POEM: Pocket-Oriented Elaboration of molecules

Description

This project aims at using information from ligand pieces bound to protein subpockets to (semi-) automatically build new molecules tailored to a particular target pocket on the basis of the subpockets/target pocket estimated similarities.
This workflow was tested to design new sub-micromolar hit candidates for CDK8 inhibition:

Eguida M., Schmitt-Valencia C., Hibert M., Villa, P. and Rognan, D. Target-Focused Library Design by Pocket-Applied Computer Vision and Fragment Deep Generative Linking. J. Med. Chem. 2022, 65, 13771–13783. https://doi.org/10.1021/acs.jmedchem.2c00931

Please note that the publication refers to release v1.0.0

Content

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

Requirements

• Conda: https://docs.conda.io/projects/conda/en/latest/user-guide/install/linux.html environements with python 3.6+.
• IChem: http://bioinfo-pharma.u-strasbg.fr/labwebsite/download.html
• ProCare: https://github.com/kimeguida/ProCare
• DeLinker: https://github.com/oxpig/DeLinker
• RDKit: http://www.rdkit.org, https://github.com/rdkit/rdkit

Part 1/ Subpocket screening and fragments preparation

1. Alignment of fragments'supocket clouds to target pocket cloud with ProCare

sc-PDB subpockets and fragments <br>

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

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

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

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

Outputs:

• aligned subpockets, fragments and procare_scores.tsv available on zenodo
• subpocket: cfh_xx_fragN_cavity4.mol2
• fragment: cfh_xx_fragN.mol2

2. Convert aligned fragments from mol2 to sdf

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

Outputs:

• sdf available on zenodo
• fragment: cfh_xx_fragN.sdf

3. Compute IChem interactions:

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

Outputs:

• interactions available on zenodo
• interactions file: cfh_xx_fragN.ifp <br>

Part 2/ Selection and annotation of relevant fragments

To reproduce this step, the required data out of steps 1-to-3 were made availaible at https://zenodo.org/record/7023191 as aligned_fragments.tgz and ouputs obtained output_files.tgz <br>

4. Select top-scored subpockets and annotate fragments according to six predefined CDK8 areas

tar -xzf aligned_fragments.tgz <br> cd aligned_fragments/ <br>

current directory: aligned_fragments containing data from steps 1-3 <br>

assignment of CDK8 areas <br> conda deactivate <br> conda activate delinker (state of our conda env: envs/delinker.yml)<br> 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 <br>

Outputs: <br>

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

Part 3/ Fragments linking

To reproduce these steps, the required data were made availaible at https://zenodo.org/record/7023191 as aligned_fragments.tgz and ouputs obtained output_files.tgz <br>

5. Enumerate candidates for linking: pairs of fragments and atoms

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 <br>

Outputs:

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

6. Linking with DeLinker

DeLinker: https://github.com/oxpig/DeLinker <br>

Feed DeLinker with connecatble candidates: <br> python ../scripts/delinker.py -f linkable_fragments_round1_<N>.list -p <your_path_to_DeLinker>/DeLinker/ > /dev/null <br> This step was distributed on computer clusters. <br> 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/

7. Check generation success

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

Outputs: <br>

• generation_complete.smi <br>
• generation_uncomplete.smi <br>

8. Name molecules and filter with openEye Filter

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

Output:

• generation_complete_indexed.smi <br>

<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 <br>

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. <br>

Output:

• druglike_molecules.smi <br>

9. Synthetic accessibility, descriptors, filtering:

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

• druglike_molecules_sascore.tsv <br>

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

Outputs: <br>

• druglike_molecules_descriptors.tsv
• generation_linker_descriptors.tsv

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

Output:

• linker_discarded.tsv <br>

10. Extract round 1 library

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 <br>

11. Grow molecules in round 2 library from a selected hit

Example of hit compound 12 <br> 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 <br>

Outputs: <br>

• hit12_round2_mols.tsv <br>
• hit12_round2_sascore_pass.tsv <br>

12. Filter and generate round 2 library

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

Output:

• hit12_round2_mols_descriptors.tsv <br>

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

Output:

• libr2.txt <br>

Help and issues

Signal issues

https://github.com/kimeguida/POEM/issues

Support contacts

Merveille Eguida: keguida'[at]'unistra'[dot]'fr
Didier Rognan, PhD: rognan'[at]'unistra'[dot]'fr

Prospective applications

1. Eguida, M.; Schmitt-Valencia, C.; Hibert, M.; Villa, P.; Rognan, D. Target-Focused Library Design by Pocket-Applied Computer Vision and Fragment Deep Generative Linking. J. Med. Chem. 2022, 65, 13771–13783. https://doi.org/10.1021/acs.jmedchem.2c00931

2. CACHE hits prediction Challenge #1 https://cache-challenge.org

Citation

If you use POEM, please cite:

@article{doi:10.1021/acs.jmedchem.2c00931,
abstract = {We here describe a computational approach (POEM: Pocket Oriented Elaboration of Molecules) to drive the generation of target-focused libraries while taking advantage of all publicly available structural information on protein-ligand complexes. A collection of 31 384 PDB-derived images with key shapes and pharmac