[Chimera-users] Clustering docking results using "headless" Chimera

[Conrad Huang]

Yes, a script is still probably the easiest way to do what you want. 
However, there is a call in the old script that no longer exists in the 
new one.  I'm attaching a new script that will do a couple things 
differently than before:

- Instead of opening files in the script, it will now use models that 
have already been opened.
- It will call matchmaker to align all models to the model with the 
lowest model/submodel number (usually the first model opened).

To run it, save the script as "cluster.py" and run the command:

	chimera --nogui --silent your_model_files cluster.py

where cluster.py is the name of the script.  The first model opened will 
be the reference structure to which everything else is aligned.

In the script, Matchmaker is invoked by the line:

	chimera.runCommand("mm %s %s" % (ref.oslIdent(), m.oslIdent()))

If you want to use more options (see 
http://www.cgl.ucsf.edu/chimera/docs/UsersGuide/midas/mmaker.html for 
details), just add it after the second "%s" on the line.

Please let me if this does what you need, or if you'd like it to do more 
stuff.

Conrad


On 6/26/2012 7:57 PM, Hurt, Darrell (NIH/NIAID) [E] wrote:
> Hi there,
>
> I want to use Chimera on a server (or in an automated fashion by
> command line; "-nogui") to read in a bunch (1000s) of PDB structures
> (each having an identical number of atoms) and then do clustering
> using the NMRCLUST code that you've so conveniently adapted.
>
> I found this old thread:
> http://www.cgl.ucsf.edu/pipermail/chimera-users/2008-October/003193.html
>
>  Is this still the preferred method for doing what I'm hoping?
>
> Also, is it possible to do a MatchMaker alignment before the
> clustering?
>
> Thanks, Darrell
>
>
> -- Darrell Hurt, Ph.D. Section Head, Computational Biology
> Bioinformatics and Computational Biosciences Branch (BCBB)
> OCICB/OSMO/OD/NIAID/NIH
>
> 31 Center Drive, Room 3B62B, MSC 2135 Bethesda, MD 20892-2135 Office
> 301-402-0095 Mobile 301-758-3559
> http://bioinformatics.niaid.nih.gov<http://bioinformatics.niaid.nih.gov/>
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> (Public)
>
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-------------- next part --------------
def clusterModels(modelList):
	from EnsembleMatch.distmat import DistanceMatrix
	from chimera import match
	# First we need to construct a full distance matrix, including
	# possibly identical elements
	fulldm = DistanceMatrix(len(modelList))
	sameAs = {}
	atoms = [ match._coordArray(m.atoms) for m in modelList ]
	for i in range(len(modelList)):
		mi = modelList[i]
		if mi in sameAs:
			continue
		ai = atoms[i]
		for j in range(i + 1, len(modelList)):
			aj = atoms[j]
			m = match.Match(ai, aj)
			if m.rms <= 0:
				# Detect identical elements and track them
				mj = modelList[j]
				sameAs[mj] = mi
			fulldm.set(i, j, m.rms)
	# Now we reduce the distance matrix by removing identical elements
	if not sameAs:
		# No identical elements, just use the whole thing
		dm = fulldm
		models = modelList
	else:
		models = []
		indexMap = []
		for i, mi in enumerate(modelList):
			if mi in sameAs:
				# Skip identical element
				continue
			models.append(mi)
			indexMap.append(i)
		dm = DistanceMatrix(len(models))
		for i in range(len(models)):
			im = indexMap[i]
			for j in range(i + 1, len(models)):
				jm = indexMap[j]
				dm.set(i, j, fulldm.get(im, jm))
	# Next we run the clustering algorithm
	from EnsembleMatch.nmrclust import NMRClust
	nmrc = NMRClust(dm)
	# Next reformat the results into something useful.
	# "clusterInfo" is a list of whose elements are 2-tuples of
	# (cluster representative model, list of models in cluster)
	clusterInfo = []
	for cid, c in enumerate(nmrc.clusters):
		members = c.members()
		mList = []
		for member in members:
			m = models[member]
			m.clusterId = cid
			m.clusterRep = 0
			mList.append(m)
		rep = nmrc.representative(c)
		m = models[rep]
		m.clusterRep = len(members)
		clusterInfo.append((m, mList))
	for mj, mi in sameAs.iteritems():
		mj.clusterId = mi.clusterId
		mj.clusterRep = 0
		rep, members = clusterInfo[mi.clusterId]
		rep.clusterRep += 1
		members.append(mj)
	return clusterInfo

# Here's an example on how you can use the function above.
# Run this script with:
#	chimera --nogui --silent your_model_files cluster.py
# where cluster.py is the name of this file.  The first model
# opened will be the reference structure to which everything
# else is aligned.
if __name__ == "chimeraOpenSandbox":
	import chimera
	models = chimera.openModels.list(modelTypes=[chimera.Molecule])
	ref = models[0]
	for m in models[1:]:
		chimera.runCommand("mm %s %s" % (ref.oslIdent(), m.oslIdent()))
	ci = clusterModels(models)
	print len(models), "models ->", len(ci), "clusters"
	for rep, members in ci:
		print rep.oslIdent(), "(%d member)" % len(members)
		for m in members:
			print '\t', m.oslIdent()