Baseline update

The Kripo data set is generated from scratch every year or when algorithms change.

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

Use directory listing of new pdb files as input:



Too slow when run on single cpu. Chunkify input, run in parallel and merge results

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:

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: >

3. Pharmacophores

The raw pharmacophores are stored in the FRAGMENT_PPHORES sub-directory. Each pocket has a * 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 need to be added to the pharmacophores datafile with:

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.sqlite

Step 4 and 5 can be submitted to scheduler with:

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

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/

7. Calculate similarity scores between fingerprints

The similarities between fingerprints can be calculated with:

all_chunks=$(ls *fp.gz |wc -l)
jid_fpunzip=$(sbatch --parsable -n $all_chunks -J fpunzip --dependency=afterok:$jid_dups $SCRIPTS/
nr_chunks="$(($all_chunks * $all_chunks / 2 - $all_chunks))"
jid_fpneigh=$(sbatch --parsable -n $nr_chunks -J fpneigh --dependency=afterok:$jid_fpunzip $SCRIPTS/
jid_fpzip=$(sbatch --parsable -n $all_chunks -J fpzip --dependency=afterok:$jid_fpneigh $SCRIPTS/
jid_merge_matrices=$(sbatch --parsable -n 1 -J merge_matrices --dependency=afterok:$jid_fpneigh $SCRIPTS/

To prevent duplicates similarities of a chunk against itself should ignore the upper triangle.


Don’t fpneigh run sequentially but submit to batch queue system and run in parallel

8. Convert pairs file into dense similarity matrix


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

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/

The output of this step is ready to be served as a webservice using the kripodb serve command.

9. Switch staging to current

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

The staging can be made current with the following commands:

mv current old
mv staging current

10.0 Update web service

The webservice running at 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 The content version.txt must be updated.