Modelling in systems biology provides a tool for research. Modelling helps researchers to process, make sense of large amounts of data and also test different hypotheses. Hence, a model can save much time and money since it makes it possible to test and compare different hypotheses before one starts doing laboratory work.
Our goal with the project and the model is to investigate the folding process and the necessity of the chaperone GroES. Our hypothesis is that GroES has a function on its own where it can interact with the substrate by holding it, thus preventing it to misfold, also called Holdase effect. This hypothesis is based on the work of our supervisor Per Hammarström . But, the folding process is complex with many known and unknown variables that are incredibly difficult to test in the lab. Therefore the main objective was to find the optimal conditions for maximal protein production in E.coli. To do this, we implemented the model of LiU iGEM 2017 and developed it further. The model was made in MATLAB and ordinary differential equations (ODEs) was implemented. The model contains a translation of FoldEco, which is a model created by Evan T. Powers, David L. Powers and Lila M. Gierasch .
The FoldEco model was adjusted by adding reactions that describe our hypothesis. We have added two reactions between Unfolded (U) and GroES, see figure above. By doing so, we give GroES the possibility to have a holdase effect. To see if it even would be worth trying to increase the amount of natively folded proteins with GroES, we created three different types of theoretical protein profile, the result is shown below in figure 1.
The first (blue) being a protein that would be prone to both misfolding and aggregation. The second (orange) would be easier to fold but still prone to aggregation. Lastly, we created a protein profile which was neither prone to misfolding nor aggregation (green)
Describes the relationship between an increased GroES concentration and the GroES-Substrate releasing rate factor for the three different protein profiles.
Since we did not have any data on the rate at which GroES could bind and release the substrate, a wide range of the release rate was tested. As the hypothetical holdase effect is described by the release rate of the substrate, we only concerned ourselves with decreasing the releasing rate factor. This is indicated by the arrow from GroES to the unfolded state, which is shown in the simplified picture of our model above.
In figure 2, above, we can see how the three different theoretical proteins natively folded state concentration changes over an increasing holdase effect. The differences in concentration that could be correctly folded between the proteins were satisfying enough to proceed.
Describes the relationship between the yield of native substrate an the GroES-Substrate releasing rate factor for the three different protein profiles.
By optimising the addition of GroES concentration that would give the largest yield for a given releasing rate factor, see figure 3, we can see that the misfolding prone protein would need additional GroES at a lower releasing rate factor than for the other two proteins. Compared to the aggregation prone proteins, the non-aggregation prone protein shows no positive response together with additional GroES. The yields plotted in figure 3 suggests that for low holdase effects, an addition of GroES would not result in any improvement. However, as the holdase effect increases, an improvement of the yields begin for the aggregation prone proteins. The improvement continues until it reaches a maximum, then starts to decline.
How valid are these results?
The first question one might ask oneself is if the concentration added to the system is within reasonable ratios relative to the initial concentrations. A study has shown that during stress in E.coli induced by heat shock, GroES concentrations can rise from 1.9µM to 4.7µM. FoldEco uses an initial concentration of GroES that is 35µM. However, this is of the monomeric component of GroES and in E.coli, GroES consists of 7 subunits. An increase of mostly between 30µM and 120µM relative to 35µM could be interpreted as a reasonable amount. In addition, the yield is not too dramatic either, but a 30% increase of a protein that is hard to fold and prone to aggregation is something that could be beneficial for a range of reasons.
What does this tell us?
The holdase effect of GroES is unknown, but a potential holdase effect has been implemented in the model. When testing the model we have seen that the more the protein is prone to aggregation and difficult to fold the greater the ability GroES has to possibly have a positive effect. If the protein is easy to fold and is not an aggregation prone protein, the model shows that GroES does not have any positive effect and does not help the protein to fold but rather hinders it. In summary, the model suggests that GroES would have the ability to interact with proteins that are prone to misfold. This is also a suggestion in our laboratory experiments and results, see Project → Demonstrate
 Transient conformational remodeling of folding proteins by GroES—individually and in concert with GroEL Moparthi SB, Sjölander D, Villebeck L, Jonsson BH, Hammarström P, Carlsson C. (2014) J Chem Biol Jan 7(1):1-15
 Evan t. Powers, David L. Powers and Lila M. Gierasch (2012) FoldEco: A Model for Proteostasis in E.coli.
Neidhardt FC, VanBogelen RA (1987) Heat shock response. In: Neidhardt FC, Ingraham JL, Low KB, Magasanik B, Schaechter M, Umbarger HE (eds) Escherichia coli and Salmonella typhimurium: cellular and molecular biology. American Society for Microbiology, Washington, DC, pp 1334–1345