Docking Analysis in DOCK3.8
Location of new scripts/Install Instructions
All programs described are located on this directory for now. Copy the directory to your own $HOME or wherever you see fit. Github link soon.
Note the link to python3.8 in this directory. You need to include a link to a python3.8 executable in your personal bin directory. There are no pip requirements, just a blank python 3.8 install. You can also just use mine @ /wynton/home/btingle/soft/python-3.8-install/bin/python3.8
Main pose retrieval algorithm, runs on multiple cores. 7 cores is recommended and also the default.
If input is a file, each line in the file should map to a valid pose file, e.g:
/wynton/group/bks/work/yingyang/5HT-1d/04_LSD/run_dock_es1.5_ld0.3/docked_chunks/chunk0000/test.mol2.gz /wynton/group/bks/work/yingyang/5HT-1d/04_LSD/run_dock_es1.5_ld0.3/docked_chunks/chunk0001/test.mol2.gz /wynton/group/bks/work/yingyang/5HT-1d/04_LSD/run_dock_es1.5_ld0.3/docked_chunks/chunk0002/test.mol2.gz /wynton/group/bks/work/yingyang/5HT-1d/04_LSD/run_dock_es1.5_ld0.3/docked_chunks/chunk0003/test.mol2.gz /wynton/group/bks/work/yingyang/5HT-1d/04_LSD/run_dock_es1.5_ld0.3/docked_chunks/chunk0004/test.mol2.gz /wynton/group/bks/work/yingyang/5HT-1d/04_LSD/run_dock_es1.5_ld0.3/docked_chunks/chunk0005/test.mol2.gz /wynton/group/bks/work/yingyang/5HT-1d/04_LSD/run_dock_es1.5_ld0.3/docked_chunks/chunk0006/test.mol2.gz /wynton/group/bks/work/yingyang/5HT-1d/04_LSD/run_dock_es1.5_ld0.3/docked_chunks/chunk0007/test.mol2.gz /wynton/group/bks/work/yingyang/5HT-1d/04_LSD/run_dock_es1.5_ld0.3/docked_chunks/chunk0008/test.mol2.gz /wynton/group/bks/work/yingyang/5HT-1d/04_LSD/run_dock_es1.5_ld0.3/docked_chunks/chunk0009/test.mol2.gz
Output is where the top 300K poses will be written out when the script has finished. e.g /scratch/top_poses.mol2.gz
python3.8 top_poses.py <input> <output> <<ncores>>
run_top_poses.bash <input> <output>
Typical qsub usage
qsub -wd $PWD run_top_poses.bash <input> <output>
Map-reduce script to submit a number of analysis jobs and combine their results. The preferred method of running large analysis workloads.
Input field is evaluated the same as in top_poses.py.
Staging directory should be an NFS directory writable by your user. This is where input/output will be stored by the script.
Final output will show up in <staging directory>/output_final.poses.mol2.gz
Batch size refers to how many poses files will be evaluated by each job, the default is 1000, though you may want to modify this depending on the properties of your poses files/how many there are.
Only works on sge for right now. Tested on Wynton.
run_top_poses_mr.bash <input> <staging directory> <<batch size>>
After your jobs have finished, check the logs to see if anything went wrong.
If everything went smoothly, there should be an output file corresponding to each input file, there should be nothing in the .err logs, and each .out log should end with a string of text that looks like this:
received all input! joining threads... done processing! writing out... 299900 / 300000
If you find an output file that doesn't end like this, you may wish to re-attempt that particular job.
You may also see a message that looks like this:
short timeout reached while retrieving pose... trying again! curr=...
This just indicates slowness in the file reading, and is common to see at the beginning of a log or when the filesystem is under high load.