Complex-based Ligand-Binding Proteins Redesign by Equivariant Diffusion-based Generative Models

Preprint: https://www.biorxiv.org/content/10.1101/2024.04.17.589997v1.full.pdf

DOI: https://doi.org/10.1101/2024.04.17.589997

Contributors:

• Nhan Nguyen
• Duy Nguyen
• Truong Son Hy (Correspondent / PI)

The architecture of the code draws inspiration from DPL (Diffusion model for Protein–Ligand complexes) developed by Nakata, S., Mori, Y. & Tanaka, S. End-to-end protein–ligand complex structure generation with diffusion-based generative models. BMC Bioinformatics 24, 233 (2023). https://doi.org/10.1186/s12859-023-05354-5

The main functionalities from DPL include, but not limited to:

• Featurization of ligand SMILES
• SE3 equivariant denoising (Triangular Multiplicative Updates)
• Training process and generation scripts

The main innovations we made away from DPL, but not limited to:

• Stochastically masking & featurization of protein sequences
• Adaptations of Single Representation Attention and Outer Product Mean from AF2
• Parameterization of \beta_T diffusion (instead of using variational lower bound in DPL)
• Denoising through both sequence and structure spaces
• Flexible generation output (sequences only, sequence-structures)

Setup Environment

Clone this repository and install dependencies:

git clone https://github.com/HySonLab/Protein_Redesign.git
cd Protein_Redesign
conda env create -f environment.yml
conda activate ProteinReDiff

Download model parameters:

gdown --fuzzy --folder https://drive.google.com/drive/folders/1rPlzMUPgKLFd_Krk8cGqhEeitWByPOMn?usp=sharing

Additionally, TMalign is required to align generated structures. You can install it as follows:

wget https://zhanggroup.org/TM-align/TMalign.cpp
g++ -static -O3 -ffast-math -lm -o TMalign TMalign.cpp
chmod +x TMalign
export PATH="/path/to/TMalign:$PATH"

Sample generation

Generate single complex structure using ProteinReDiff:

python generate.py \
    --ckpt_path "checkpoints/PRD_ver1.ckpt"\
    --output_dir "workdir/inference/example_ProteinReDiff" \
    --protein "LSEQLKHCNGILKELLSKKHAAYAWPFYKPVDASALGLHDYHDIIKHPMDLSTVKRKMENRDYRDAQEFAADVRLMFSNCYKYNPPDHDVVAMARKLQDVFEFRYAKMPD" \
    --ligand "Cc1ccc2c(c1c3cc(cc4c3nc([nH]4)C5CC5)c6c(noc6C)C)cccn2" \
    --num_samples 3 \
    --num_steps 1000

The sequence can be masked prior to input using X token (mask_prob is the fraction of input protein residues to be masked from 0.0 to 1.0):

python generate.py \
    --ckpt_path "checkpoints/PRD_ver1.ckpt"\
    --output_dir "workdir/inference/example_ProteinReDiff" \
    --protein "LSEQXXXXNGILKELLSKXXXXYAWPFYKPVDASALGLHDYHDIIKXXXXLSTVKRKMENRDYRDXXXXAADVRLMFSNCYKYNPPDHDVVAMARKLQDVFEFRYAKMPD" \
    --ligand "Cc1ccc2c(c1c3cc(cc4c3nc([nH]4)C5CC5)c6c(noc6C)C)cccn2" \
    --num_samples 4 \
    --num_steps 1000 \
    --mask_prob 0.0

Generate structure ensembles without ligand (use the dummy token *):

python generate.py \
    --ckpt_path "checkpoints/PRD_ver1.ckpt"\
    --output_dir "workdir/inference/example_ProteinReDiff" \
    --protein "LSEQLKHCNGILKELLSKKHAAYAWPFYKPVDASALGLHDYHDIIKHPMDLSTVKRKMENRDYRDAQEFAADVRLMFSNCYKYNPPDHDVVAMARKLQDVFEFRYAKMPD" \
    --ligand "*" \
    --num_samples 3 \
    --num_steps 1000

Generate multiple complex structures:

python -m scripts.predict_batch_strc_msk_inp \
  --ckpt_path "checkpoints/PRD_ver1.ckpt" \
  --output_dir "workdir/inference/example_ProteinReDiff" \
  --fasta "./scripts/test_sequences_from_pdb.fasta" \
  --ligand_file './scripts/scripts.smiles' \
  --accelerator "gpu"\
  --num_gpus 1 \
  --batch_size 1 \
  --num_samples 1 \
  --mask_prob 0.5 \
  --num_steps 1000

Alternatively, generate multiple samples per sequences only (mask_prob can be adjusted to increase diversity, best sequences are masked below 0.5):

python -m scripts.predict_batch_seq_msk_inp \
  --ckpt_path "checkpoints/PRD_ver1.ckpt" \
  --output_dir "workdir/inference/example_ProteinReDiff" \
  --fasta "./scripts/test_sequences_from_pdb.fasta" \
  --ligand_file './scripts/test_pdb.smiles' \
  --accelerator "gpu"\
  --num_gpus 1 \
  --batch_size 1 \
  --num_samples 10 \
  --mask_prob 0.3 \
  --num_steps 1000

Training

Download the PDBbind dataset from https://zenodo.org/record/6408497 and unzip it.

Move the resulting PDBBind_processed directory to ./data/.

Preprocess the dataset:

python preprocess_pdbbind.py

Finally, run the training script:

python train.py \
    --num_workers 8 \
    --batch_size 1 \
    --accumulate_grad_batches 8 \
    --save_dir "workdir/train/example_ProteinReDiff" \
    --single_dim 256 \
    --pair_dim 32 \
    --num_blocks 4

Please modify the batch_size, gpus, and accumulate_grad_batches arguments according to your machine(s). Also, use the flag data_dir for directory containing your weights (ie. "./data") and save_dir for directory to save training log files.

Default values can be used to reproduce the settings used in our paper:

python train.py \
    --training_mode \
    --num_gpus 1\
    --num_workers 30 \
    --batch_size 2 \
    --accumulate_grad_batches 10 \
    --save_dir "workdir/train/example_ProteinReDiff" \
    --single_dim 512 \
    --mask_prob 0.15 \
    --pair_dim 64 \
    --num_steps 2000 \
    --num_blocks 4

Due to the limitation of runtime on GPUs, we prepared a train_from_ckpt.py script to further train on finished epoch:

python train_from_ckpt.py \
    --training_mode \
    --num_gpus 1\
    --num_workers 30 \
    --batch_size 2 \
    --accumulate_grad_batches 8 \
    --save_dir "$save_dir" \
    --single_dim 512 \
    --mask_prob 0.15 \
    --pair_dim 64 \
    --num_steps 1000 \
    --num_blocks 4 \
    --trained_ckpt "checkpoints/PRD_ver1.ckpt"

Acknowledgements

This work is primarily based on the following repositories:

• https://github.com/shuyana/DiffusionProteinLigand (modules, training, inference scripts)
• https://github.com/aqlaboratory/openfold (AF2 modules)
• https://github.com/deepmind/alphafold (AF2 modules, protein sequence procesing)
• https://github.com/facebookresearch/esm (protein featurization)
• https://github.com/HannesStark/EquiBind (partitions of train/validation/test sets)

Please cite our work as follows

@article {Nguyen2024.04.17.589997,
	author = {Viet Thanh Duy Nguyen and Nhan Nguyen and Truong Son Hy},
	title = {Complex-based Ligand-Binding Proteins Redesign by Equivariant Diffusion-based Generative Models},
	elocation-id = {2024.04.17.589997},
	year = {2024},
	doi = {10.1101/2024.04.17.589997},
	publisher = {Cold Spring Harbor Laboratory},
	abstract = {Proteins, serving as the fundamental architects of biological processes, interact with ligands to perform a myriad of functions essential for life. The design and optimization of ligand-binding proteins are pivotal for advancing drug development and enhancing therapeutic efficacy. In this study, we introduce ProteinReDiff, a novel computational framework designed to revolutionize the redesign of ligand-binding proteins. Distinguished by its utilization of Equivariant Diffusion-based Generative Models and advanced computational modules, ProteinReDiff enables the creation of high-affinity ligand-binding proteins without the need for detailed structural information, leveraging instead the potential of initial protein sequences and ligand SMILES strings. Our thorough evaluation across sequence diversity, structural preservation, and ligand binding affinity underscores ProteinReDiff{\textquoteright}s potential to significantly advance computational drug discovery and protein engineering. Our source code is publicly available at https://github.com/HySonLab/Protein_RedesignCompeting Interest StatementThe authors have declared no competing interest.},
	URL = {https://www.biorxiv.org/content/early/2024/04/20/2024.04.17.589997},
	eprint = {https://www.biorxiv.org/content/early/2024/04/20/2024.04.17.589997.full.pdf},
	journal = {bioRxiv}
}