Biomod/2011/PSU/BlueGenes/results: Difference between revisions

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GNM has already been thoroughly and successfully applied to proteins. There is even a database known as iGNM that has calculated protein fluctuations for over 20,000 structures from the protein data bank or PDB. In most cases, carbon atoms are chosen as the nodes to represent the network. A common cutoff value is 7 angstroms.  
GNM has already been thoroughly and successfully applied to proteins. There is even a database known as iGNM that has calculated protein fluctuations for over 20,000 structures from the protein data bank or PDB. In most cases, carbon atoms are chosen as the nodes to represent the network. A common cutoff value is 7 angstroms.  
To show you that GNM works, here, we calculated the fluctuation for HIV-1 protease and compared it to the temperature factor, which are experimental values. There are 200 nodes in this structure.
To show you that GNM works, in Figure 1, we calculated the fluctuation for HIV-1 protease and compared it to the temperature factor, which are experimental values. There are 200 nodes in this structure.


[[Image:Hiv1protease.png]]
[[Image:Hiv1protease.png]]
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There haven’t been many applications of GNM on DNA structures. But since GNM filters out the chemistry of the atoms, there is no reason why it shouldn’t work.  
There haven’t been many applications of GNM on DNA structures. But since GNM filters out the chemistry of the atoms, there is no reason why it shouldn’t work.  
Here we tried it with DNA of PDB ID 307D. We used the phosphate atoms as the nodes and a cutoff distance of 13 angstroms. There are 60 nodes. You can see that results are pretty satisfactory but are slightly less accurate than the comparison with protein. This is proteins have longer chains than DNA and more nodes, thus it rules out randomness. DNA is more flexible and thus harder to calculate the flexibility. We chose 307D strictly because it had a long chain.  
In Figure 2, we tried it with DNA of PDB ID 307D. We used the phosphate atoms as the nodes and a cutoff distance of 13 angstroms. There are 60 nodes. You can see that results are pretty satisfactory but are slightly less accurate than the comparison with protein. This is proteins have longer chains than DNA and more nodes, thus it rules out randomness. DNA is more flexible and thus harder to calculate the flexibility. We chose 307D strictly because it had a long chain.  


[[Image:307d.png]]
[[Image:307d.png]]
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===GNM with Synthetic DNA===
===GNM with Synthetic DNA===
Now that we have shown that GNM is applicable in both proteins and DNA, we want to use it to calculate fluctuations of synthetic DNA. Below you can see 2 structures that we've made using NanoEngineer-1.  
Now that we have shown that GNM is applicable in both proteins and DNA, we want to use it to calculate fluctuations of synthetic DNA. Below you can see 2 structures that we've made using NanoEngineer-1, a cube and a prism. Both show similar satisfactory results. It makes sense to think that the vertices of the structure are the least flexible because they are the most confined. The legs of the prism are more flexible because they are not as rigidly confined.  


[[Image:Structures.png]]
[[Image:Structures.png]]
:Figure 3: Fluctuation data of a cube (top) and prism (bottom) structure created in NanoEngineer-1 and calculated using GNM.


To verify that the GNM method gives accurate results to synthetic DNA structures, we compared our data with another method. Figure 4, left, you can see a half-gear structure that we created in NanoEngineer-1 and calculated using GNM. On the left is a similar structure 
[[Image:Compare gnm.png]]
[[Image:Compare gnm.png]]



Revision as of 11:07, 28 October 2011


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Results

GNM with Protein

GNM has already been thoroughly and successfully applied to proteins. There is even a database known as iGNM that has calculated protein fluctuations for over 20,000 structures from the protein data bank or PDB. In most cases, carbon atoms are chosen as the nodes to represent the network. A common cutoff value is 7 angstroms. To show you that GNM works, in Figure 1, we calculated the fluctuation for HIV-1 protease and compared it to the temperature factor, which are experimental values. There are 200 nodes in this structure.

Figure 1: Fluctuation data for HIV-1 protease predicted by GNM and compared with temperature factor (TF)


GNM with DNA

There haven’t been many applications of GNM on DNA structures. But since GNM filters out the chemistry of the atoms, there is no reason why it shouldn’t work. In Figure 2, we tried it with DNA of PDB ID 307D. We used the phosphate atoms as the nodes and a cutoff distance of 13 angstroms. There are 60 nodes. You can see that results are pretty satisfactory but are slightly less accurate than the comparison with protein. This is proteins have longer chains than DNA and more nodes, thus it rules out randomness. DNA is more flexible and thus harder to calculate the flexibility. We chose 307D strictly because it had a long chain.

Figure 2: Fluctuations of nodes for 307D (PDB ID) predicted by GNM and compared with TF


GNM with Synthetic DNA

Now that we have shown that GNM is applicable in both proteins and DNA, we want to use it to calculate fluctuations of synthetic DNA. Below you can see 2 structures that we've made using NanoEngineer-1, a cube and a prism. Both show similar satisfactory results. It makes sense to think that the vertices of the structure are the least flexible because they are the most confined. The legs of the prism are more flexible because they are not as rigidly confined.

Figure 3: Fluctuation data of a cube (top) and prism (bottom) structure created in NanoEngineer-1 and calculated using GNM.


To verify that the GNM method gives accurate results to synthetic DNA structures, we compared our data with another method. Figure 4, left, you can see a half-gear structure that we created in NanoEngineer-1 and calculated using GNM. On the left is a similar structure


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