Normal mode analysis Gaucher Disease
The protein function often depends on its conformation and dynamical properties. To get a complete picture of the dynamic properties of proteins, the traditional de novo mehthod is known to be Molecular dynamics (MD) simulation. These simulations consider both harmonic and anharmonic motions, and produce great insights into protein dynamics. When the timescales of the dynamics is short enough, MD is a great tool. However this method is still very computationally consuming.
An alternative method is Normal mode analysis (NMA) which has become a popular and often used theoretical tool in the study of functional motions in enzymes, viruses, and large protein assemblies<ref name="NMA1">Eric C Dykeman and Otto F Sankey.(2010). Normal mode analysis and applications in biological physics. JOURNAL OF PHYSICS.</ref>. NMA is based on a physical theory about the motion of an oscillation system where all parts within the system move sinusoidally with the same frequency and with a fixed phase relation. By using it to study the protein dynamical motion, the atoms are considered as point masses connected by springs to simulate the inter-atomic forces. NMA uses only harmonic approximations and anharmonic motions are neglected, therefore it can not give a very precise simulation as MD does. However, it is still very helpful to describe the low-frequency, large-amplitude motions which are most closely related to protein function<ref name="NMA2">Adam D. Schuyler, Gregory S. Chirikjian.(2003). [http://custer.lcsr.jhu.edu/wiki/images/7/76/Schuyler03.pdf Normal mode analysis of proteins: a comparison of rigid cluster modes with Cα coarse graining]. Journal of Molecular Graphics and Modelling.</ref>.
In this task, we used different normal mode analysis servers to study chain A of glucocerebrosidase (2nt0).
Technical details are reported in our protocol.
The WEBnm@ web server provides automated computation and analysis of low-frequency normal modes for proteins. After getting results, the users are thought to have a first glance if the protein contains large amplitude movements and therefore is worth to apply further analyses. WEBnm@ employs the MMTK package (K. Hinsen, J.Comput.Chem., 2000) to calculate the normal modes and only the C-alpha atoms are used. Variety of analysis tools are available:
- Deformation Energies of each mode, eigenvalues
- Atomic displacements and normalized squared fluctuations
- Visualization of the modes
- Correlation matrix
For each mode, the deformation energies were given to show the associated energy. And the corresponding eigenvalues indicated the frequency of the motion. <xr id="tab:defor_enery"/> presents the values of the deformation energy for modes 7 to 20 and <xr id="fig:Eigenvalues_plot"/> shows us the the eigenvalues of each mode. The energy values and eigenvalues are increased in modes 7 to 20.</figtable>
In <xr id="fig:atom_disp_7to12"/>, we see the normalized square of the displacement of each C-alpha atom for modes 7 to 12. The sum of that value of all the residues is 100. The cluster of peaks there reflects the region in strong motion and the individual peak shows the local flexibility.
And in <xr id="fig:fluc_plot"/>, we see the normalized the fluctuation of each C-alpha atom for all modes. The sum of that value of all the residues is 100. This fluctuation values are calculated by summing all the atomic displacements from each mode which takes their eigenvalues as weight value. They indicate the normalized temperature factors.
Looking at the displacement plots, in the modes 7, 9, 10 and 11, there are peaks showing throughout the whole protein which suggest that the most parts of this protein are flexible. In mode 8, two individual peaks showing local flexibility at each end of the protein can be found.
"The correlation matrix shows the correlated movement of the Calphas in the protein. Both axis denote the Calphas of the protein in sequential order, so that each cell in the plot shows the isotropic correlation of two residues in the protein on a range from -1 (anti-correlated) via 0 (uncorrelated) to 1 (correlated). " - from Webnma@
The model 7 </figure>
The model 8 </figure>
The model 9 </figure>
The model 10 </figure>
The model 11 </figure>
Anisotropic Network Model