envs/ --> conda environments
cdk8_structures/ --> target structures
aligned_fragments.tgz available for download at 10.5281/zenodo.7023191 --> output data after steps 1-3, input for step 4+
scripts/ --> scripts to for library generation
output_files.tgz available for download at 10.5281/zenodo.7023191 --> data obtained at each step, and depedencies

Conda environements with python 3.6+.

sc-PDB subpockets and fragments

• input structures from the sc-PDB database
  
• fragmentation, cavity clouds and interactions computed with IChem
   for more information on fragmentation, sc-PDBFrag
  

CDK8 pocket
<this_repo>/cdk8_structures/5hbh_cavityALL_p0-p1-p6.mol2

ProCare: https://github.com/kimeguida/ProCare (state of our conda env: envs/procare.yml)

source <path_to_your_conda>
conda activate procare
cd aligned_fragments/
python ../scripts/procare_launcher.py -s <subpocket> -t ../cdk8_structures/5hbh_cavityALL_p0-p1-p6.mol2 --transform --ligandtransform <fragment>

Outputs: aligned subpockets, fragments and procare_scores.tsv

We used OpenEye python toolkits (state of our conda env: envs/oepython.yml):
conda deactivate
conda activate oepython
../scripts/convert.py <fragment>.mol2 <fragment>.sdf

<path_to_your_ichem>/IChem ../cdk8_structures/5hbh_protein.mol2 <fragment>.mol2 > <fragment>.ifp

To reproduce this step, the required data out of steps 1-to-3 were made availaible at link

current directory: aligned_fragments containing data from steps 1-3

assignment of CDK8 areas
conda deactivate
conda activate delinker (state of our conda env: envs/delinker.yml)
python ../scripts/select_fragments_round1.py -f ../output_files/procare_scores.tsv -d . -p ../cdk8_structures/5hbh_protein.mol2 -c ../cdk8_structures/5hbh_cavityALL_p0-p1-p6.mol2

Outputs:
subpocket_p0_gate.list which corresponds to GA1
subpocket_p0_hinge.list --> H
subpocket_p0_solv_1.list --> SE2
subpocket_p0_solv_2.list --> SE1
subpocket_p6_alphaC.list --> AC
subpocket_p6_lys52.list --> GA2
available in <this_repo>/output_files/

python ../scripts/linkable_fragments_round1_job.py --hinge subpocket_p0_hinge.list --gate subpocket_p0_gate.list --solv1 subpocket_p0_solv_1.list --solv2 subpocket_p0_solv_2.list --alphac subpocket_p6_alphaC.list --lys52 subpocket_p6_lys52.list

Outputs:
linkable_fragments_round1_<N>.list with N in {0, 1, 2, 3, 4, 5, 6} available in <this_repo>/output_files/

DeLinker: https://github.com/oxpig/DeLinker

Feed DeLinker with connecatble candidates:
python ../scripts/delinker.py -f linkable_fragments_round1_<N>.list -p <your_path_to_DeLinker>/DeLinker/ > /dev/null
This step was distributed on computer clusters.
Output: generation.smi renamed and zipped as generation_<N>.smi.gz with N in {0, 1, 2, 3, 4, 5, 6} available in <this_repo>/output_files/

Sometimes, no linker is generated and DeLinker might return truncated attempts.
python ../scripts/get_linker.py --file generation_<N>.smi.gz --fragsdir . --pathdelinker <your_path_to_DeLinker>/DeLinker/

Outputs:
generation_complete.smi
generation_uncomplete.smi

SMILES were assigned IDs to keep track of the molecules infos. Filter will protonate and generate canonical SMILES different from RDKit's.
python ../scripts/index_generated_molecules.py -i generation_complete.smi -o generation_complete_indexed.smi

Output: generation_complete_indexed.smi

<path_to_openeye>/filter -in generation_complete_indexed.smi -out druglike_molecules.smi -fail druglike_failed_molecules.smi -filter ../output_files/filter_labo_cdk8.txt

Check that other annotations in the file did not affect how Filter processed the SMILES. In our case, we extracted the SMILES and indexes to a separate file molecules.smi.

Output: druglike_molecules.smi

SAscore from https://github.com/rdkit/rdkit/blob/master/Contrib/SA_Score/sascorer.py
python ../scripts/get_sascore.py -i druglike_molecules.smi -o druglike_molecules_sascore.tsv
Output: druglike_molecules_sascore.tsv

RDKit descriptors
python ../scripts/get_druglike_descriptors.py -i druglike_molecules.smi -o druglike_molecules_descriptors.tsv
python ../scripts/get_linker_descriptors.py -i generation_complete_indexed.smi -o generation_linker_descriptors.tsv

Outputs:
druglike_molecules_descriptors.tsv
generation_linker_descriptors.tsv

Clean generated linkers, remove too flexible, hydrophobic
python ../scripts/filter_linker.py -i generation_linker_descriptors.tsv -o linker_discarded.tsv

Output: linker_discarded.tsv

python ../scripts/library_round1.py --descriptor druglike_molecules_descriptors.tsv --sascore druglike_molecules_sascore.tsv --discarded linker_discarded.tsv -o libr1.txt

Output: libr1.txt

Example of hit compound 12
python ../scripts/round2_fuse_mols.py --dl druglike_molecules.smi --gen generation_complete_indexed.smi --origin ../output_files/frag_origin.tsv --discarded linker_discarded.tsv --procare ../output_files/procare_scores.tsv

Outputs:
hit12_round2_mols.tsv
hit12_round2_sascore_pass.tsv

RDKit descriptors
python ../scripts/get_round2_descriptors.py -i hit12_round2_mols.tsv -o hit12_round2_mols_descriptors.tsv

Output: hit12_round2_mols_descriptors.tsv

candidates for synthesis
python ../scripts/library_round2.py -i hit12_round2_mols_descriptors.tsv --sascore hit12_round2_sascore_pass.tsv -o libr2.txt

Output:libr2.txt