A Multiscale Computational Model for Simulating the Kinetics of Protein Complex Assembly
Proteins fulfill versatile biological functions by interacting with each other and forming high-order complexes. Although the order in which protein subunits assemble is important for the biological function of their final complex, this kinetic information has received comparatively little attention in recent years. Here we describe a multiscale framework that can be used to simulate the kinetics of protein complex assembly. There are two levels of models in the framework. The structural details of a protein complex are reflected by the residue-based model, while a lower-resolution model uses a rigid-body (RB) representation to simulate the process of complex assembly. These two levels of models are integrated together, so that we are able to provide the kinetic information about complex assembly with both structural details and computational efficiency.
Key wordsProtein complex assembly Multiscale modeling Coarse-grained simulation Protein association rate Kinetic Monte Carlo Diffusion-reaction algorithm
This work was supported in part by the National Institutes of Health (Grant No. R01GM120238) and a start-up grant from the Albert Einstein College of Medicine.
- 2.Levy ED, Teichmann S (2013) Structural, evolutionary, and assembly principles of protein oligomerization. Prog Mol Biol Transl Sci 117:25–51. https://doi.org/10.1016/b978-0-12-386931-9.00002-7 CrossRefPubMedGoogle Scholar
- 15.Frembgen-Kesner T, Elcock AH (2010) Absolute protein-protein association rate constants from flexible, coarse-grained Brownian dynamics simulations: the role of intermolecular hydrodynamic interactions in barnase-barstar association. Biophys J 99(9):L75–L77. https://doi.org/10.1016/j.bpj.2010.09.006 CrossRefPubMedPubMedCentralGoogle Scholar
- 27.Haddadian EJ, Gross EL (2006) A Brownian dynamics study of the interactions of the luminal domains of the cytochrome b6f complex with plastocyanin and cytochrome c6: the effects of the Rieske FeS protein on the interactions. Biophys J 91(7):2589–2600. https://doi.org/10.1529/biophysj.106.085936 CrossRefPubMedPubMedCentralGoogle Scholar
- 29.Ander M, Beltrao P, Di Ventura B, Ferkinghoff-Borg J, Foglierini M, Kaplan A, Lemerle C, Tomas-Oliveira I, Serrano L (2004) SmartCell, a framework to simulate cellular processes that combines stochastic approximation with diffusion and localisation: analysis of simple networks. Syst Biol (Stevenage) 1(1):129–138CrossRefGoogle Scholar
- 31.Stiles J, Bartol TM (2001) Monte Carlo methods for simulating realistic synaptic microphysiology using MCell. Computational Neuroscience:87–127Google Scholar
- 33.Ridgway D, Broderick G, Lopez-Campistrous A, Ru'aini M, Winter P, Hamilton M, Boulanger P, Kovalenko A, Ellison MJ (2008) Coarse-grained molecular simulation of diffusion and reaction kinetics in a crowded virtual cytoplasm. Biophys J 94(10):3748–3759. https://doi.org/10.1529/biophysj.107.116053 CrossRefPubMedPubMedCentralGoogle Scholar
- 35.Lee B, LeDuc PR, Schwartz R (2008) Stochastic off-lattice modeling of molecular self-assembly in crowded environments by Green's function reaction dynamics. Phys Rev E 78(3). https://doi.org/10.1103/PhysRevE.78.031911