I am still downloading PDB files through Bio.python. I am just interested in the overall distance distribution of residues, say Lys alpha carbon. NMR entries might end up over-representing due to their multiple models. Thank you for pointing it out. Is there a quick way to distinguish crystal structures from NMR structures?
Hi Feixia, You could certainly use Chimera to do that. You need to know some Python. Take a look at the Programmer’s Guide:
In particular, the “basic primer” discusses how to loop over files in a directory and do things to them one by one.
Here’s some example code for printing the lysine CA-CA distances for a single open file. You could take that and move it into the loop described in the basic primer — customizing it as you wish…
from chimera import openModels, Molecule
# opening NMR files can produce multiple models, so use a loop...
for mol in openModels.list(modelTypes=[Molecule]):
lysCas = [a for a in mol.atoms if a.name == “CA” and a.residue.type == “LYS”]
for i, ca1 in enumerate(lysCas):
for ca2 in lysCas[i+1:]:
print mol.name, ca1, ca2, ca1.coord().distance(ca2.coord())
—Eric
Eric Pettersen
UCSF Computer Graphics Lab
Hi there,
I am interested in retrieving distance information from large dataset in an automatic fashion. For instance, can we use Chimera to get the distances between lysine alpha-carbons of current PDB entries. Presumably, we can download all PDB structures on our local desktop, and just call functions one structure at a time. I wonder if we can do that with Chimera. Your advice will be highly appreciated.
Best,
Feixia