atomic force microscopy reveals aggregation of gastric mucin at low pH. Biomacromolecules 6, 3458–3466 (2005)

    Article  Google Scholar 

  10. Cao, X.: Aggregation and gelation of mucin and its interactions with lipid vesicles. Dissertation, Boston University (1997)

  11. Rapaport, D.C.: The Art of Molecular Dynamics Simulation. Cambridge University Press, Cambridge (1995)

    Google Scholar 

  12. Teplow, D.B., Lazo, N.D., Bitan, G., Bernstein, S., Wyttenbach, T., Bowers, M.T., Baumketner, A., Shea, J.E., Urbanc, B., Cruz, L., Borreguero, J., Stanley, H.E.: Elucidating amyloid β-protein folding and assembly: a multidisciplinary approach. Acc. Chem. Res. 39, 635–645 (2006)

    Article  Google Scholar 

  13. Smith, A.V., Hall, C.K.: α-helix formation: discontinuous molecular dynamics on an intermediate-resolution protein model. Protein Struct. Funct. Genet. 44, 344–360 (2001)

    Article  Google Scholar 

  14. Ding, F., Borreguero, J.M., Buldyrev, S.V., Stanley, H.E., Dokholyan, N.V.: Mechanism for the α-helix to β-hairpin transition. Proteins 53, 220–228 (2003)

    Article  Google Scholar 

  15. Urbanc, B., Borreguero, J.M., Cruz, L., Stanley, H.E.: Ab initio discrete molecular dynamics approach to protein folding and aggregation. Meth. Enzymol. 412, 314–338 (2006)

    Article  Google Scholar 

  16. Urbanc, B., Cruz, L., Yun, S., Buldyrev, S.V., Bitan, G., Teplow, D.B., Stanley, H.E.: In silico study of amyloid β-protein folding and oligomerization. Proc. Natl. Acad. Sci. USA 101, 17345–17350 (2004)

    Article  ADS  Google Scholar 

  17. Kyte, J., Doolittle, R.F.: A simple method for displaying the hydropathic character of a protein. J. Mol. Biol. 157, 105–132 (1982)

    Article  Google Scholar 

  18. Lam, A.R., Teplow, D.B., Stanley, H.E., Urbanc, B.: Effects of the Arctic (E22–>G) mutation on amyloid β-protein folding: discrete molecular dynamics study. J. Am. Chem. Soc. 130, 17413–17422 (2008)

    Article  Google Scholar 

  19. Urbanc, B., Betnel, M., Cruz, L., Bitan, G., Teplow, D.B.: Elucidation of amyloid β-protein oligomerization mechanisms: discrete molecular dynamics study. J. Am. Chem. Soc. 132, 4266–4280 (2010)

    Article  Google Scholar 

  20. Berendsen, H.J.C., Postma, J.P.M., van Gunsteren, W.F., Dinola, A., Haak, J.R.: Molecular dynamics with coupling to an external bath. J. Chem. Phys. 81, 3684–3690 (1984)

    Article  ADS  Google Scholar 

  21. Nosé, S.: A unified formulation of the constant temperature molecular dynamics methods. J. Chem. Phys. 81, 511 (1984)

    Article  ADS  Google Scholar 

  22. Dani, V.S., Ramakrishnan, C., Varadarajan, R.: MODIP revisited: re-evaluation and refinement of an automated procedure for modeling of disulfide bonds in proteins. Protein Eng. 16, 187–193 (2003)

    Article  Google Scholar 

  23. Urbanc, B., Betnel, M., Cruz, L., Li, H., Fradinger, E.A., Monien, B.H., Bitan, G.: Structural basis for Aβ1-42 toxicity inhibition by Aβ C-terminal fragments: discrete molecular dynamics study. J. Mol. Biol. 410, 316–328 (2011)

    Article  Google Scholar 

  24. Masunov, A., Lazaridis, T.: Potentials of mean force between ionizable amino acid side chains in water. J. Am. Chem. Soc. 125, 1722–1730 (2003)

    Article  Google Scholar 

  25. Humphrey, W., Dalke, A., Schulten, K.: VMD: visual molecular dynamics. J. Mol. Graph. 14, 33–38 (1996)

    Article  Google Scholar 

  26. Plaxco, K.W., Simons, K.T., Baker, D.: Contact order, transition state placement and the refolding rates of single domain proteins. J. Mol. Biol. 277, 985–994 (1998)

    Article  Google Scholar 

  27. Turner, B.S., Bhaskar, K.R., Hadzopoulou-Cladaras, M., LaMont, J.T.: Cysteine-rich regions of pig gastric mucin contain von Willebrand factor and cystine knot domains at the carboxyl terminal. Biochim. Biophys. Acta 1447, 77–92 (1999)

    Article  Google Scholar 

  28. Roy, A., Kucukural, A., Zhang, Y.: I-TASSER: a unified platform for automated protein structure and function prediction. Nat. Protoc. 5, 725–738 (2010)

    Article  Google Scholar 

  29. Zhang, Y.: Template-based modeling and free modeling by I-TASSER in CASP7. Proteins 69(Suppl 8), 108–117 (2007)

    Article  Google Scholar 

  30. Yun, S., Urbanc, B., Cruz, L., Bitan, G., Teplow, D.B., Stanley, H.E.: Role of electrostatic interactions in amyloid β-protein (Aβ) oligomer formation: a discrete molecular dynamics study. Biophys. J. 92, 4064–4077 (2007)

    Article  ADS  Google Scholar 

  31. Barz, B., Urbanc, B.: Dimer formation enhances structural differences between amyloid β-protein (1–40) and (1–42): an explicit-solvent molecular dynamics study. PLoS ONE 7, e34345 (2012)

    Article  ADS  Google Scholar 

  32. Gniewek, P., Kolinski, A.: Coarse-grained Monte Carlo simulations of mucus: structure, dynamics, and thermodynamics. Biophys. J. 99, 3507–3516 (2010)

    Article  ADS  Google Scholar 

  33. Ruggeri, Z.M.: von Willebrand factor. J. Clin. Invest. 100, S41–S46 (1997)

    Google Scholar 

  34. de Wit, T.R., van Mourik, J.A.: Biosynthesis, processing and secretion of von Willebrand factor: biological implications. Best. Pract. Res. Cl. Ha. 14, 241–255 (2001)

    Article  Google Scholar 

  35. Dekker, J., Rossen, J.W.A., Buller, H.A., Einerhand, A.W.C.: The MUC family: an obituary. Trends Biochem. Sci. 27, 126–131 (2002)

    Article  Google Scholar 

  36. Berendsen, H.J.C., van der Spoel, D., van Drunen, R.: GROMACS: a message-passing parallel molecular dynamics implementation. Comput. Phys. Commun. 91, 43–56 (1995)

    Article  ADS  Google Scholar 

  37. Lindahl, E., Hess, B., van der Spoel, D.: GROMACS 3.0: a package for molecular simulation and trajectory analysis. J. Mol. Model. 7, 306–317 (2001)

    Google Scholar 

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Acknowledgements

The authors thank Dr. Yuriy V. Sereda for his contribution to the implementation of PRO amino acid into the four-bead protein model. B.U. and B.B. acknowledge the support by the NIH grant AG027818 and thank NSF for the access to the Extreme Science and Engineering Discovery Environment (XSEDE) supercomputing facilities through the grant PHYS100030. B.S.T. thanks Dr. Nezam Afdhal, M.D., Beth Israel Deaconess Medical Center for financial support.

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Correspondence to Brigita Urbanc.

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Figure S1

Normalized distributions of (a) the distance between the Cα atoms of a PRO residue and the residue directly preceding it in the sequence and (b) the dihedral angle ω defining trans (ω=π) versus cis (ω=0) conformation. The distributions were calculated using 15,000 reported protein structures from the Protein Data Bank (EPS 4.24 MB)

Figure S2

Normalized distributions of pair-wise RMSD values for all initial and final DMD-derived conformations of (a) PGM-2X and (b) vWF-C1 domains. Distributions for final conformations at neutral and low pH are shown in black and red, respectively (EPS 3.31 MB)

Figure S3

A difference between the neutral and low pH contact map of PGM-2X folded structures (see Fig. 5). The triangle below the diagonal shows positive values of the contact map difference, i.e., the contacts that are stronger at neutral pH and the triangle above the diagonal shows the negative values of the contact map difference, i.e., the contacts that are stronger at low pH. The color scale quantifying the average number of contacts between two residues is displayed on the right (TIFF 624 kb)

Figure S4

A difference between the neutral and low pH contact map of vWF-C1 folded structures (see Fig. 10). The triangle below the diagonal shows positive values of the contact map difference, i.e., the contacts that are stronger at neutral pH and the triangle above the diagonal shows the negative values of the contact map difference, i.e., the contacts that are stronger at low pH. The color scale quantifying the average number of contacts between two residues is displayed on the right (TIFF 513 kb)

Figure S5

Probability distribution of the separation |i-j| along the protein sequence between CYS residue i and CYS residue j for (A) PGM-2X and (B) vWF-C1 folded structures. Only the CYS-CYS residue pairs with respective Cβ atoms within a distance of 0.42 nm, corresponding to disulfide bonds in the model, were considered. Distributions at neutral and low pH conformations are shown in black and red, respectively (EPS 3.69 MB)

Figure S6

PGM-2X protein models predicted by the I-TASSER server. The N-terminal amino acid ASP1 is colored red and the C-terminal amino acid PRO242 is colored blue. The images were generated by the VMD software package [25] (TIFF 946 kb)

Figure S7

vWF-C1 protein models predicted by the I-TASSER server. The N-terminal amino acid CYS1 is colored red and the C-terminal amino acid CYS67 is colored blue. The images were generated by the VMD software package [25] (TIFF 671 kb)

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Barz, B., Turner, B.S., Bansil, R. et al. Folding of pig gastric mucin non-glycosylated domains: a discrete molecular dynamics study. J Biol Phys 38, 681–703 (2012). https://doi.org/10.1007/s10867-012-9280-x

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