Hi Martin,
> I use the following script to prepare small side chain fragment PDB
> files of mutants in PyMOL:
> http://pastebin.com/nDTZApHP
> I use the PyMOL built-in mutagenesis wizard and the sculpting wizard to
> locally optimize the side chains within the environment of the protein.
> For large numbers of side chains, this starts to take a long amoutn of time.
> Is it possible to parallelize the mutagenesis process? PyMOL is
> non-responsive while executing the script. Can it be run in the
> background of PyMOL?
This looks like an interesting problem, that if I had enough free
time, I'd dig into. I don't so, let me at least offer a few comments.
First, PyMOL can have more than one wizard at once:
cmd.wizard("mutagenesis")
w = cmd.get_wizard()
cmd.wizard("mutagenesis")
w2 = cmd.get_wizard()
# Should print False
print w1==w2
Next, if you can use the wizards via the "w" or "w2" handles then you
could create a series of wizards like this, one for each CPU, that
could operate on a certain residue in a safe multi-threaded way, maybe
something like the following ad hoc scenario:
(1) creating a function that takes as input a structure name, and
residue target range, and a token (to get unique names) eg.
def my_partial_mutate(str_name, sel, token):
tmp_name = cmd.get_unused_name(token)
cmd.create(tmp_name, str_name)
# ... now run your mutation code on tmp_name
cmd.save(unique_file_name, tmp_name)
cmd.delete(tmp_name)
(2) creating a pool of N threads where N=num_cpus that run the
function from step 1
import multiprocessing
ncpus = multiprocessing.cpu_count()
(3) iteratively call the function from (1) using a thread from (2)
where the function is passed a _new_ copy of the structure created
with the "copy" or "create" commands.
for x in range(get_mutation_list):
while running_threads == ncpus:
wait a sec
t = Threading.thread(target=my_partial_mutate,args=(my_structure_name,
x, tnum))
t.start()
If you use separate wizards and objects I don't think you have to
worry about locking -- you can just blow through the tasks being
limited by CPUs. All this could be wrong as I haven't put too much
time into it, but it might be a start.
Check out the official Python docs for multithreaded programming.
One last option is to divide the task into into N/M subtasks, where
N=num residues to mutate and M=num CPUs, and run that script from the
command line passing in residues ranges. This would require the least
amount of effort, maximize CPU usage, and be thread-safe as you'd be
running (N/M) separate processes.
Good luck!
Cheers,
-- Jason
--
Jason Vertrees, PhD
PyMOL Product Manager
Schrödinger, Inc.
(e) jason.vertr...@schrodinger.com
(o) +1 (603) 374-7120
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