Overview

After conducting the previous modeling, we now obtain the possible structure and the binding position of the fusion protein. The next step is to inspect on the fusion protein’s enzyme activity. We want to know the degrading efficiency under some circumstances, hence by constructing a degradation model, and expect to learn how well does the fusion protein work. Finally, we sincerely send our gratitude to OUC-China on assisting us using Gillespie algorithm to process Stochastic simulation

Result

Reaction formula

ODE equation

Fit the experimental data:

1.  Estimation method: Estimating non-mixed effects model with Lsqnonlin.

2.  Estimated parameters:

•  Raw data:

Fig. I Experimental data of degradation of aflatoxin.

Fig. II Data comparison between experimental data and simulation.

Fig. III Estimated parameter values and their standard errors. Note that the standard errors are extremely higher than the parameter values, demonstrating that though parameter values shown here are best estimated, they are not accurate for lacking adequate experimental data.

Stochastic simulation

1.  Simulation method: Gillespie algorithm

2.  Number of samples: 30000 (300shown)

Fig. IV Stochastic simulation results of degradation of aflatoxin by MSMEG_5998. Red lines represent the stochastic simulations, and the black line represents the mean level of degradation.

3.  Comparison between experiments and simulations

Fig. V Comparison between experimental data and simulation results. Simulation data is the mean level of the stochastic simulations, R^2 is calculated to be 0.9962.

Docking modeling

Overview

After the team conducted the wet lab experiments on Aflatoxin degradation, the results show a possibility that the two functional parts in the fusion protein may be accurate, therefore, the team want to proof the concept by simulating the binding position of aflatoxin and the fusion protein, in order to assure our fusion protein can be functional or even with a higher performance as expected. The team detected the possible active sites of the proteins in our project and then stimulated the docking process involving the use of AutoDock and PyMol.[8] By doing so, we are expecting to observe the performance of the fusion protein, and more importantly, to inspect on the improvements from the new protein comparing to the original ones. Please notice that the fusion protein is merged with two different proteins, which is MEMEG5998 and Thioredoxin. Therefore, in the lateral discussion, docking simulation contains two different protein-ligand model, which are “Thioredoxin-Fusion protein” model and” MSMEG5998-aflatoxinB2” model.

The docking simulation of “Thioredoxin-Fusion protein”

1.  Since the structure of Thioredoxin has been studied, we can lock down the active site of thioredoxin by use Uniprot. The team found that there are two active site , which are NO. 33 and NO.36 of the sequence.

2.  By using NCBI BLAST, the team compared the sequence of the fusion protein with Thioredoxin. The team confirmed that the active sites of fusion protein corresponding to the ones of Thioredoxin are No.33 and 36 , both are Cysteine, C.

3.  The team later on constructed a fusion protein 3D model and then labelled the active sites by using PyMOL. By creating the model the team could learn why thioredoxin is helpful toward protein folding, since the active sites of Thioredoxin is not facing away from MSMEG5998.

This 3D model shows the surface of the fusion protein, which allows us to grasp the concept of what our protein looks like. The region labeled in red is the possible binding site of Tioredoxin, which as we speculate can assist the fusion protein itself while folding.

The structure of the fusion protein (MSMEG5998 part)

1.  While the structure of MSMEG5998 remains unknown, the team still manage to predict the model by using similar protein to create a model, the software tool we used is Swiss Model[3] [4].

2.  When deciding the model of MEMEG5998, the team used the Swiss Model, by comparing the amino acid sequence among the database of protein sequence. There are two main factors leads to two different models, which is by coverage or by identity. The team chose the highest coverage protein sequence to be our model, named” MSMEG5998 Swiss model”.

3.  The sequence of the MSMEG5998 Swiss model is compared with that of fusion protein by using Uniprot. The team then discovered three similar groups being labeled below, which are likely active sites.

4.  The three possible loci correspond to the fusion protein sequence are:

i.  189，Arginine，R

ii.  214，Glutamine，Q

iii. 246，Alanine，A

Since the .pdb files presented by raptorX were unable to visualize hydrogen bonds of the compound , thus the team used PMViewer v1.5.7 to add on hydrogen bonds and negative charge. (the following pictures are compounds before and after enhancements)

Further enhancements to the compound before docking simulation on MSMEG5998

Under PMViewer, the appearance of the protein before enhancements.

The fusion protein after enhancements, which adds hydrogen and charge to the protein. This process allow the structure and the binding process as real as possible.

Adding ligand to the docking simulation of MSMEG5998-Aflatoxin B2

Search PubChem to locate the ligand, which in this case is AflatoxinB2, and then download the SDF format.

The docking of MSMEG5998 to Aflatoxin B2

1.  The settings for Aflatoxin B2 before docking: Minimize the energy, in order to acquire a stabilized compound which is easier to go through the docking simulation.

2.  Select the docking function to proceed.

Autodocking area

The possible autodocking area are limited to the three active sites of MSMEG5998 mentioned earlier, which can increase the model’s accuracy. After autodocking, we visualize the result by using PyMOL to create a 3D docking model. The three active sites for docking are tested, and compared to one another. The team finally come up with one ideal active site, which is

The docking was processed by Autodock (please visit our software tools page, the cube area is the area our team choose to process the docking stimulation, the results are in the picture below.

This is a side view of the protein macromolecule. The MSMEG5998 active site 214 is presented in red, while the blue compound represents Aflatoxin.

Discussion and Conclusion

1.  By using protein modeling techniques, the team predicted a fusion protein with multifunction while one doesn’t inhibit the other, or creating structural failure. Which later on helped us in the wet lab experiment to proceed.

2.  With the software tools, the team is able to predict an enhanced fusion protein (MSMEG5998 combined with Thioredoxin) that performs better than the original protein (MSMEG5998).

4.   Future goals:

i.  unfortunately, there is a time limit to our project. However, the team would love to continue our modeling project and also put the theory into practice, trying to see whether active site 214 is the actually binding site with Aflatoxin. The team would conduct experiments of point mutation on site 214, to see if the binding affinity changes or not, in order to explain why this site 214 is crucial toward Aflatoxin degradation.

ii.  After conducting the two main modeling project, our team successfully predict the function of our fusion protein; however, the long term goal is that the team envision our aflatoxin-degrading protein be put in to massive and commercialize production. Therefore, our team would want to measure the productivity of our protein, in order to seek for the ideal producing conditions and reach the maximum efficiency.(Click the button to see some of the results from the experiment our team has conducted.)

References