Since our protien is intended to test for water pathogens in an aqueous solution, we wanted to ensure our protien and its constructs were stable under a realistic environment. The wild-type Bst polymerase was stable under aqueous conditions as proven through LAMP, however we were unsure if the DNA-BP was stable as well as the fully modified version of Bst was stable. To test this, we conducted nanoscale molecular dynamic simulations using an open-source molecular dynamic software GROMACS (version 2022.2) through a Linux based interface.

The methodology employed to generate our models was adopted from Justin Lemkul’s GROMACS tutorials.

Step 1: Create Bst polymerase construct in FoldX and PyMOL

We used FoldX and PyMOL to predict the folding structure and model our Bst construct before performing any simulations. We had to ensure that the fusion protien would not interfere with the structure of Bst before moving forward.

Step 2: Generate the Protein Topology

After importing the PDB file of our Bst construct from PyMOL into GROMACS, the protien topology was generated. The topology contains all the information necessary to define the molecule within a simulation including nonbonded parameter (atom types and charges) and bonded parameters (bonds angles and dihedrals). We then had to select a force field which contains the information that will be written to the topology. We used the OPLS-AA/L all-atom force field because it is an optimized force field for liquid simulations.

Step 3: Create and Solvate a box

A simple aqueous system was generated for our simulation as we expect our protien to operate under similar conditions. This aqueous box contained our constructs in which we would begin performing molecular dynamics.

Step 4: Add ions to neutralize system net charge

The solvated system created in step 3 now contains our charged constructs. Charges in the system can lead to inaccuracies in the simulation, so they were neutralized by adding ions through genion. Genion reads through the topology and replaces water molecules with ions that the user specifies. We added Na+ and Cl- ions to the solvation to neutralize any charges.

Step 5: Energy Minimization

With this electroneutral system created in step 4, we had to ensure that the system had no steric clashes or inappropriate geometry. The structure was relaxed through energy minimization (em) where the goal is to find the minimum energy conformation for the given structure. Minimization is achieved when the minimum point of the first derivative of a function (f) with respect to each of its variables (x1,2..i) is zero and the second derivative is postive (1):

Step 6: Isothermal-Isochoric Equilibration

The solvent and ions around the protein were equilibrated by to avoid system collapse due to unrestrained dynamics. This is because the solvent is mostly optimized within itself but not necessarily with the solute (the protien). In Isothermal-isochoric equilibration, the system is optimized based on constant number of particles, volume and temperature (NVT).

Step 7: Isothermal-Isobaric Equilibrium

This step is similar to step 6, where the system is optimized based on constant number of particles, pressure and temperature (NPT).

Step 8: Nanoscale Molecular Dynamic Simulations

Now that the system has been energy minimized and fully equilibrated at the desired pressure and temperature, the molecular dynamic simulations can be conducted. Molecular dynamics analyzes the movements and interactions of our constructs under controlled conditions to predict the thermodynamic and structural stability. We were able to assess the radius of gyration and root mean square deviation (RMSD) of each of our constructs.

We performed four nanoscale molecular dynamic simulations on:

1. DNA-BP Sac7e
2. Bst polymerase with point mutations
3. Wild-type Bst polymerase fusion with Sac7e
4. Bst polymerase with point mutations fused with sac7e

Along with obtaining data regarding the molecular dynamics, we obtained the visual trajectory of these protiens under these aqueous conditions.

Figure 1.1. Nanoscale trajectory of DNA-BP Sac7e

Figure 1.2. Nanoscale trajectory of Bst polymerase with point mutations

Figure 1.3. Nanoscale trajectory of Wild-type Bst polymerase fusion with Sac7e

Figure 1.4. Bst polymerase with point mutations fused with sac7e

As visualized from the 3D movies, each of the constructs do not undergo drastic changes, thus indicative of protien stability under an aqueous condition. It should be notes that these simulations are fairly rudimentary in terms of molecular dynamics, however still provided us with valuable insight into the structural properties of our protiens. The first parameter that was assessed was the energy minimization:

Figure 2.1. Energy minimization of Sac7e DNA-BP

Figure 2.2. Energy minimization of bst polymerase with point mutations

Figure 2.3. Energy minimization of wildtype bst polymerase fused to DNA-BP Sac7e

Figure 2.4. Energy minimization of bst polymerase with point mutations fused with sac7e

By energy minimizing the structure, we were able to identify the most stable conformation of our protiens (2). Although these minimized energies don’t directly provide specific insight about the protien stability, an energy minimized structure represents the underlying configurations about which fluctuations occur during dynamics thus provide a meaningful basis for structural analysis (2).

The first parameter that was measured in our molecular dynamic simulations was the radius of gyration: 

Figure 3.1. Radius of Gyration of Sac7e DNA-BP

Figure 3.2. Radius of Gyration of bst polymerase with point mutations

Figure 3.3. Radius of Gyration of wildtype bst polymerase fused to DNA-BP Sac7e

Figure 2.4. Radius of Gyration of bst polymerase with point mutations fused with sac7e

The radius of gyration (Rg) is defined as the distribution of atoms of a protien around its axis (3). Rg is used to measure how compact the 3D secondary structures are during protien folding over a period of time (4). If a protien is compact and stably folded, it will likely maintain a relatively steady value of Rg in all dimensions, however if the protien unfolds, the Rg will change over time. As seen from the graphs, all our protiens have steady Rg values in all dimensions, indicating stably compact protiens across the board.

The second parameter we measured in our molecular dynamic simulations was the “root-mean-square deviation” or RMSD for each of our proteins:

Figure 4.1. RMSD of Sac7e DNA-BP

Figure 4.2. RMSD of bst polymerase with point mutations

Figure 4.3. RMSD of wildtype bst polymerase fused to DNA-BP Sac7e

Figure 4.4. RMSD of bst polymerase with point mutations fused with sac7e

The RMSD monitors the change in confirmation of the backbone as the protien shifts from an initial to final position (5). The RMSD is calculated between a defined staring point of the simulation and all succeeding reference frames and is calculated using the formula:

where N denotes the number of atoms, i the current atom, rX the target structure and rY the refence structure (6). Thus, the stability of the protien is based on the degree by which it deviates while changing conformation, where smaller deviations equate to a more stable structure. As seen from our graphs, all of our proteins have relatively low RMSD values less than 0.8 which infers that these protiens are stable.