Computational model explains high activity and rapid cycling of Rho GTPases within protein complexes.
Formation of multiprotein complexes on cellular membranes is critically dependent on the cyclic activation of small GTPases. FRAP-based analyses demonstrate that within protein complexes, some small GTPases cycle nearly three orders of magnitude faster than they would spontaneously cycle in vitro. At the same time, experiments report concomitant excess of the activated, GTP-bound form of GTPases over their inactive form. Intuitively, high activity and rapid turnover are contradictory requirements. How the cells manage to maximize both remains poorly understood. Here, using GTPases of the Rab and Rho families as a prototype, we introduce a computational model of the GTPase cycle. We quantitatively investigate several plausible layouts of the cycling control module that consist of GEFs, GAPs, and GTPase effectors. We explain the existing experimental data and predict how the cycling of GTPases is controlled by the regulatory proteins in vivo. Our model explains distinct and separable roles that the activating GEFs and deactivating GAPs play in the GTPase cycling control. While the activity of GTPase is mainly defined by GEF, the turnover rate is a sole function of GAP. Maximization of the GTPase activity and turnover rate places conflicting requirements on the concentration of GAP. Therefore, to achieve a high activity and turnover rate at once, cells must carefully maintain concentrations of GEFs and GAPs within the optimal range. The values of these optimal concentrations indicate that efficient cycling can be achieved only within dense protein complexes typically assembled on the membrane surfaces. We show that the concentration requirement for GEF can be dramatically reduced by a GEF-activating GTPase effector that can also significantly boost the cycling efficiency. Interestingly, we find that the cycling regimes are only weakly dependent on the concentration of GTPase itself.
Goryachev
Andrew B
AB
Systems Biology Group, Bioinformatics Institute, Singapore, Singapore. Andrew.Goryachev@ed.ac.uk
Pokhilko
Alexandra V
AV
D000595:Amino Acid Sequence; D003198:Computer Simulation; D004789:Enzyme Activation; D005246:Feedback / Q000502:physiology; D020690:GTPase-Activating Proteins / Q000737:chemistry / Q000378:metabolism*; D015971:Gene Expression Regulation, Enzymologic / Q000502:physiology; D006160:Guanosine Triphosphate / Q000737:chemistry / Q000378:metabolism*; D008954:Models, Biological; D008956:Models, Chemical; D008969:Molecular Sequence Data; D046912:Multiprotein Complexes / Q000737:chemistry / Q000378:metabolism; D015398:Signal Transduction / Q000502:physiology*; D020741:rho GTP-Binding Proteins / Q000737:chemistry / Q000378:metabolism*
D020690:GTPase-Activating Proteins; D046912:Multiprotein Complexes; D006160:Guanosine Triphosphate; D020741:rho GTP-Binding Proteins