(sending properly this time, rather than just to Rasmus)
I don't believe a "color by RMS" option is in ChimeraX right now (I'll suggest
it to the developers), but this will align all models then set B-factors for
each residue to the RMS CA deviation from the mean position. Could be extended
fairly trivially to do all-atom RMS if you wanted to. Change the extension for
the attached text file to .py, open your NMR ensemble in ChimeraX, select all
the models (typically just "sel #1" should do the trick), then use File/Open
and choose color_by_rms.py (or just "open color_by_rms.py" on the command line
if it's in your working directory). As long as all models have the same set of
residues, it should do the trick. Example image for 2kv5 attached.
Have fun!
-- Tristan
________________________________
From: CCP4 bulletin board <CCP4BB@JISCMAIL.AC.UK> on behalf of Harry Powell -
CCP4BB <0000193323b1e616-dmarc-requ...@jiscmail.ac.uk>
Sent: 26 May 2021 16:04
To: CCP4BB@JISCMAIL.AC.UK <CCP4BB@JISCMAIL.AC.UK>
Subject: [ccp4bb] Analysis of NMR ensembles
Hi
Given that there are plenty of people on this BB who are structural biologists
rather than “just” crystallographers, I thought someone here might be able to
help.
If I have a structure in the PDB (e.g. 2kv5) that is an ensemble of structures
that fit the NOEs, is there a tool available that will give me some idea about
the bits of the structure that do not vary much (“rigid”) and the bits that are
all over the place (“flexible”)?
Would superpose or gesamt be a good tool for this? Ideally I’d like something
that could add a figure to the B columns in a PDB file so I could see something
in QTMG (or PyMol if forced…) or do other useful things with the information.
Harry
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from chimerax.atomic import Residues, Atoms, selected_residues
from math import sqrt
import numpy
models = selected_residues(session).unique_structures
from chimerax.core.commands import run
run(session, f'match {"|".join(["#"+m.id_string for m in models[1:]])} to
#{models[0].id_string}')
for residues in zip(*[m.residues for m in models]):
residues = Residues(residues)
pas = Atoms(residues.principal_atoms)
if not len(pas):
residues.atoms.bfactors = 0
continue
coords = pas.scene_coords
avg = numpy.mean(coords, axis=0)
deviations = coords-avg
distances = numpy.linalg.norm(deviations, axis=0)
rms = sqrt(numpy.mean(distances**2))
residues.atoms.bfactors = rms
run(session, f'color bfactor sel')
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