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FlexPose

FlexPose, a framework for AI-based flexible modeling of protein-ligand binding pose.

Fig1_b

A free light-weight web server can be found here.

Table of contents

Installation

Install prerequisite packages

FlexPose is implemented in PyTorch. All basic dependencies are listed in requirements.txt and most of them can be easily installed with pip install. We provide tested installation commands in install_cmd.txt for your reference.

Install FlexPose pacakge

pip install -e .

Usage

Prediction

You can use the FlexPose as follows in demo.py:

from FlexPose.utils.prediction import predict as predict_by_FlexPose

predict_by_FlexPose(
    protein='./FlexPose/example/4r6e/4r6e_protein.pdb',               # a protein path, or a list of paths
    ligand='./FlexPose/example/4r6e/4r6e_ligand.mol2',                # a ligand path (or SMILES), or a list of paths (or SMILES)
    ref_pocket_center='./FlexPose/example/4r6e/4r6e_ligand.mol2',     # a ligand-like file for selecting pocket, e.g. predictions from Fpocket
    # batch_csv='./FlexPose/example/example_input.csv',               # for batch prediction

    device='cuda:0',                                                  # device
    structure_output_path='./structure_output',                       # structure output
    output_result_path='./output.csv',                                # record output
)
Arguments Descriptions
protein Input proteins (a list of paths)
ligand Input ligands (a list of paths)
ref_pocket_center Ligand-like files for pocket selection (a list of paths)
batch_csv Batch prediction
ens Ensemble number
structure_output_path A folder for saving predicted structures
output_result_path A csv file for saving records
min Energy minimizion
min_loop Energy minimizion loops
min_constraint Constraint energy minimizion constant (kcal/mol/Å^2)
model_conf Output model confidence
device Device
batch_size Batch size
prepare_data_with_multi_cpu Prepare inputs with multiprocessing

Training

Here, we provide a pipeline for training a model on the PDBbind and APObind datasets, and it is recommended to run these scripts in the root directory of FlexPose.

Data augmentation (Optional)

We use Rosetta to generate fake apo conformations from holo conformations. For each training iteration, there is a small probability that the model is trained with these fake conformations.

python FlexPose/preprocess/aug_pseudo_apo.py \
--apobind_path path/to/apobind \
--pdbbind_path path/to/pdbbind \
--save_path path/for/saving \
--n_rand_pert 3 \
--n_fixbb_repack 3 \
--n_flexbb_repack 3

You need to set --apobind_path and --pdbbind_path to path of the decompressed APObind and PDBbind, and set the --save_path to a folder to save data augmentation.

NOTE: Generating all conformations takes hours to days (depending on the number of CPU cores used). We recommend performing the data augmentation on computers with multiple CPU cores. Alternatively, you can set --n_rand_pert, --n_fixbb_repack and --n_flexbb_repack to 0 to skip most of the processing.

Data preprocessing (Optional)

After data augmentation, now we can generate input files for training:

python FlexPose/preprocess/prepare_APOPDBbind.py \
--apobind_path path/to/apobind \
--pdbbind_path path/to/pdbbind \
--save_path path/for/saving \
--apo_info_path path/to/apobind_all.csv \
--aff_info_path path/to/INDEX_general_PL_data.{year} \
--aug_path path/to/data/augmentation \
--tmp_path ./tmp \
--max_len_pocket 150 \
--max_len_ligand 150

You need to set --apobind_path and --pdbbind_path to path of the decompressed APObind and PDBbind (same settings as in the data augmentation), and set the --save_path to a new folder to save preprocessed data. --apo_info_path is the path to apobind_all.csv, which is provided by APObind. --aff_info_path is the path to INDEX_general_PL_data.{year}, which is provided by PDBbind.

NOTE: Set --max_len_pocket and --max_len_ligand to a small number (e.g. 64) to get a toy dataset, which can speed up training.

Train your own model

If you want to skip data augmentation and data preprocessing, the preprocessed data can be found here. Now, we can train a toy FlexPose by running:

python FlexPose/train/train_APOPDBbind.py \
--data_path path/to/preprocessed/data \
--data_list_path path/to/data/split \
--batch_size 3 \
--lr 0.0005 \
--n_epoch 200 \
--dropout 0.1 \
--use_pretrain False \
--c_x_sca_hidden 32 \
--c_edge_sca_hidden 16 \
--c_x_vec_hidden 16 \
--c_edge_vec_hidden 8 \
--n_head 2 \
--c_block 2 \
--c_block_only_coor 1

You need to set the --data_path to the preprocessed data and set the --data_list_path to a path for saving splited data IDs.

Besides, you can set --use_pretrain to True to use pre-trained encoders, and set (--pretrain_protein_encoder, --pretrain_ligand_encoder) to the path of pre-trained parameters, respectively (or set them to None to load our pre-trained encoders). We freeze pre-trained parameters by default to improve training efficiency.

Model confidence visualization

Fig_conf

You can visualize model confidence with PyMol:

spectrum b, red_white_green, minimum=0, maximum=1

License

Released under the MIT license.

Citation

If you find our model useful in your research, please cite the relevant paper:

@article{dong2023equivariant,
  title={Equivariant Flexible Modeling of the Protein--Ligand Binding Pose with Geometric Deep Learning},
  author={Dong, Tiejun and Yang, Ziduo and Zhou, Jun and Chen, Calvin Yu-Chian},
  journal={Journal of Chemical Theory and Computation},
  year={2023},
  publisher={ACS Publications}
}