Molecular simulations of cellular processes
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It is, nowadays, possible to simulate biological processes in conditions that mimic the different cellular compartments. Several groups have performed these calculations using molecular models that vary in performance and accuracy. In many cases, the atomistic degrees of freedom have been eliminated, sacrificing both structural complexity and chemical specificity to be able to explore slow processes. In this review, we will discuss the insights gained from computer simulations on macromolecule diffusion, nuclear body formation, and processes involving the genetic material inside cell-mimicking spaces. We will also discuss the challenges to generate new models suitable for the simulations of biological processes on a cell scale and for cell-cycle-long times, including non-equilibrium events such as the co-translational folding, misfolding, and aggregation of proteins. A prominent role will be played by the wise choice of the structural simplifications and, simultaneously, of a relatively complex energetic description. These challenging tasks will rely on the integration of experimental and computational methods, achieved through the application of efficient algorithms.
KeywordsMacromolecular crowding Coarse-graining Molecular dynamics Stochastic processes Diffusion in the cytoplasm Sub-diffusion Nuclear bodies Genetic material Facilitated diffusion Soft interactions Hydrodynamic interactions Integrative modeling
Compliance with ethical standards
Conflict of interest
Fabio Trovato declares that he has no conflict of interest. Giordano Fumagalli declares that he has no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Acharya S, Nandi MK, Mandal A, Sarkar S, Bhattacharyya SM (2015) Diffusion of small solute particles in viscous liquids: cage diffusion, a result of decoupling of solute–solvent dynamics, leads to amplification of solute diffusion. J Phys Chem B 119:11169–11175. https://doi.org/10.1021/acs.jpcb.5b03034 CrossRefPubMedGoogle Scholar
- Dix JA, Verkman AS (2008) Crowding effects on diffusion in solutions and cells. Annu Rev Biophys 37:247–263. https://doi.org/10.1146/annurev.biophys.37.032807.125824 CrossRefPubMedGoogle Scholar
- Fan J, Tuncay K, Ortoleva PJ (2007) Chromosome segregation in Escherichia coli division: a free energy-driven string model. Comput Biol Chem 31:257–264. https://doi.org/10.1016/j.compbiolchem.2007.05.003 CrossRefPubMedGoogle Scholar
- Hasnain S, McClendon CL, Hsu MT, Jacobson MP, Bandyopadhyay P (2014) A new coarse-grained model for E. coli cytoplasm: accurate calculation of the diffusion coefficient of proteins and observation of anomalous diffusion. PLoS One 9:e106466. https://doi.org/10.1371/journal.pone.0106466 CrossRefPubMedPubMedCentralGoogle Scholar
- Israelachvili JN (1992) Intermolecular and surface forces. Academic Press, LondonGoogle Scholar
- Kim JS, Szleifer I (2014) Crowding-induced formation and structural alteration of nuclear compartments: insights from computer simulations. Int Rev Cell Mol Biol 307:73–108. https://doi.org/10.1016/B978-0-12-800046-5.00004-7 CrossRefPubMedGoogle Scholar
- Luo X-D, Kong F-L, Dang H-B, Chen J, Liang Y (2016) Macromolecular crowding favors the fibrillization of β2-microglobulin by accelerating the nucleation step and inhibiting fibril disassembly. Biochim Biophys Acta 1864:1609–1619. https://doi.org/10.1016/j.bbapap.2016.07.012 CrossRefPubMedGoogle Scholar
- Minton AP (2017) Explicit incorporation of hard and soft protein–protein interactions into models for crowding effects in protein mixtures. 2. Effects of varying hard and soft interactions upon prototypical chemical equilibria. J Phys Chem B 121:5515–5522. https://doi.org/10.1021/acs.jpcb.7b02378 CrossRefPubMedPubMedCentralGoogle Scholar
- Okazaki K, Koga N, Takada S, Onuchic JN, Wolynes PG (2006) Multiple-basin energy landscapes for large-amplitude conformational motions of proteins: structure-based molecular dynamics simulations. Proc Natl Acad Sci U S A 103:11844–11849. https://doi.org/10.1073/pnas.0604375103 CrossRefPubMedPubMedCentralGoogle Scholar
- Roggiani M, Goulian M (2015) Chromosome–membrane interactions in bacteria. Annu Rev Genet 49:115–129. https://doi.org/10.1146/annurev-genet-112414-054958 CrossRefPubMedGoogle Scholar
- Rosa A, Zimmer C (2014) Computational models of large-scale genome architecture. Int Rev Cell Mol Biol 307:275–349. https://doi.org/10.1016/B978-0-12-800046-5.00009-6 CrossRefPubMedGoogle Scholar
- Stefferson MW, Norris SL, Vernerey FJ, Betterton MD, Hough LE (2017) Effects of soft interactions and bound mobility on diffusion in crowded environments: a model of sticky and slippery obstacles. Phys Biol 14:045008. https://doi.org/10.1088/1478-3975/aa7869 CrossRefPubMedPubMedCentralGoogle Scholar
- Trovato F, O’Brien EP (2016) Insights into cotranslational nascent protein behavior from computer simulations. Annu Rev Biophys 45:345–369. https://doi.org/10.1146/annurev-biophys-070915-094153 CrossRefPubMedGoogle Scholar
- Xia T, Li N, Fang X (2013) Single-molecule fluorescence imaging in living cells. Annu Rev Phys Chem 64:459–480. https://doi.org/10.1146/annurev-physchem-040412-110127 CrossRefPubMedGoogle Scholar
- Zhou H-X, Rivas G, Minton AP (2008) Macromolecular crowding and confinement: biochemical, biophysical, and potential physiological consequences. Annu Rev Biophys 37:375–397. https://doi.org/10.1146/annurev.biophys.37.032807.125817 CrossRefPubMedPubMedCentralGoogle Scholar