Simulating a chemically fueled molecular motor with nonequilibrium molecular dynamics.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
22 04 2022
22 04 2022
Historique:
received:
25
02
2021
accepted:
23
02
2022
entrez:
23
4
2022
pubmed:
24
4
2022
medline:
27
4
2022
Statut:
epublish
Résumé
Most computer simulations of molecular dynamics take place under equilibrium conditions-in a closed, isolated system, or perhaps one held at constant temperature or pressure. Sometimes, extra tensions, shears, or temperature gradients are introduced to those simulations to probe one type of nonequilibrium response to external forces. Catalysts and molecular motors, however, function based on the nonequilibrium dynamics induced by a chemical reaction's thermodynamic driving force. In this scenario, simulations require chemostats capable of preserving the chemical concentrations of the nonequilibrium steady state. We develop such a dynamic scheme and use it to observe cycles of a particle-based classical model of a catenane-like molecular motor. Molecular motors are frequently modeled with detailed-balance-breaking Markov models, and we explicitly construct such a picture by coarse graining the microscopic dynamics of our simulations in order to extract rates. This work identifies inter-particle interactions that tune those rates to create a functional motor, thereby yielding a computational playground to investigate the interplay between directional bias, current generation, and coupling strength in molecular information ratchets.
Identifiants
pubmed: 35459863
doi: 10.1038/s41467-022-29393-3
pii: 10.1038/s41467-022-29393-3
pmc: PMC9033874
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
2204Commentaires et corrections
Type : ErratumIn
Informations de copyright
© 2022. The Author(s).
Références
Howard, J., Hudspeth, A. & Vale, R. Movement of microtubules by single kinesin molecules. Nature 342, 154–158 (1989).
doi: 10.1038/342154a0
Finer, J. T., Simmons, R. M. & Spudich, J. A. Single myosin molecule mechanics: piconewton forces and nanometre steps. Nature 368, 113–119 (1994).
doi: 10.1038/368113a0
Brown, A. I. & Sivak, D. A. Theory of nonequilibrium free energy transduction by molecular machines. Chem. Rev. 120, 434–459 (2019).
doi: 10.1021/acs.chemrev.9b00254
Kolomeisky, A. B. & Fisher, M. E. Molecular motors: a theorist’s perspective. Annu. Rev. Phys. Chem. 58, 675–695 (2007).
doi: 10.1146/annurev.physchem.58.032806.104532
Jülicher, F., Ajdari, A. & Prost, J. Modeling molecular motors. Rev. Mod. Phys. 69, 1269 (1997).
doi: 10.1103/RevModPhys.69.1269
Mugnai, M. L., Hyeon, C., Hinczewski, M. & Thirumalai, D. Theoretical perspectives on biological machines. Rev. Mod. Phys. 92, 025001 (2020).
doi: 10.1103/RevModPhys.92.025001
Vale, R. D. et al. Direct observation of single kinesin molecules moving along microtubules. Nature 380, 451–453 (1996).
doi: 10.1038/380451a0
Thorn, K. S., Ubersax, J. A. & Vale, R. D. Engineering the processive run length of the kinesin motor. J. Cell Biol. 151, 1093–1100 (2000).
doi: 10.1083/jcb.151.5.1093
Engelke, M. F. et al. Engineered kinesin motor proteins amenable to small-molecule inhibition. Nat. Commun. 7, 1–12 (2016).
doi: 10.1038/ncomms11159
Bryant, Z., Altman, D. & Spudich, J. A. The power stroke of myosin VI and the basis of reverse directionality. Proc. Natl Acad. Sci. USA. 104, 772–777 (2007).
doi: 10.1073/pnas.0610144104
Liao, J.-C., Elting, M. W., Delp, S. L., Spudich, J. A. & Bryant, Z. Engineered myosin VI motors reveal minimal structural determinants of directionality and processivity. J. Mol. Biol. 392, 862–867 (2009).
doi: 10.1016/j.jmb.2009.07.046
Kelly, T. R., Tellitu, I. & Sestelo, J. P. In search of molecular ratchets. Angew. Chem. Int. Ed. 36, 1866–1868 (1997).
doi: 10.1002/anie.199718661
Kelly, T. R., De Silva, H. & Silva, R. A. Unidirectional rotary motion in a molecular system. Nature 401, 150–152 (1999).
doi: 10.1038/43639
Kelly, T. R. et al. Progress toward a rationally designed, chemically powered rotary molecular motor. J. Am. Chem. Soc. 129, 376–386 (2007).
doi: 10.1021/ja066044a
Wilson, M. R. et al. An autonomous chemically fuelled small-molecule motor. Nature 534, 235–240 (2016).
doi: 10.1038/nature18013
Bustamante, C., Keller, D. & Oster, G. The physics of molecular motors. Acc. Chem. Res. 34, 412–420 (2001).
doi: 10.1021/ar0001719
Astumian, R. D. Design principles for Brownian molecular machines: how to swim in molasses and walk in a hurricane. Phys. Chem. Chem. Phys. 9, 5067–5083 (2007).
doi: 10.1039/b708995c
Frenkel, D. & Smit, B. Understanding Molecular Simulation: From Algorithms to Applications, vol. 1 (Elsevier, 2001).
Astumian, R. D. Microscopic reversibility as the organizing principle of molecular machines. Nat. Nanotechnol. 7, 684–688 (2012).
doi: 10.1038/nnano.2012.188
Fang, X., Kruse, K., Lu, T. & Wang, J. Nonequilibrium physics in biology. Rev. Mod. Phys. 91, 045004 (2019).
doi: 10.1103/RevModPhys.91.045004
Koga, N. & Takada, S. Folding-based molecular simulations reveal mechanisms of the rotary motor F1–ATPase. Proc. Natl Acad. Sci. USA. 103, 5367–5372 (2006).
doi: 10.1073/pnas.0509642103
Isaka, Y. et al. Rotation mechanism of molecular motor V1-ATPase studied by multiscale molecular dynamics simulation. Biophysical J. 112, 911–920 (2017).
doi: 10.1016/j.bpj.2017.01.029
Togashi, Y. & Mikhailov, A. S. Nonlinear relaxation dynamics in elastic networks and design principles of molecular machines. Proc. Natl Acad. Sci. USA. 104, 8697–8702 (2007).
doi: 10.1073/pnas.0702950104
Huang, M.-J., Kapral, R., Mikhailov, A. S. & Chen, H.-Y. Coarse-grain simulations of active molecular machines in lipid bilayers. J. Chem. Phys. 138, 195101 (2013).
doi: 10.1063/1.4803507
Cressman, A., Togashi, Y., Mikhailov, A. S. & Kapral, R. Mesoscale modeling of molecular machines: cyclic dynamics and hydrodynamical fluctuations. Phys. Rev. E 77, 050901 (2008).
doi: 10.1103/PhysRevE.77.050901
Mukherjee, S., Alhadeff, R. & Warshel, A. Simulating the dynamics of the mechanochemical cycle of myosin-V. Proc. Natl Acad. Sci. USA. 114, 2259–2264 (2017).
doi: 10.1073/pnas.1700318114
Craig, E. M. & Linke, H. Mechanochemical model for myosin V. Proc. Natl Acad. Sci. USA. 106, 18261–18266 (2009).
doi: 10.1073/pnas.0908192106
Okazaki, K.-i. & Hummer, G. Phosphate release coupled to rotary motion of F1-ATPase. Proc. Natl Acad. Sci. USA. 110, 16468–16473 (2013).
doi: 10.1073/pnas.1305497110
Okazaki, K.-i. & Hummer, G. Elasticity, friction, and pathway of γ-subunit rotation in FoF1-ATP synthase. Proc. Natl Acad. Sci. USA. 112, 10720–10725 (2015).
doi: 10.1073/pnas.1500691112
Nam, K., Pu, J. & Karplus, M. Trapping the ATP binding state leads to a detailed understanding of the F1-ATPase mechanism. Proc. Natl Acad. Sci. USA. 111, 17851–17856 (2014).
doi: 10.1073/pnas.1419486111
Pu, J. & Karplus, M. How subunit coupling produces the γ-subunit rotary motion in F1-ATPase. Proc. Natl Acad. Sci. USA. 105, 1192–1197 (2008).
doi: 10.1073/pnas.0708746105
Dai, L., Flechsig, H. & Yu, J. Deciphering intrinsic inter-subunit couplings that lead to sequential hydrolysis of F1-ATPase ring. Biophysical J. 113, 1440–1453 (2017).
doi: 10.1016/j.bpj.2017.08.015
Czub, J., Wieczór, M., Prokopowicz, B. & Grubmüller, H. Mechanochemical energy transduction during the main rotary step in the synthesis cycle of F1-ATPase. J. Am. Chem. Soc. 139, 4025–4034 (2017).
doi: 10.1021/jacs.6b11708
Amano, S., Borsley, S., Leigh, D. A. & Sun, Z. Chemical engines: driving systems away from equilibrium through catalyst reaction cycles. Nat. Nanotechnol. 16, 1057–1067 (2021).
Xiao, Q., Chen, Y., Bereau, T. & Shi, Y. An in-silico walker. Chem. Phys. Lett. 659, 6–9 (2016).
doi: 10.1016/j.cplett.2016.06.019
Rückner, G. & Kapral, R. Chemically powered nanodimers. Phys. Rev. Lett. 98, 150603 (2007).
doi: 10.1103/PhysRevLett.98.150603
Tao, Y.-G. & Kapral, R. Design of chemically propelled nanodimer motors. J. Chem. Phys. 128, 164518 (2008).
doi: 10.1063/1.2908078
Valadares, L. F. et al. Catalytic nanomotors: self-propelled sphere dimers. Small 6, 565–572 (2010).
doi: 10.1002/smll.200901976
Colberg, P. H., Reigh, S. Y., Robertson, B. & Kapral, R. Chemistry in motion: tiny synthetic motors. Acc. Chem. Res. 47, 3504–3511 (2014).
doi: 10.1021/ar5002582
Gerritsma, E. & Gaspard, P. Chemomechanical coupling and stochastic thermodynamics of the F1-ATPase molecular motor with an applied external torque. Biophysical Rev. Lett. 5, 163–208 (2010).
doi: 10.1142/S1793048010001214
Seifert, U. Stochastic thermodynamics of single enzymes and molecular motors. Eur. Phys. J. E 34, 1–11 (2011).
doi: 10.1140/epje/i2011-11026-7
Altaner, B., Wachtel, A. & Vollmer, J. Fluctuating currents in stochastic thermodynamics. II. Energy conversion and nonequilibrium response in kinesin models. Phys. Rev. E 92, 042133 (2015).
doi: 10.1103/PhysRevE.92.042133
Noji, H., Yasuda, R., Yoshida, M. & Kinosita, K. Direct observation of the rotation of F1-ATPase. Nature 386, 299–302 (1997).
doi: 10.1038/386299a0
Yasuda, R., Noji, H., Kinosita Jr, K. & Yoshida, M. F1-ATPase is a highly efficient molecular motor that rotates with discrete 120 steps. Cell 93, 1117–1124 (1998).
doi: 10.1016/S0092-8674(00)81456-7
Hoffmann, P. M. How molecular motors extract order from chaos (a key issues review). Rep. Prog. Phys. 79, 032601 (2016).
doi: 10.1088/0034-4885/79/3/032601
Astumian, R. D. Running on information. Nat. Nanotechnol. 11, 582–583 (2016).
doi: 10.1038/nnano.2016.98
Qiu, Y., Feng, Y., Guo, Q.-H., Astumian, R. D. & Stoddart, J. F. Pumps through the ages. Chem 6, 1952–1977 (2020).
doi: 10.1016/j.chempr.2020.07.009
Astumian, R. D. Thermodynamics and kinetics of a Brownian motor. Science 276, 917–922 (1997).
doi: 10.1126/science.276.5314.917
Bier, M. & Astumian, R. D. Biased Brownian motion as the operating principle for microscopic engines. Bioelectrochemistry Bioenerg. 39, 67–75 (1996).
doi: 10.1016/0302-4598(95)01833-6
Astumian, R. D., Mukherjee, S. & Warshel, A. The physics and physical chemistry of molecular machines. ChemPhysChem 17, 1719–1741 (2016).
doi: 10.1002/cphc.201600184
Gupta, A., Clark, L. A. & Snurr, R. Q. Grand canonical Monte Carlo simulations of nonrigid molecules: siting and segregation in silicalite zeolite. Langmuir 16, 3910–3919 (2000).
doi: 10.1021/la990756f
Chempath, S., Clark, L. A. & Snurr, R. Q. Two general methods for grand canonical ensemble simulation of molecules with internal flexibility. J. Chem. Phys. 118, 7635–7643 (2003).
doi: 10.1063/1.1562607
Biddle, J. W. & Gunawardena, J. Reversal symmetries for cyclic paths away from thermodynamic equilibrium. Phys. Rev. E 101, 062125 (2020).
doi: 10.1103/PhysRevE.101.062125
Puglisi, A., Pigolotti, S., Rondoni, L. & Vulpiani, A. Entropy production and coarse graining in Markov processes. J. Stat. Mech.: Theory Exp. 2010, P05015 (2010).
doi: 10.1088/1742-5468/2010/05/P05015
Esposito, M. Stochastic thermodynamics under coarse graining. Phys. Rev. E 85, 041125 (2012).
doi: 10.1103/PhysRevE.85.041125
Horowitz, J. M. & Gingrich, T. R. Thermodynamic uncertainty relations constrain non-equilibrium fluctuations. Nat. Phys. 16, 15–20 (2020).
doi: 10.1038/s41567-019-0702-6
Di Terlizzi, I. & Baiesi, M. Kinetic uncertainty relation. J. Phys. A: Math. Theor. 52, 02LT03 (2018).
doi: 10.1088/1751-8121/aaee34
Amano, S. et al. Insights from an information thermodynamics analysis of a synthetic molecular motor. Nat. Chem. https://doi.org/10.1038/s41557-022-00899-z (2022).
Angioletti-Uberti, S., Mognetti, B. M. & Frenkel, D. Theory and simulation of DNA-coated colloids: a guide for rational design. Phys. Chem. Chem. Phys. 18, 6373–6393 (2016).
doi: 10.1039/C5CP06981E
Weeks, J. D., Chandler, D. & Andersen, H. C. Role of repulsive forces in determining the equilibrium structure of simple liquids. J. Chem. Phys. 54, 5237–5247 (1971).
doi: 10.1063/1.1674820
Albaugh, A. & Gingrich, T. R. Estimating reciprocal partition functions to enable design space sampling. J. Chem. Phys. 153, 204102 (2020).
doi: 10.1063/5.0025358
Athènes, M. & Adjanor, G. Measurement of nonequilibrium entropy from space-time thermodynamic integration. J. Chem. Phys. 129, 024116 (2008).
doi: 10.1063/1.2953328
Fass, J. et al. Quantifying configuration-sampling error in Langevin simulations of complex molecular systems. Entropy 20, 318 (2018).
doi: 10.3390/e20050318