Team:Queens Canada/Protein Stability
Protein Stability
Introduction
Throughout the modeling process of the biosensor components, we were unsure if our theoretical construct would be stable when expressed in cell lines. We believed the target ligand (OspA) would be stable in solution, as we sourced the construct from a reputed paper, however, we were uncertain if the ScFv that we had created would be stable. Therefore, to ensure both constructs would be theoretically stable we conducted nanoscale molecular dynamic simulations. These in silico simulations were then used to provide valuable insights into the nanoscale interactions of our constructs allowing us to assess.
Methodology
Step 1: Overview
All molecular dynamics were run on GROMACS Version 2021.3. We followed the combined and modified methodology of Justin Lemkul’s GROMACS Tutorials as well as bioinformatic simulation tutorials from both “Bioinformatics with BB” and “Sanket Bapat” provided through YouTube.
Step 2: Generate the Protein Topology
After importing the construct files into GROMACS, the protein topology was generated. The topology contains the defining characteristics of a protein within a simulation, including nonbonded (atom types, charges) and bonded (bonds, angles, dihedrals) parameters. It also contains a force field, which is a collection of equations and associated constants that attempt to recapitulate the physical characteristics of a protein. The OPLS-AA force field was used for our models.
Step 3: Create and Solvate a Box
For our modelling purposes, a simple aqueous system was generated. This involved defining a box that would contain our constructs and then systematically filling the box with water molecules.
Step 4: Neutralizing System Net Charge
The solvated system we had created contained charge constructs which can lead to inaccuracies in the simulation. Therefore, to ensure accuracy the system was neutralized by adding ions. Na+ and Cl- ions were added to the solvation to neutralize any charge that was present in the protein.
Step 5: Energy Minimization
With a solvated and electroneutral system created we had to ensure there were no steric clashes or misfolding in our constructs. To confirm this, we performed structural relaxation through energy minimization.
Step 6: Isothermal-Isochoric Equilibration
To avoid system collapse, the solvent and ions were equilibrated around the protein. This was done by equilibrating the system based on temperature to ensure the proper orientation of the protein.
Step 7: Isothermal-Isobaric Equilibration
This step follows in a similar landscape to step 6 however, the system was equilibrated and stabilized based on pressure and density as opposed to temperature.
Step 8: Run Nanoscale Molecular Dynamic Simulation
After the system had been energy minimized and equilibrated, we started collecting data on the nanoscale dynamics of each atom in our system. This provided us with information on the interactions of our protein constructs in the simulation system, allowing for the interpretation of thermodynamic and structural stability.
Results
Several nanoscale molecular dynamic simulations were performed on both our 3-24 ScFv and OspA ligand. From the simulated constructs, we formed on GROMACS we created molecular models over a nanosecond within the solution. By doing so, we were able to visualize the trajectory of these proteins.
Assessment of the atomic trajectory of our proteins over one nanosecond allowed us to visualize the stability of our biosensor constructs. Despite this simulation being fairly rudimentary in terms of molecular dynamics, it provided us with valuable insight into the structural properties of our proteins, including energy minimization, root-mean-square-deviation, and the radius of gyration. This data is represented in Figures 2-4, respectively.
The first step in analyzing the molecular stability of each of these constructs was to energy minimize each structure. Energy minimization is the process of finding the lowest possible energy confirmation for all atoms with the structure, allowing us to find the most stable structure (1). This is a crucial first step in the analysis as minimized structures can estimate binding strain when interacting with a ligand.
The radius of gyration is used to measure protein folding and in turn how compact the 3D structure of a protein is (2). The radius of gyration is determined based on the combination of four separate classes of protein domains (α, β, α/β, and α + β), all of which have characteristic gyration radii. Based on the composition of these protein domains making up the protein construct, a specific gyration radius can be determined. For both the 3-24 (~26kDa) and OspA (~148kDa) construct it can be determined that both are stable based on their radius of gyration compared to their molecular weights as suggested by Smilgies and Folta-Stogniew (3).
Root mean square deviation (RMSD) is another metric from which we can assess the stability of our constructs by monitoring the change in conformation the backbone undergoes as the protein shifts from an initial to the final position (4). The stability of the protein is determined based on the degree by which it deviates while changing confirmation, with smaller deviations equating to more stable protein structures. Based on both constructs having RMSD values <0.8 (8Å) they both can be considered relatively stable (5).
Future Directions
The molecular stability simulations we conducted gave us valuable insight into how our constructs would interact once in solution and how stable they would remain. However, these simulations were conducted in a relatively stable solution of water which limits strain on the constructs. While this solution was valuable to gain a baseline for construct stability, it is an unrealistic solution that our constructs will not likely be placed during the process of testing. Therefore, alternative simulations could be conducted to better understand how our constructs will react once placed in to, for example, the detergent used to help lyse the tick (Katherine please check this to make sure this is accurate in respect to the detergent and how it will work.
References
1. Mackay, D. H. J., Cross, A. J., and Hagler, A. T. (1989) The Role of Energy Minimization in Simulation Strategies of Biomolecular Systems. Predict. Protein Struct. Princ. Protein Conform. 10.1007/978-1-4613-1571-1_7
2. Lobanov, M. Y., Bogatyreva, N. S., and Galzitskaya, O. V. (2008) Radius of gyration as an indicator of protein structure compactness. Mol. Biol. 2008 424. 42, 623–628
3. Smilgies, D.-M., and Folta-Stogniew, E. (2015) Molecular weight–gyration radius relation of globular proteins: a comparison of light scattering, small-angle X-ray scattering and structure-based data. J. Appl. Crystallogr. 48, 1604
4. Aier, I., Varadwaj, P. K., and Raj, U. (2016) Structural insights into conformational stability of both wild-type and mutant EZH2 receptor. Sci. Reports 2016 61. 6, 1–10
5. BORDOGNA, A., PANDINI, A., and BONATI, L. (2011) Predicting the Accuracy of Protein–Ligand Docking on Homology Models. J. Comput. Chem. 32, 81