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As described on our project design page (link), our most ambitious design called for modification of the alpha subunit of endogenous yeast G protein. The challenge here was that we wanted to maintain its natural functionality while expanding it to serving our purposes.

This meant we needed to design a G-alpha that would:

1. A) have a natural-like affinity for the beta/gamma complex of the yeast G protein to stay bound to the beta/gamma complex when no signal was present, thus inhibiting leaky activation of the pathway.
2. B) have low enough affinity for the beta/gamma complex after cleavage by our TEV-protease so that the fragments dissociated from the beta/gamma complex, thus not interfering with their natural function.

Just to refresh, our work on G-alpha hinged on finding a mutant that preserved natural function when there was no signal (i.e. no cytokine), and also dissociated appropriately in the presence of a signal.

This page presents all stages of G-alpha engineering in a chronological order. The progress followed the engineering cycle (design-build-test-learn) as we iterated our designs several times. As we utilized primarily dry-lab experiments, the whole cycle often spans over each stage. Every stage was informed by all the previous ones. FIGURE  WITH THE ENGINEERING CYLE

1) stage one - design

In the first round, we based our design choices on knowledge about the interaction interface of the alpha and beta subunits in bovine G protein, as we found no crystal structure of yeast G protein. Immediately, we noticed that the yeast G protein contained an ‘insertion sequence’ (highlighted in the figure) that other G proteins did not, meaning that it probably was not essential for the general functionality of G-alpha. This attribute made the insertion sequence a great starting point for our search for a mutation site.

2) design-build

We received a file with empirically observed binding interface of bovine G-alpha and G-beta through our supervisors. We could translate known characteristics of the bovine G protein into what we expected to see in the yeast G protein because of the similarity of their sequences that we observed after sequence alignment. Our initial analysis was based on our own knowledge of the biophysical properties of different amino acids. As illustrated by the figure, we identified what we believed were most essential residues (red color).

3) test-learn

Our dry lab collaboration with iGEM team Aalto-Helsinki started taking shape and we accepted their suggestion to use Rosetta for our protein engineering. Firstly, we supplemented our own analysis with an alanine scan (performed by Robetta server), which provided us with quantitative predictions of selected residues’ role in G-alpha/G-beta binding interface. Alanine scan was a script that calculated how Gibbs free energy of the two proteins changed upon changing a single residue to alanine. The results confirmed our intuitions to a good extent and offered several valuable insights that led to refinement of our G-alpha designs. As can be seen in the figure, residue 227 was predicted as the single most important residue in the protein-protein interaction. See the dry lab page to learn more about technical details of the method (DRY LAB PAGE LINK).

4) design-build-test-learn

We designed three versions of G-alpha ranging from more conservative to more extreme modifications. We confirmed that none of the edits lead to unexpected conformational changes by SwissProt modeling. We used Cello to predict protein localization – correct localization to the membrane was crucial for biosensor functionality. As shown in the figure, our novel designs yielded better localization scores than the wildtype. Cytoplasmic localization was desirable, as certain enzymes in the cytoplasm were known to modify G-alpha for it to go to the membrane.

5) design-build-test-learn

Here we faced the reality of laboratory experimentation. Three G-alpha mutants with many diverse mutations could lead to lots of ambiguity as the laboratory results could be hard to interpret due to inability to monitor what mutation had profound impact. Time constraint in the wet lab motivated us to limit the number of possible mutation points. We eliminated point mutations and kept only cleavage sites for TEV protease. To improve the quality of future experiments, we expanded the range of G-alpha candidates from three to seven by introducing all combinations of the three selected mutations. We checked folding predictions for all seven novel G-alphas which resulted in a significant overlap (as visible in the figure). None of the proteins seemed likely to take on a faulty conformation according to SwissProt.

6) test-learn

We were still wondering how to predict the behavior of fragments generated by cleavage of a mutant G-alpha by TEV protease. We decided to run such a simulation in Rosetta. Thanks to Kurt V. Mikkelsen, we got the opportunity to use his supercomputer to perform such a computationally demanding analysis. We simulated change in Gibbs free energy of the seven mutant G-alphas (see the figure) and the nine fragments formed after cleavage (see figure). The rational was to find a candidate G-alpha with binding affinity like the wild type, but with lowest possible binding affinity of its fragments after cleavage. (For more technical details about how the simulation was set up, see the dry lab page.) The simulation lead to some unexpected results – majority of fragments seemed to have higher affinities to G-beta, compared to mutant G-alphas.

First, we were startled by the findings but after thorough discussions concluded that the overall G-alpha conformation probably restricted some degrees of freedom of the high-affinity regions. These regions facilitated most interaction between G-alpha and G-beta, as we learned in one of the earlier stages. Our cleavage sites were located between these high-affinity regions, therefore, cleavage effectively freed the high-affinity regions from the previous restrictive conformation and allowed them to bind G-beta more tightly. Obviously, these findings turned our initial intuitions about the protein-protein interaction on its head. We also realized that affinities do not necessarily provided the answer to successful signal transduction, as further analysis showed that conformational changes probably lead to Ste5 recruitment by G-beta (sites where Ste5 bound were not obstructed by G-alpha). Information about affinities without knowledge about conformational changes of G-beta and their causes could not answer our questions. Anyways, mutant M124 was the only whose some fragments had lower affinity to G-beta compared to the mutant itself, so we decided it was the most promising candidate if we followed our reasoning behind the rational of this simulation.

7) test-learn

Following the conclusion of the previous Rosetta simulation, we designed and ran another simulation to obtain more refined details about fragments from M124 cleavage. The results showed that all generated fragments had a significantly higher affinity to the G-beta compared to the mutant G-alpha M124. Although, exploration of PDB files revealed that spatial assignment of the fragments showed clashes (example in the figure) so that affinity of each fragment could be limited by other presence of the other fragments, ultimately leading to diminished presence at G-beta. Nevertheless, results of our simulations could not inform us about conformational changes of G-beta which protected us from formulating clear conclusions.

PDB (m124-fragments) HERE

8) wet lab

The wet lab decided to prepare mutants with combinations of more than three cleavage sites. Several mutants were successfully obtained and last-minute tested for: maintaining their natural functions, cleavage by TEV protease, desired activity in the biosensor.

9) future

The next round of simulations could be based on molecular dynamics for us to see whether the results of Rosetta could be trusted. Apart from that, the focus could shift from affinities to conformations, and particularly G-beta could be more explored. In the wet lab, separate assays could be prepared to check each condition against a control.