fragment-ranking

A method for ranking fragments by how much novel information they give about protein targets in fragment screens. When using the results of fragment screens on many diverse targets, this method has been shown to select a set of functionally diverse fragments that can get information more efficiently from new targets.

A custom version of ODDT [1] is included, which contains a module that generates an atomic-level interaction fingerprint.

Instructions for use

Conda or miniconda is required. Downloads and instructions can be found at: https://docs.conda.io/projects/conda/en/latest/. Once this requirement has been fulfilled, you can install fragment-ranking.

Installation:

1. clone repository

git clone https://github.com/annacarbery/fragment-ranking.git
cd fragment-ranking

2. create new conda environment and install pymol and rdkit (versions used in original analysis: pymol 2.4.1 and rdkit 2021.03.5)

conda create -n fragment-ranking -y
conda activate fragment-ranking
conda install -c schrodinger pymol -y
conda install -c conda-forge rdkit -y 

3. Install custom ODDT version (provided within fragment-ranking repository)

cd oddt
python setup.py install
cd ..

Required input files:

1. Fragment library, in sdf format.
2. Structures of targets with fragments bound, in pdb format. Structures should not have any atoms or residues missing.
3. JSON dictionary of pdb file with associated fragment (in SMILES string), for example : {"mArh-x1018.pdb": "Clc1ccc2nnnn2n1",...}

Example data is provided.

Run:

1. Generate interaction fingerprints from structures. Either residue-level or atomic-level interaction fingerprints can be used.

python src/generate_IFPs.py -IFP [atomic/residue] -sdf [path_to_fragment_library.sdf] -exps [path_to_experiments.json] -pdbs [path_to_pdb_files]

to test with example data:

python src/generate_IFPs.py -IFP atomic -sdf data/library.sdf -exps data/experiments_mArh.json -pdbs data/structures/

2. Rank fragments

python src/rank_fragments.py -sdf [path_to_fragment_library.sdf] -bits data/smiles_bits_[atomic/residue].json -o [output_file]

to test with example data:

python src/rank_fragments.py -sdf data/library.sdf -bits data/smiles_bits_atomic.json -o ranked_fragments.json

References

1. Wójcikowski, M., Zielenkiewicz, P., & Siedlecki, P. (2015). Open Drug Discovery Toolkit (ODDT): a new open-source player in the drug discovery field. Journal of Cheminformatics, 7(1), 26. doi:10.1186/s13321-015-0078-2