With ongoing laboratory restrictions imposed at Queen’s University due to COVID-19, a large focus of our project became modelling. Modelling often serves as a critical component of the design and planning phase of any successful iGEM project. By creating models, we can verify our literature-derived theories about the systems we are using, along with in silico ‘proof-of-concepts’ that impact our design considerations. Therefore, we ensured that sophisticated modelling techniques were incorporated into every aspect of our project. This included (1) an inquiry on the dynamic stability of our phosphate, potassium, glucose, parathyroid hormone (PTH), and fibroblast growth factor 23 (FGF-23) biosensor constructs, (2) development of an E/K coiled-coil immobilization system, and (3) the guided mutation, and introduction of cysteine residues for immobilization.
The research and development phase of synthetic biology is an expensive and time intensive phase. When we model our proteins, systems, and devices, this gives our team members valuable insight into the way these parts interact with one another and their behaviors. While not every experiment in the lab goes perfectly, modelling effectively will limit the rate of failure when we test our theories. For example, modelling a protein in PyMOL may provide insight into what residues are exposed on the surface for a mutagenesis. Blindly making mutations and ordering that synthetic DNA would be unwise, and likely you’d experience a lot of trouble. In summary - modelling deepens our understanding of the project (and will probably make In vitro testing easier).
What did we Model?
We modelled what feels like everything under the sun. We’ll summarize for you. Our initial modelling looked at protein structures in PyMOL and Chimera to do mutations and build fluorescent constructs. In Benchling we modelled our sequences and annotated these to make tracking our changes easy (highly recommended). Our molecular dynamics work looked at the stability of our constructs in solution and certain thermodynamic measurements were obtained. Using visual software’s, we also modelled some construct maps and workflows/schematics. Our hardware team used CAD and SOLIDWORKS to give us some awesome 3D renderings of our device. Honestly – we probably missed something; we modelled a lot. Check out our appendix for all of our models and figures!
Static vs. Dynamic Modelling
When most people compare static and dynamic models, they refer to static as a single point in time and dynamic models as representing time-dependent behaviours, where a system may change properties over time (e.g. distance between two proteins). An example of a static model would be modelling the active site of a protein in PyMOL whereas a dynamic model could be measuring the dissociation constant between two coiled proteins in Gromacs (molecular dynamics)
By following this workflow, we were able to ensure our models were grounded in strong scientific literature
and adhered to the objectives of our project. While dynamic modelling was a new frontier of learning for
the wet-lab members, this workflow promoted consistent communication with advisors and all team members –
ensuring collective deep understanding and successful modelling.
The workflow above was implemented in the creation of all our models, which can be seen below.