Difference between revisions of "Team:Calgary/Measurement"
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<h2>What did we quantify?</h2> | <h2>What did we quantify?</h2> | ||
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| − | <p>To assist the dynamic characterization of proteins by other teams, we looked to develop a methodology that allows for the calculation and aggregation of Brownian motion measurements for each amino acid in a sequence. The Brownian motion measurement chosen was the Root Mean Square Fluctuation (RMSF) calculated in ten picosecond intervals for every atom of a protein. | + | <p>To assist the dynamic characterization of proteins by other teams, we looked to develop a methodology that allows for the calculation and aggregation of |
| + | Brownian motion measurements for each amino acid in a sequence. Brownian motion refers to the erratic movement of particles in fluid. The Brownian motion measurement chosen was the Root Mean Square Fluctuation (RMSF) | ||
| + | calculated in ten picosecond intervals for every atom of a protein. | ||
<br> | <br> | ||
<br> | <br> | ||
| − | The RMSF data was calculated from a nanosecond Molecular Dynamic Simulation (MDS) completed within GROMACS, an industrial MDS software. The average RMSF values for every atom was calculated for each amino acid, this ensured that the unit of measurement was observed on a scale that was modifiable by teams. This resulted in a series of curves that quantitatively expressed the dynamics for each amino acid. | + | The RMSF data was calculated from a nanosecond Molecular Dynamic Simulation (MDS) completed within GROMACS, |
| + | an industrial MDS software. The average RMSF values for every atom was calculated for each amino acid, | ||
| + | this ensured that the unit of measurement was observed on a scale that was modifiable by teams. | ||
| + | This resulted in a series of curves that quantitatively expressed the dynamics for each amino acid. | ||
</p> | </p> | ||
<div class="header-area"> | <div class="header-area"> | ||
Revision as of 01:51, 22 October 2019
Inspiration
What's the impact?
Proteins have become a staple in the iGEM community, but there is very little iGEM teams can do to understand their protein’s atomic behaviour. We wanted to generate a quantitative way to allow other teams to characterize each amino acid of their proteins. This allows for more informed protein engineering and utilization through an understanding of the dynamics that accompany the protein.
Figure 1: An example of a dynamic protein model.
Measurement
What did we quantify?
To assist the dynamic characterization of proteins by other teams, we looked to develop a methodology that allows for the calculation and aggregation of
Brownian motion measurements for each amino acid in a sequence. Brownian motion refers to the erratic movement of particles in fluid. The Brownian motion measurement chosen was the Root Mean Square Fluctuation (RMSF)
calculated in ten picosecond intervals for every atom of a protein.
The RMSF data was calculated from a nanosecond Molecular Dynamic Simulation (MDS) completed within GROMACS,
an industrial MDS software. The average RMSF values for every atom was calculated for each amino acid,
this ensured that the unit of measurement was observed on a scale that was modifiable by teams.
This resulted in a series of curves that quantitatively expressed the dynamics for each amino acid.
Figure2: RMSF characterization of the ModGIX protein.
Above is a complete view of the movement attributed to every amino acid of a protein. Having all of the curves present at the same time can be quite confusing. The true power of this measurement can be harnessed when amino acids are displayed in smaller clusters. Below is the dynamics of the 25th, 80th, and 90th amino acid of the 6GIX protein.
Figure 3: molecular dynamics of 25th, 80th, and 90th amino acid in the 6GIX protein.
These three amino acids demonstrate how the dynamics of a protein may look similar at first, but individual inspection can show that they actually are quite different. The general methodology for obtaining this measurement are available here.
Functionality
WHAT CAN RMSF ACCOMPLISH
After characterizing a protein by its dynamics RMSF values, there are numerous possibilities for
further analysis and modification. In our case, it was utilized in the development of ModGIX,
our modified chlorophyll binding protein. ModGIX was developed through the use of functional
Principal Component Analysis (fPCA), made possible by the functional properties of the fPCA
curves. The results were then clustered using an expectation maximization algorithm, the clusters
obtained allowed for the determination of amino acids that attributed to the highest variance
from crystal structure. This analysis was made possible by the measurement of RMSF. Read more about the development of ModGIX here.
This measurement also allowed our team to have a metric for the stress exerted on
the protein. Through RMSF curves teams are able to characterize their proteins based on atomic
movements. Opening this door allows teams to make informed modifications of proteins and
develop a nanoscale understanding of their system.
This measurement has been successfully conducted for the characterization of our 6GIX protein (BBa_K3114006) and our ModGIX protein (BBa_K3114006).
References
Lemkul J.A. (2018). "From Proteins to Perturbed Hamiltonians: A Suite of Tutorials for the GROMACS-2018 Molecular Simulation Package, v1.0" Living J. Comp. Mol. Sci. In Press.
Abraham M.J., van der Spoel D., Lindahl E., Hess B., and the GROMACS development team (2018). GROMACS User Manual version, www.gromacs.org(2018)
Palm, D. M., Agostini, A., Averesch, V., Girr, P., Werwie, M., Takahashi, S., . . . Paulsen, H. (2018). Chlorophyll a/b binding-specificity in water-soluble chlorophyll protein. Nature Plants,4(11), 920-929.
Páll, S., Abraham, M. J., Kutzner, C., Hess, B., Lindahl, E. (2015).Tackling exascale software challenges in molecular dynamics simulations with GROMACS. In: Solving Software Challenges for Exascale. Vol. 8759. Markidis, S., Laure, E. eds. Vol. 8759. . Springer Inter- national Publishing Switzerland London 3–27.
