Abstract
Ice-binding proteins (IBPs) are a diverse class of proteins that are essential for the survival of organisms in cold conditions. IBPs are diverse in their function and can prevent or promote ice growth and selectively bind to specific crystallographic planes of the growing ice lattice. Moreover, IBPs are widely utilized to modulate ice crystal growth and recrystallization in the food industry and as cryoprotectants to preserve biological matter. A key unresolved aspect of the mode of action is how the ice-binding sites of these proteins distinguish between ice and water and interact with multiple crystal facets of the ice. The use of molecular dynamics (MD) simulation allows us to thoroughly investigate the binding mechanism and energetics of ice-binding proteins, to complement and expand on the mechanistic understandings gained from experiments. In this chapter, we describe a series of molecular dynamics simulation methodologies to investigate the mechanism of action of ice-binding proteins. Specifically, we provide detailed instructions to set up MD simulations to study the binding and interaction of ice-binding proteins using atomistic and coarse-grained simulations.
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References
McCammon JA, Gelin BR, Karplus M (1977) Dynamics of folded proteins. Nature 267:585–590. https://doi.org/10.1038/267585a0
Karplus M, McCammon JA (2002) Molecular dynamics simulations of biomolecules. Nat Struct Biol 9:646–652. https://doi.org/10.1038/nsb0902-646
DeVries Arthur L, Wohlschlag Donald E (1969) Freezing resistance in some Antarctic fishes. Science 163:1073–1075. https://doi.org/10.1126/science.163.3871.1073
DeVries AL, Komatsu SK, Feeney RE (1970) Chemical and physical properties of freezing point-depressing glycoproteins from Antarctic fishes. J Biol Chem 245:2901–2908. https://doi.org/10.1016/S0021-9258(18)63073-X
Bar Dolev M, Braslavsky I, Davies PL (2016) Ice-binding proteins and their function. Annu Rev Biochem 85:515–542. https://doi.org/10.1146/annurev-biochem-060815-014546
Davies PL (2014) Ice-binding proteins: a remarkable diversity of structures for stopping and starting ice growth. Trends Biochem Sci 39:548–555. https://doi.org/10.1016/j.tibs.2014.09.005
Wolber PK et al (1986) Identification and purification of a bacterial ice-nucleation protein. Proc Natl Acad Sci 83:7256. https://doi.org/10.1073/pnas.83.19.7256
Hartmann S et al (2013) Immersion freezing of ice nucleation active protein complexes. Atmos Chem Phys 13:5751–5766. https://doi.org/10.5194/acp-13-5751-2013
Liou Y-C, Tocilj A, Davies PL, Jia Z (2000) Mimicry of ice structure by surface hydroxyls and water of a β-helix antifreeze protein. Nature 406:322–324. https://doi.org/10.1038/35018604
Leinala EK et al (2002) A β-helical antifreeze protein isoform with increased activity: structural and functional insights*. J Biol Chem 277:33349–33352. https://doi.org/10.1074/jbc.M205575200
Hakim A et al (2013) Crystal structure of an insect antifreeze protein and its implications for ice binding*. J Biol Chem 288:12295–12304. https://doi.org/10.1074/jbc.M113.450973
Garnham CP, Campbell RL, Davies PL (2011) Anchored clathrate waters bind antifreeze proteins to ice. Proc Natl Acad Sci 108:7363. https://doi.org/10.1073/pnas.1100429108
Hudait A, Odendahl N, Qiu Y, Paesani F, Molinero V (2018) Ice-nucleating and antifreeze proteins recognize ice through a diversity of anchored clathrate and ice-like motifs. J Am Chem Soc 140:4905–4912. https://doi.org/10.1021/jacs.8b01246
Hudait A et al (2018) Preordering of water is not needed for ice recognition by hyperactive antifreeze proteins. Proc Natl Acad Sci 115:8266. https://doi.org/10.1073/pnas.1806996115
Qiu Y, Hudait A, Molinero V (2019) How size and aggregation of ice-binding proteins control their ice nucleation efficiency. J Am Chem Soc 141:7439–7452. https://doi.org/10.1021/jacs.9b01854
Hudait A, Qiu Y, Odendahl N, Molinero V (2019) Hydrogen-bonding and hydrophobic groups contribute equally to the binding of hyperactive antifreeze and ice-nucleating proteins to ice. J Am Chem Soc 141:7887–7898. https://doi.org/10.1021/jacs.9b02248
Pandey R et al (2016) Ice-nucleating bacteria control the order and dynamics of interfacial water. Sci Adv 2:e1501630. https://doi.org/10.1126/sciadv.1501630
Graether SP, Jia Z (2001) Modeling pseudomonas syringae ice-nucleation protein as a beta-helical protein. Biophys J 80:1169–1173. https://doi.org/10.1016/S0006-3495(01)76093-6
MacKerell AD et al (1998) All-atom empirical potential for molecular modeling and dynamics studies of proteins. J Phys Chem B 102:3586–3616. https://doi.org/10.1021/jp973084f
MacKerell AD, Feig M, Brooks CL (2004) Improved treatment of the protein backbone in empirical force fields. J Am Chem Soc 126:698–699. https://doi.org/10.1021/ja036959e
Meister K et al (2015) Investigation of the ice-binding site of an insect antifreeze protein using sum-frequency generation spectroscopy. J Phys Chem Lett 6:1162–1167. https://doi.org/10.1021/acs.jpclett.5b00281
Odendahl N (2016) Comparison of popular force fields for molecular modelling of proteins applied to ice binding Tenebrio Molitor antifreeze proteins. B.S. in Chemistry Honors thesis, The University of Utah, Salt Lake City
Abascal JLF, Vega C (2005) A general purpose model for the condensed phases of water: TIP4P/2005. J Chem Phys 123:234505. https://doi.org/10.1063/1.2121687
Babin V, Leforestier C, Paesani F (2013) Development of a “first principles” water potential with flexible monomers: dimer potential energy surface, VRT spectrum, and second virial coefficient. J Chem Theory Comput 9:5395–5403. https://doi.org/10.1021/ct400863t
Babin V, Medders GR, Paesani F (2014) Development of a “first principles” water potential with flexible monomers. II: trimer potential energy surface, third virial coefficient, and small clusters. J Chem Theory Comput 10:1599–1607. https://doi.org/10.1021/ct500079y
Medders GR, Babin V, Paesani F (2014) Development of a “first-principles” water potential with flexible monomers. III. Liquid phase properties. J Chem Theory Comput 10:2906–2910. https://doi.org/10.1021/ct5004115
Horn HW et al (2004) Development of an improved four-site water model for biomolecular simulations: TIP4P-Ew. J Chem Phys 120:9665–9678. https://doi.org/10.1063/1.1683075
Berendsen HJC, Grigera JR, Straatsma TP (1987) The missing term in effective pair potentials. J Phys Chem 91:6269–6271. https://doi.org/10.1021/j100308a038
Abascal JLF, Sanz E, García Fernández R, Vega C (2005) A potential model for the study of ices and amorphous water: TIP4P/ice. J Chem Phys 122:234511. https://doi.org/10.1063/1.1931662
Case DA et al (2005) The Amber biomolecular simulation programs. J Comput Chem 26:1668–1688. https://doi.org/10.1002/jcc.20290
Berendsen HJC, van der Spoel D, van Drunen R (1995) GROMACS: a message-passing parallel molecular dynamics implementation. Comput Phys Commun 91:43–56. https://doi.org/10.1016/0010-4655(95)00042-E
Phillips JC et al (2020) Scalable molecular dynamics on CPU and GPU architectures with NAMD. J Chem Phys 153:044130. https://doi.org/10.1063/5.0014475
Smith W, Yong CW, Rodger PM (2002) DL_POLY: application to molecular simulation. Mol Simul 28:385–471. https://doi.org/10.1080/08927020290018769
Thompson AP et al (2022) LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales. Comput Phys Commun 271:108171. https://doi.org/10.1016/j.cpc.2021.108171
Moore EB, Molinero V (2011) Structural transformation in supercooled water controls the crystallization rate of ice. Nature 479:506–508. https://doi.org/10.1038/nature10586
Lupi L et al (2017) Role of stacking disorder in ice nucleation. Nature 551:218–222. https://doi.org/10.1038/nature24279
Hudait A, Molinero V (2014) Ice crystallization in ultrafine water–salt aerosols: nucleation, ice-solution equilibrium, and internal structure. J Am Chem Soc 136:8081–8093. https://doi.org/10.1021/ja503311r
Metya AK, Molinero V (2021) Is ice nucleation by organic crystals nonclassical? An assessment of the monolayer hypothesis of ice nucleation. J Am Chem Soc 143:4607–4624. https://doi.org/10.1021/jacs.0c12012
Hudait A, Molinero V (2016) What determines the ice polymorph in clouds? J Am Chem Soc 138:8958–8967. https://doi.org/10.1021/jacs.6b05227
Qiu Y, Molinero V (2018) Why is it so difficult to identify the onset of ice Premelting? J Phys Chem Lett 9:5179–5182. https://doi.org/10.1021/acs.jpclett.8b02244
Hudait A, Qiu S, Lupi L, Molinero V (2016) Free energy contributions and structural characterization of stacking disordered ices. Phys Chem Chem Phys 18:9544–9553. https://doi.org/10.1039/C6CP00915H
Molinero V, Moore EB (2009) Water modeled as an intermediate element between carbon and silicon. J Phys Chem B 113:4008–4016. https://doi.org/10.1021/jp805227c
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
Berendsen HJC, Postma JPM, van Gunsteren WF, DiNola A, Haak JR (1984) Molecular dynamics with coupling to an external bath. J Chem Phys 81:3684–3690. https://doi.org/10.1063/1.448118
Schneider T, Stoll E (1978) Molecular-dynamics study of a three-dimensional one-component model for distortive phase transitions. Phys Rev B 17:1302–1322. https://doi.org/10.1103/PhysRevB.17.1302
Grønbech-Jensen N, Farago O (2013) A simple and effective Verlet-type algorithm for simulating Langevin dynamics. Mol Phys 111:983–991. https://doi.org/10.1080/00268976.2012.760055
Ryckaert J-P, Ciccotti G, Berendsen HJC (1977) Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J Comput Phys 23:327–341. https://doi.org/10.1016/0021-9991(77)90098-5
Andersen HC (1983) Rattle: a “velocity” version of the shake algorithm for molecular dynamics calculations. J Comput Phys 52:24–34. https://doi.org/10.1016/0021-9991(83)90014-1
Darden T, York D, Pedersen L (1993) Particle mesh Ewald: an N·log(N) method for Ewald sums in large systems. J Chem Phys 98:10089–10092. https://doi.org/10.1063/1.464397
Kolafa J (2004) Time-reversible always stable predictor–corrector method for molecular dynamics of polarizable molecules. J Comput Chem 25:335–342. https://doi.org/10.1002/jcc.10385
Parrinello M, Rahman A (1981) Polymorphic transitions in single crystals: a new molecular dynamics method. J Appl Phys 52:7182–7190. https://doi.org/10.1063/1.328693
Martyna GJ, Tobias DJ, Klein ML (1994) Constant pressure molecular dynamics algorithms. J Chem Phys 101:4177–4189. https://doi.org/10.1063/1.467468
Stillinger FH, Weber TA (1985) Computer simulation of local order in condensed phases of silicon. Phys Rev B 31:5262–5271. https://doi.org/10.1103/PhysRevB.31.5262
Fileti EE, Chaudhuri P, Canuto S (2004) Relative strength of hydrogen bond interaction in alcohol–water complexes. Chem Phys Lett 400:494–499. https://doi.org/10.1016/j.cplett.2004.10.149
Jacobson LC, Hujo W, Molinero V (2010) Amorphous precursors in the nucleation of clathrate hydrates. J Am Chem Soc 132:11806–11811. https://doi.org/10.1021/ja1051445
Kamberaj H, Low RJ, Neal MP (2005) Time reversible and symplectic integrators for molecular dynamics simulations of rigid molecules. J Chem Phys 122:224114. https://doi.org/10.1063/1.1906216
Miller TF et al (2002) Symplectic quaternion scheme for biophysical molecular dynamics. J Chem Phys 116:8649–8659. https://doi.org/10.1063/1.1473654
Torrie GM, Valleau JP (1977) Nonphysical sampling distributions in Monte Carlo free-energy estimation: umbrella sampling. J Comput Phys 23:187–199. https://doi.org/10.1016/0021-9991(77)90121-8
Steinhardt PJ, Nelson DR, Ronchetti M (1983) Bond-orientational order in liquids and glasses. Phys Rev B 28:784–805. https://doi.org/10.1103/PhysRevB.28.784
Grossfield A, WHAM; version 2.0.6, http://membrane.urmc.rochester.edu/content/wham
Errington JR, Debenedetti PG (2001) Relationship between structural order and the anomalies of liquid water. Nature 409:318–321. https://doi.org/10.1038/35053024
Acknowledgments
The author gratefully acknowledges discussions with Prof. Valeria Molinero, Dr. Yuqing Qiu, Prof. Francesco Paesani, and Dr. Daniel Moberg. The simulation trajectories were generated using the supercomputing resources and computer time provided by the Center for High Performance at the University of Utah.
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Hudait, A. (2024). Multiscale Molecular Dynamics Simulations of Ice-Binding Proteins. In: Drori, R., Stevens, C. (eds) Ice Binding Proteins. Methods in Molecular Biology, vol 2730. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3503-2_13
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