A New Approach In Protein Folding Studies Revealed The Potential Site For Nucleation Center
A new approach to predict the 3D structures of proteins by combining the knowledge-based method and Molecular Dynamics Simulation is presented on the chicken villin headpiece subdomain (HP-36). Comparative modeling is employed as the knowledge-based method to predict the core region (Ala9-Asn28) of the protein while the remaining residues are built as extended regions (Met1-Lys8; Leu29-Phe36) which then further refined using Molecular Dynamics Simulation for 120 ns. Since the core region is built based on a high sequence identity to the template (65%) resulting in RMSD of 1.39 Å from the native, it is believed that this well-developed core region can act as a 'nucleation center' for subsequent rapid downhill folding. Results also demonstrate that the formation of the non-native contact which tends to hamper folding rate can be avoided. The best 3D model that exhibits most of the native characteristics is identified using clustering method which then further ranked based on the conformational free energies. It is found that the backbone RMSD of the best model compared to the NMR-MDavg is 1.01 Å and 3.53 Å, for the core region and the complete protein, respectively. In addition to this, the conformational free energy of the best model is lower by 5.85 kcal/mol as compared to the NMR-MDavg. This structure prediction protocol is shown to be effective in predicting the 3D structure of small globular protein with a considerable accuracy in much shorter time compared to the conventional Molecular Dynamics simulation alone.
[1] Sanchez R, Sali A: Advances in comparative protein-structure modeling.
Curr Op Struc Biol 1997, 7:206-214.
[2] Moult J, Fidelis K, Rost B, Hubbard T, Tramontano A: Critical
assessment of methods of protein structure prediction (CASP) - Round
6. Proteins 2005, 61:3-7.
[3] Zagrovic B, Snow CD, Shirts MR, Pande VS: Simulation of folding of a
small alpha-helical protein in atomistic detail using worldwidedistributed
computing. J Mol Biol 2002, 323:927-937.
[4] Duan Y, Kollman PA: Pathways to a protein folding intermediate
observed in a 1-microsecond simulation in aqueous solution. Science
1998, 282:740-744.
[5] Jang S, Kim E, Shin S, Pak Y: Ab initio folding of helix bundle proteins
using molecular dynamics simulations. J Am Chem Soc 2003,
125:14841-14846.
[6] Krautler V, Aemissegger A, Hunenberger PH, Hilvert D, Hansson T, van
Gunsteren WF: Use of molecular dynamics in the design and structure
determination of a photoinducible b-hairpin. J Am Chem Soc 2004,
127:4935-4942.
[7] Kubelka J, Eaton WA, Hofrichter J: Experimental tests of villin
subdomain folding simulations. J Mol Biol 2003, 329:625-630.
[8] Sussman JL, Lin D, Jiang J, Manning NO, Prilusky J, Ritter O, Abola
EE: Protein Data Bank (PDB): Database of three-dimensional structural
information of biological macromolecules. Acta Cryst D Biol Cryst
1998, 54:1078-1084.
[9] Jorgensen WL, Chandrasekar A, Madura JD, Impey RW, Klein ML:
Comparison of simple potential functions for simulating liquid water. J
Chem Phys 1983, 79:926-935.
[10] Berendsen HJC, Postma JPM, van Gunsteren WF, Dinola A, Haak JR:
Molecular dynamics with coupling to an external bath. J Comp Phys
1984, 81:3684-3690.
[11] Pearlman DA, Case DA, Caldwell JW, Ross WS, Cheatham III TE,
DeBolt S, Ferguson N, Seibel G, Kollman P: 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. Comp Phys Comm
1995, 91:1-41.
[12] Duan Y, Wu C, Chowdhury S, Lee M.C, Xiong G, Zhang W, Yang R,
Cieplak P, Luo R, Lee T, et al: A point-charge force field for molecular
mechanics simulations of proteins based on condensed-phase quantum
mechanical calculations. J Comp Chem 2003, 24:1999-2012.
[13] Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ: Basic local
alignment search tool. J Mol Biol 1990, 215:403- 410.
[14] Thompson JD, Higgins DG, Gibson TJ: CLUSTALW: Improving the
sensitivity of progressive multiple sequence alignment through sequence
weighting, position-specific gap penalties and weight matric choice. Nuc
Acids Res 1994, 22:4673-4680.
[15] Sali A, Blundell TL: Comparative protein modelling by satisfaction of
spatial restraints. J Mol Biol 1993, 234:779-815.
[16] Hubbard SJ, Thornton JM: "NACCESS", Computer program.
Department of Biochemistry and Molecular Biology, University College
London; 1993.
[17] Feig M, Karanicolas J, Brooks III CL: MMTSB Tool set: Enhanced
sampling and multiscale modeling methods for applications in structural
biology. J Mol Biol 2004, 55:379-400.
[18] Kollman P, Massova I, Reyes CM, Kuhn B, Huo S, Chong LT, Lee MR,
Lee T, Duan Y, Wang W, et al: Calculating structures and free energies
of complex molecules: Combining molecular mechanics and continuum
models. Acc Chem Res 2000, 33:889-897.
[19] Srinivasan J, Cheatham III TE, Cieplak P, Kollman P, Case DA:
Continuum solvent studies of the stability of DNA, RNA and
phosphoramidate-DNA helices. J Am Chem Soc 1998, 120:9401-9409.
[20] Vorobjev YN, Hermans J: ES/IS: Estimation of conformational free
energy by combining dynamics simulations with explicit solvent with an
implicit solvent continuum model. Biophys Chem 1999, 78:195-205.
[21] Lee MR, Duan Y, Kollman PA: Use of MM-PB/SA in estimating the
free energies of proteins: Application to native, intermediates, and
unfolded villin headpiece. Proteins 2000, 39:309-316.
[22] Alonso DOV, Daggett V: Molecular dynamics simulations of
hydrophobic collapse of ubiquitin. Protein Sci 1998, 7:860-874.
[23] Creighton TE: PROTEINS: Structures and molecular properties. 2nd
edn. New York: W.H. Freeman and Company; 1993.
[24] Yang A, Honig B: Free energy determinants of secondary structure
formation: I. a-helices. J Mol Biol 1995, 252:351-365.
[25] Shirts MR, Pande VS: Screensavers of the world, unite! Science 2001,
290:1903-1904.
[26] Vorobjev YN, Hermans J: Free energies of protein decoys provide
insight into determinants of protein stability. Protein Sci 2001, 10:2498-
2506.
[1] Sanchez R, Sali A: Advances in comparative protein-structure modeling.
Curr Op Struc Biol 1997, 7:206-214.
[2] Moult J, Fidelis K, Rost B, Hubbard T, Tramontano A: Critical
assessment of methods of protein structure prediction (CASP) - Round
6. Proteins 2005, 61:3-7.
[3] Zagrovic B, Snow CD, Shirts MR, Pande VS: Simulation of folding of a
small alpha-helical protein in atomistic detail using worldwidedistributed
computing. J Mol Biol 2002, 323:927-937.
[4] Duan Y, Kollman PA: Pathways to a protein folding intermediate
observed in a 1-microsecond simulation in aqueous solution. Science
1998, 282:740-744.
[5] Jang S, Kim E, Shin S, Pak Y: Ab initio folding of helix bundle proteins
using molecular dynamics simulations. J Am Chem Soc 2003,
125:14841-14846.
[6] Krautler V, Aemissegger A, Hunenberger PH, Hilvert D, Hansson T, van
Gunsteren WF: Use of molecular dynamics in the design and structure
determination of a photoinducible b-hairpin. J Am Chem Soc 2004,
127:4935-4942.
[7] Kubelka J, Eaton WA, Hofrichter J: Experimental tests of villin
subdomain folding simulations. J Mol Biol 2003, 329:625-630.
[8] Sussman JL, Lin D, Jiang J, Manning NO, Prilusky J, Ritter O, Abola
EE: Protein Data Bank (PDB): Database of three-dimensional structural
information of biological macromolecules. Acta Cryst D Biol Cryst
1998, 54:1078-1084.
[9] Jorgensen WL, Chandrasekar A, Madura JD, Impey RW, Klein ML:
Comparison of simple potential functions for simulating liquid water. J
Chem Phys 1983, 79:926-935.
[10] Berendsen HJC, Postma JPM, van Gunsteren WF, Dinola A, Haak JR:
Molecular dynamics with coupling to an external bath. J Comp Phys
1984, 81:3684-3690.
[11] Pearlman DA, Case DA, Caldwell JW, Ross WS, Cheatham III TE,
DeBolt S, Ferguson N, Seibel G, Kollman P: 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. Comp Phys Comm
1995, 91:1-41.
[12] Duan Y, Wu C, Chowdhury S, Lee M.C, Xiong G, Zhang W, Yang R,
Cieplak P, Luo R, Lee T, et al: A point-charge force field for molecular
mechanics simulations of proteins based on condensed-phase quantum
mechanical calculations. J Comp Chem 2003, 24:1999-2012.
[13] Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ: Basic local
alignment search tool. J Mol Biol 1990, 215:403- 410.
[14] Thompson JD, Higgins DG, Gibson TJ: CLUSTALW: Improving the
sensitivity of progressive multiple sequence alignment through sequence
weighting, position-specific gap penalties and weight matric choice. Nuc
Acids Res 1994, 22:4673-4680.
[15] Sali A, Blundell TL: Comparative protein modelling by satisfaction of
spatial restraints. J Mol Biol 1993, 234:779-815.
[16] Hubbard SJ, Thornton JM: "NACCESS", Computer program.
Department of Biochemistry and Molecular Biology, University College
London; 1993.
[17] Feig M, Karanicolas J, Brooks III CL: MMTSB Tool set: Enhanced
sampling and multiscale modeling methods for applications in structural
biology. J Mol Biol 2004, 55:379-400.
[18] Kollman P, Massova I, Reyes CM, Kuhn B, Huo S, Chong LT, Lee MR,
Lee T, Duan Y, Wang W, et al: Calculating structures and free energies
of complex molecules: Combining molecular mechanics and continuum
models. Acc Chem Res 2000, 33:889-897.
[19] Srinivasan J, Cheatham III TE, Cieplak P, Kollman P, Case DA:
Continuum solvent studies of the stability of DNA, RNA and
phosphoramidate-DNA helices. J Am Chem Soc 1998, 120:9401-9409.
[20] Vorobjev YN, Hermans J: ES/IS: Estimation of conformational free
energy by combining dynamics simulations with explicit solvent with an
implicit solvent continuum model. Biophys Chem 1999, 78:195-205.
[21] Lee MR, Duan Y, Kollman PA: Use of MM-PB/SA in estimating the
free energies of proteins: Application to native, intermediates, and
unfolded villin headpiece. Proteins 2000, 39:309-316.
[22] Alonso DOV, Daggett V: Molecular dynamics simulations of
hydrophobic collapse of ubiquitin. Protein Sci 1998, 7:860-874.
[23] Creighton TE: PROTEINS: Structures and molecular properties. 2nd
edn. New York: W.H. Freeman and Company; 1993.
[24] Yang A, Honig B: Free energy determinants of secondary structure
formation: I. a-helices. J Mol Biol 1995, 252:351-365.
[25] Shirts MR, Pande VS: Screensavers of the world, unite! Science 2001,
290:1903-1904.
[26] Vorobjev YN, Hermans J: Free energies of protein decoys provide
insight into determinants of protein stability. Protein Sci 2001, 10:2498-
2506.
@article{"International Journal of Biological, Life and Agricultural Sciences:64528", author = "Nurul Bahiyah Ahmad Khairudin and Habibah A Wahab", title = "A New Approach In Protein Folding Studies Revealed The Potential Site For Nucleation Center", abstract = "A new approach to predict the 3D structures of proteins by combining the knowledge-based method and Molecular Dynamics Simulation is presented on the chicken villin headpiece subdomain (HP-36). Comparative modeling is employed as the knowledge-based method to predict the core region (Ala9-Asn28) of the protein while the remaining residues are built as extended regions (Met1-Lys8; Leu29-Phe36) which then further refined using Molecular Dynamics Simulation for 120 ns. Since the core region is built based on a high sequence identity to the template (65%) resulting in RMSD of 1.39 Å from the native, it is believed that this well-developed core region can act as a 'nucleation center' for subsequent rapid downhill folding. Results also demonstrate that the formation of the non-native contact which tends to hamper folding rate can be avoided. The best 3D model that exhibits most of the native characteristics is identified using clustering method which then further ranked based on the conformational free energies. It is found that the backbone RMSD of the best model compared to the NMR-MDavg is 1.01 Å and 3.53 Å, for the core region and the complete protein, respectively. In addition to this, the conformational free energy of the best model is lower by 5.85 kcal/mol as compared to the NMR-MDavg. This structure prediction protocol is shown to be effective in predicting the 3D structure of small globular protein with a considerable accuracy in much shorter time compared to the conventional Molecular Dynamics simulation alone.
", keywords = "3D model, Chicken villin headpiece subdomain,Molecular dynamic simulation NMR-MDavg, RMSD.", volume = "5", number = "4", pages = "255-6", }
