ESTIMATION OF IN VIVO FOLDING PARAMETERS OF A NATIVE ESCHERICHIA COLI PROTEIN USING A COMBINATION OF COMPUTATION AND IN VIVO EXPERIMENTS
Date
2016-05-13
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Abstract
It has become increasingly clear that the process of proper protein folding in the cell cannot be taken for granted. In fact 20-30% of the cell’s volume contains biomacromolecules with proteins as the major component. A concentration of macromolecules up to 0.3 to 0.4 g/mL such as in Escherichiacoli (Zimmerman & Trach, 1991) presents an environment radically different from the much-studied in vitro systems. First, the physical interactions among these biomolecules are much more pronounced than in the dilute in vitro
conditions. Second, the crowded cellular environment may lead to aberrant contacts among proteins, leading to consequences such as misfolding or aggregation, which underlie various types of diseases. A proteostasis network, comprising of biochemical pathways involving chaperone systems and degrading systems, maintains the relatively normal, healthy state of proteins in a cell. The fact that a chaperone may associate with multiple different clients with varying levels of affinity and that different components of the proteostasis network may work synergistically implies its complexity. Understanding such complexity requires a holistic and systems-level approach.
In vitro studies of proteins such as enzymes often characterize the molecule by a set of thermodynamic and kinetic constants including rate constants and equilibrium constants for processes such as folding, misfolding
or aggregation. However, as mentioned above, due to the highly crowded cellular environment, these in vitro parameters are unlikely to encapsulate the molecule’s in vivo behavior. Using S-adenosylmethionine synthase (MetK) - a native E.coli protein - as a model, we propose a protocol to estimate the in vivo kinetic and thermodynamic parameters of protein folding. We propose that by varying the amounts of MetK and of the components of the proteostasis network, we would be able to construct a parameter space of the
amount of folded MetK as a function of its expression level and that of proteostasis network components. Incorporating this information into a computational model of proteostasis network in E.coli called FoldEco (Powers et al., 2012), we were able to estimate the folding parameters for MetK in vivo and compare with those from in vitro experiments. Our study highlighted the use of computational modeling in understanding biological systems and at the same time underscored the complexity of the highly concentrated and organized cellular contents.
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Keywords
biochemistry, protein folding, proteostasis network, proteostasis, FoldEco