Globular bundles and entangled network of proteins (CorA) by a coarse-grained Monte Carlo simulation

Warin Rangubpit; Sunan Kitjaruwankul; Panisak Boonamnaj; Pornthep Sompornpisut; R.B. Pandey; Warin Rangubpit; Sunan Kitjaruwankul; Panisak Boonamnaj; Pornthep Sompornpisut; R.B. Pandey

doi:10.3934/biophy.2019.2.68

AIMS Biophysics

2019, Volume 6, Issue 2: 68-82. doi: 10.3934/biophy.2019.2.68

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Research article Topical Sections

Globular bundles and entangled network of proteins (CorA) by a coarse-grained Monte Carlo simulation

1.
Center of Excellence in Computational Chemistry, Department of Chemistry, Chulalongkorn University, Bangkok 10330, Thailand
2.
Faculty of Science at Sriracha, Kasetsart University Sriracha Campus, Chonburi 20230, Thailand
3.
Department of Physics and Astronomy, University of Southern Mississippi, Hattiesburg, MS 39406, USA

Received: 26 April 2019 Accepted: 24 June 2019 Published: 18 July 2019

Using a coarse-grained model, self-organized assembly of proteins (e.g., CorA and its inner segment iCorA) is studied by examining quantities such as contact profile, radius of gyration, and structure factor as a function of protein concentration at a range of low (native phase) to high (denature phase) temperatures. Visual inspections show distinct structures, i.e., isolated globular bundles to entangled network on multiple length scales in dilute to crowded protein concentrations. In native phase, the radius of gyration of the protein does not vary much with the protein concentration while that of its inner segment increases systematically. In contrast, the radius of gyration of the protein shows enormous growth with the concentration due to entanglement while that of the inner segment remains almost constant in denatured phase. The multi-scale morphology of the collective assembly is quantified by estimating the effective dimension D of protein from scaling of the structure factor: collective assembly from inner segments remains globular (D~3) at almost all length scales in its native phase while that from protein chains shows sparsely distributed morphology with D ≤ 2 in entire temperature range due to entanglement except in crowded environment at low temperature where D~2.6. Higher morphological response of chains with only the inner-segments due to selective interactions in its native phase may be more conducive to self-organizing mechanism than that of the remaining segments of the protein chains.
- Self-organizing structure of proteins (CorA),
- protein folding,
- Coarse-grained model,
- Monte Carlo simulation,
- effective dimension of self-assembly
Citation: Warin Rangubpit, Sunan Kitjaruwankul, Panisak Boonamnaj, Pornthep Sompornpisut, R.B. Pandey. Globular bundles and entangled network of proteins (CorA) by a coarse-grained Monte Carlo simulation[J]. AIMS Biophysics, 2019, 6(2): 68-82. doi: 10.3934/biophy.2019.2.68

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Abstract

Using a coarse-grained model, self-organized assembly of proteins (e.g., CorA and its inner segment iCorA) is studied by examining quantities such as contact profile, radius of gyration, and structure factor as a function of protein concentration at a range of low (native phase) to high (denature phase) temperatures. Visual inspections show distinct structures, i.e., isolated globular bundles to entangled network on multiple length scales in dilute to crowded protein concentrations. In native phase, the radius of gyration of the protein does not vary much with the protein concentration while that of its inner segment increases systematically. In contrast, the radius of gyration of the protein shows enormous growth with the concentration due to entanglement while that of the inner segment remains almost constant in denatured phase. The multi-scale morphology of the collective assembly is quantified by estimating the effective dimension D of protein from scaling of the structure factor: collective assembly from inner segments remains globular (D~3) at almost all length scales in its native phase while that from protein chains shows sparsely distributed morphology with D ≤ 2 in entire temperature range due to entanglement except in crowded environment at low temperature where D~2.6. Higher morphological response of chains with only the inner-segments due to selective interactions in its native phase may be more conducive to self-organizing mechanism than that of the remaining segments of the protein chains.

Acknowledgments

This research has been supported by the Ratchadaphiseksomphot Endowment Fund, Chulalongkorn University to PS, the Chulalongkorn university dusadi phipat scholarship to WR. Support from the Chulalongkorn University for a visiting professorship is gratefully acknowledged by RBP along with the warm hospitality by the Department of Chemistry. The authors acknowledge HPC at The University of Southern Mississippi supported by the National Science Foundation under the Major Research Instrumentation (MRI) program via Grant # ACI 1626217.

Conflict of interest

We do not have conflict of interest.

References

[1]	Furukawa Y, Nukina N (2013) Functional diversity of protein fibrillar aggregates from physiology to RNA granules to neurodegenerative diseases. Biochim Biophys Acta 1832: 1271–1278. doi: 10.1016/j.bbadis.2013.04.011
[2]	Bai Y, Luo Q, Liu J (2016) Protein self-assembly via supramolecular strategies. Chem Soc Rev 45: 2756–2767. doi: 10.1039/C6CS00004E
[3]	McManus JJ, Charbonneau P, Zaccarelli E, et al. (2016) The physics of protein self-assembly. Curr Opin Colloid Interface Sci 22: 73–79. doi: 10.1016/j.cocis.2016.02.011
[4]	Sgarbossa A (2012) Natural biomolecules protein aggregation: Emerging strategies against amyloidogenesis. Int J Mol Sci 13: 17121–17137. doi: 10.3390/ijms131217121
[5]	Sun H, Luo Q, Hou C, et al. (2017) Nanostructures based on protein self-assembly: From hierarchical construction to bioinspired materials. Nano Today 14: 16–41. doi: 10.1016/j.nantod.2017.04.006
[6]	Garcia-Seisdedos H, Empereur-Mot C, Elad N, et al. (2017) Proteins evolve on the edge of supramolecular self-assembly. Nature 548: 244–247. doi: 10.1038/nature23320
[7]	Pandey RB, Farmer BL, Gerstman BS (2015) Self-assembly dynamics for the transition of a globular aggregate to a fibril network of lysozyme proteins via a coarse-grained Monte Carlo simulation. AIP Adv 5.
[8]	Yang L, Liu A, Cao S, et al. (2016) Self-Assembly of proteins: Towards supramolecular materials. Chem Eur J 22: 15570–15582. doi: 10.1002/chem.201601943
[9]	Hmiel SP, Snavely MD, Florer JB, et al. (1989) Magnesium transport in Salmonella typhimurium: genetic characterization and cloning of three magnesium transport loci. J Bacteriol 171: 4742–4751. doi: 10.1128/jb.171.9.4742-4751.1989
[10]	Maguire ME (1992) MgtA and MgtB: prokaryotic P-type ATPases that mediate Mg²⁺ influx. J Bioenerg Biomembr 24: 319–328.
[11]	Kehres DG, Lawyer CH, Maguire ME (1998) The CorA magnesium transporter gene family. Microb Comp Genomics 3: 151–169. doi: 10.1089/omi.1.1998.3.151
[12]	Eshaghi S, Niegowski D, Kohl A, et al. (2006) Crystal structure of a divalent metal ion transporter CorA at 2.9 angstrom resolution. Science 313: 354–357.
[13]	Lunin VV, Dobrovetsky E, Khutoreskaya G, et al. (2006) Crystal structure of the CorA Mg²⁺ transporter. Nature 440: 833–837. doi: 10.1038/nature04642
[14]	Payandeh J, Li C, Ramjeesingh M, et al. (2008) Probing structure-function relationships and gating mechanisms in the CorA Mg²⁺ transport system. J Biol Chem 283: 11721–11733. doi: 10.1074/jbc.M707889200
[15]	Payandeh J, Pai EF (2006) A structural basis for Mg²⁺ homeostasis and the CorA translocation cycle. EMBO J 25: 3762–3773. doi: 10.1038/sj.emboj.7601269
[16]	Dalmas O, Cuello LG, Jogini V, et al. (2010) Structural Dynamics of the Magnesium-bound Conformation of CorA in a lipid bilayer. Structure 18: 868–878. doi: 10.1016/j.str.2010.04.009
[17]	Dalmas O, Sompornpisut P, Bezanilla F, et al. (2014) Molecular mechanism of Mg²⁺-dependent gating in CorA. Nat Commun 5: 3590. doi: 10.1038/ncomms4590
[18]	Neale C, Chakrabarti N, Pomorski P, et al. (2015) Hydrophobic gating of ion permeation in magnesium channel CorA. Plos Comput Biol 11: e1004303. doi: 10.1371/journal.pcbi.1004303
[19]	Kitjaruwankul S, Wapeesittipan P, Boonamnaj P, et al. (2016) Inner and outer coordination shells of Mg²⁺ in CorA selectivity filter from Molecular Dynamics simulations. J Phys Chem B 120: 406–417. doi: 10.1021/acs.jpcb.5b10925
[20]	Matthies D, Dalmas O, Borgnia MJ, et al. (2016) Cryo-EM structures of the magnesium channel CorA reveal symmetry break upon gating. Cell 164: 747–756. doi: 10.1016/j.cell.2015.12.055
[21]	Chakrabarti N, Neale C, Payandeh J, et al. (2010) An iris-like mechanism of pore dilation in the CorA magnesium transport system. Biophys J 98: 784–792. doi: 10.1016/j.bpj.2009.11.009
[22]	Nordin N, Guskov A, Phua T, et al. (2013) Exploring the structure and function of Thermotoga maritima CorA reveals the mechanism of gating and ion selectivity in Co²⁺/Mg²⁺ transport. Biochem J 451: 365–374. doi: 10.1042/BJ20121745
[23]	Kitjaruwankul S, Khrutto C, Sompornpisut P, et al. (2016) Asymmetry in structural response of inner and outer transmembrane segments of CorA protein by a coarse-grain model. J Chem Phys 145: 135101. doi: 10.1063/1.4963807
[24]	Kitjaruwankul S, Boonamnaj P, Paudel SS, et al. (2018) Thermal-induced folding and unfolding of a transmembrane protein (CorA). Physica A 506: 987–992. doi: 10.1016/j.physa.2018.05.014
[25]	Munishkina LA, Ahmad A, Fink AL, et al. (2008) Guiding protein aggregation with macromolecular crowding. Biochemistry 47: 8993–9006. doi: 10.1021/bi8008399
[26]	Minton AP (2001) The influence of macromolecular crowding and macromolecular confinement on biochemical reactions in physiological media. J Biol Chem 276: 10577–10580. doi: 10.1074/jbc.R100005200
[27]	Ellis RJ (2001) Macromolecular crowding: an important but neglected aspect of the intracellular environment. Curr Opin Struct Biol 11: 114–119. doi: 10.1016/S0959-440X(00)00172-X
[28]	Alas SJ, González-Pérez PP, Beltrán HI (2019) In silico minimalist approach to study 2D HP protein folding into an inhomogeneous space mimicking osmolyte effect: First trial in the search of foldameric backbones. BioSystems 181: 31–43. doi: 10.1016/j.biosystems.2019.04.005
[29]	González-Pérez PP, Orta DJ, Pena I, et al. (2017) A computational approach to studying protein folding problems considering the crucial role of the intracellular environment. J Comput Biol 24: 995–1013. doi: 10.1089/cmb.2016.0115
[30]	Tsao D, Dokholyan NV (2010) Macromolecular crowding induces polypeptide com paction and decreases folding cooperativity. Phys Chem Chem Phys 12: 3491–3500. doi: 10.1039/b924236h
[31]	Ping G, Yuan JM, Vallieres M, et al. (2003) Effects of confinement on protein folding and protein stability. J Chem Phys 118: 8042–8048. doi: 10.1063/1.1564053
[32]	Kuznetsova I, Zaslavsky B, Breydo L, et al. (2015) Beyond the excluded volume effects: mechanistic complexity of the crowded milieu. Molecules 20: 1377–1409. doi: 10.3390/molecules20011377
[33]	Binder K (1995) Monte Carlo and Molecular Dynamics Simulations in Polymer Science. Oxford University Press.
[34]	Pandey RB, Farmer BL (2014) Aggregation and network formation in self-assembly of protein (H3.1) by a coarse-grained Monte Carlo simulation. J Chem Phys 141.
[35]	Betancourt MR, Thirumalai D. (1999) Pair potentials for protein folding: choice of reference states and sensitivity of predicted native states to variations in the interaction schemes. Protein Sci 2:361–369.
[36]	Miyazawa S, Jernigan RL (1985) Estimation of effective inter residue contact energies from protein crystal structures: quasi-chemical approximation. Macromolecules 18:534–552. doi: 10.1021/ma00145a039
[37]	Miyazawa S, Jernigan RL (1996) Residue-residue potentials with a favorable contact pair term for simulation and treading. J Mol Biol 256: 623–644. doi: 10.1006/jmbi.1996.0114
[38]	Tanaka S, Scheraga HA. (1976) Medium and long range interaction parameters between amino acids for predicting three dimensional structures of proteins. Macromolecules 9: 945–950. doi: 10.1021/ma60054a013
[39]	Godzik A (1996) Knowledge-based potentials for protein folding: what can we learn from protein structures? Structure 4: 363–366. doi: 10.1016/S0969-2126(96)00041-X
[40]	Huang SY, Zou X. (2011) Statistical mechanics-based method to extract atomic distance-dependent potentials from protein structures. Proteins 79: 2648–2661. doi: 10.1002/prot.23086
[41]	Pandey RB, Kuang Z, Farmer BL, et al. (2012) Stability of peptide (P1, P2) binding to a graphene sheet via an all-atom to all-residue coarse-grained approach. Soft Matter 8: 9101–9109. doi: 10.1039/c2sm25870f
[42]	Feng J, Pandey RB, Berry RJ, et al. (2011) Adsorption mechanism of single amino acid and surfactant molecules to Au {111} surfaces in aqueous solution: design rules for metal binding molecules. Soft Matter 7: 2113–2120. doi: 10.1039/c0sm01118e

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