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To find out those abnormal aromatics, I calculated the RMSD of benzene ring in the model and 6msm. (aromaticList.py, generateList.py).
To run the script:
python generateList.py
Example to calculate RMSD of benzene ring in Pymol:
select model5Ring337, (newModel5 and i. 337 and n. CD1+CD2+CE1+CE2+CZ+CG)
select 6msmRing337, (6msm and i. 337 and n. CD1+CD2+CE1+CE2+CZ+CG)
align model5Ring337, 6msmRing337
I used R to analysis the RMSD and get a histogram of RMSD shown below.
We considered the cutoff of RMSD to be 0.2. If the RMSD is greater than 0.2, we regarded the residue as an abnormal residue. There are 15 residues that have RMSD greater than 0.2. Here is the RMSD outlier list.
We considered the cutoff of RMSD to be 0.2. If the RMSD is greater than 0.2, we regarded the residue as an abnormal residue. There are 15 residues that have RMSD greater than 0.2. Here is the RMSD outlier list.
To get a model that has normal aromatics, I (RMSD > 0.2) in the model with distance and alpha helix constraints. And then used the model without distorted aromatics as template to generate a new model.
To get a model that has normal aromatics, I (RMSD > 0.2) in the model with distance and alpha helix constraints. And then used the model without distorted aromatics as template to generate a new model.
Removed the distorted aromatics
Removed the distorted aromatics
Put the model with distance and alpha helix constraints (modelWithRestraint5.pdb), removePDB.py and generateRemoved.py in the same folder. It will generate a pdb file called In.pdb. The In.pdb file will be used to build a model that has constraints and not distorted benzene rings.
python generateRemoved.py
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