Incremental update

Contents

• Incremental update
  • 1. Create staging directory
  • 2. Create sub-pocket pharmacophore fingerprints
  • 3. Create fragment information
    • 1. Fragment shelve
    • 2. Fragment sdf
    • 3. Pharmacophores
  • 4. Add new fragment information to fragment sqlite db
  • 5. Populate PDB metadata in fragments database
  • 6. Check no fragments are duplicated
  • 7. Calculate similarity scores between fingerprints
  • 8. Convert pairs file into dense similarity matrix
  • 9. Switch staging to current
    • 9.1 Merge fingerprint files (optional)
  • 10.0 Update web service

The Kripo data set can be incrementally updated with new PDB entries.

1. Create staging directory

Setup path with update scripts using:

export SCRIPTS=$PWD/../kripodb/update_scripts

Create a new directory:

mkdir staging
cd ..

2. Create sub-pocket pharmacophore fingerprints

The ids.txt file must contain a list of PDB identifiers which have not been processed before. It can be fetched from https://www.rcsb.org/.

Adjust the PDB save location in the singleprocess.py script to the staging directory.

Run the following command to generate fragments/pharmacophores/fingerprints for each PDB listed in ids.txt:

python singleprocess.py

3. Create fragment information

1. Fragment shelve

Where the fragment came from is stored in a Python shelve file. It can be generated from the pharmacophore files using:

compiledDatabase.py

2. Fragment sdf

The data generated thus far contains the molblocks of the ligands and atom nrs of each fragment. The fragment molblocks can be generated into a fragment sdf file with:

fragid2sd.py fragments.shelve > fragments.sd

3. Pharmacophores

The raw pharmacophores are stored in the FRAGMENT_PPHORES sub-directory. Each pocket has a *_pphore.sd.gz file which contains the pharmacophore points of the whole pocket and a *_pphores.txt file which contains the indexes of pharmacophore points for each sub pocket or fragment. The raw pharmacophores of the update can be added to the existing pharmacophores datafile with:

cp ../current/pharmacophores.h5 .
kripodb pharmacophores add FRAGMENT_PPHORES pharmacophores.h5

4. Add new fragment information to fragment sqlite db

The following commands add the fragment shelve and sdf to the fragments database:

cp ../current/fragments.sqlite .
kripodb fragments shelve fragments.shelve fragments.sqlite
kripodb fragments sdf fragments.sd fragments.sqlite

Step 4 and 5 can be submitted to scheduler with:

jid_db=$(sbatch --parsable -n 1 -J db_append $SCRIPTS/db_append.sh)

5. Populate PDB metadata in fragments database

The following command will updated the PDB metadata to fragments database:

kripodb fragments pdb fragments.sqlite

6. Check no fragments are duplicated

The similarity matrix can not handle duplicates. It will result in addition of scores:

jid_dups=$(sbatch --parsable -n 1 -J check_dups --dependency=afterok:$jid_db $SCRIPTS/incremental_duplicates.sh)

7. Calculate similarity scores between fingerprints

The similarities between the new and existing fingerprints and between new fingerprints themselves can be calculated with:

current_chunks=$(ls ../current/*fp.gz |wc -l)
all_chunks=$(($current_chunks + 1))
jid_fpneigh=$(sbatch --parsable -n $all_chunks -J fpneigh --dependency=afterok:$jid_dups $SCRIPTS/incremental_similarities.sh)
jid_merge_matrices=$(sbatch --parsable -n 1 -J merge_matrices --dependency=afterok:$jid_fpneigh $SCRIPTS/incremental_merge_similarities.sh)

8. Convert pairs file into dense similarity matrix

Note

Converting the pairs file into a dense matrix goes quicker with more memory.

The frame size (-f) should be as big as possible, 100000000 requires 6Gb RAM.

The following commands converts the pairs into a compressed dense matrix:

jid_compress_matrix=$(sbatch --parsable -n 1 -J compress_matrix --dependency=afterok:$jid_merge_matrices $SCRIPTS/freeze_similarities.sh)

The output of this step is ready used to find similar fragments, using either the webservice with the kripodb serve command or with the kripodb similarities similar command directly.

9. Switch staging to current

The webserver and webservice are configure to look in the current directory for files.

The current and new pharmacophores need to be combined:

mv staging/FRAGMENT_PPHORES staging/FRAGMENT_PPHORES.new
rsync -a current/FRAGMENT_PPHORES staging/FRAGMENT_PPHORES
rm -r staging/FRAGMENT_PPHORES.new

Todo

rsync of current/FRAGMENT_PPHORES to destination, maybe too slow due large number of files. Switch to move old pharmacohores and rsync new pharmacophores into it when needed.

The current and new fingerprints need to be combined:

cp -n current/*.fp.gz staging/

The staging can be made current with the following commands:

mv current old && mv staging current

9.1 Merge fingerprint files (optional)

To keep the number of files to a minimum it is advised to merge the fingerprint files from incremental updates of a year.

The incremental fingerprint files are named like out.<year><week>.fp.gz, to generate kripo_fingerprints_<year>_fp.gz run:

sbatch --parsable -n 1 -J merge_fp $SCRIPTS/incremental_merge_fp.sh <year>

10.0 Update web service

The webservice running at http://3d-e-chem.vu-compmedchem.nl/kripodb must be updated with the new datafiles.

The following files must copied to the server

• fragments.sqlite
• pharmacophores.h5
• similarities.packedfrozen.h5

The webservice must be restarted.

To show how up to date the webservice is the release date of the latest PDB is stored in version.txt which can be reached at http://3d-e-chem.vu-compmedchem.nl/kripodb/version.txt The content version.txt must be updated.