Neural Iterative Selection Expansion (NISE) using LASErMPNN/LigandMPNN with Boltz-1x/Boltz-2x

Introduced in the paper Zero-shot design of drug-binding proteins via neural selection-expansion!

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Check out our interactive Google Colab notebook to get acquainted with the protocol. Novel protein-ligand poses can be generated with CARPdock which we provide a separate interactive Google Colab notebook for running.

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Jointly optimize the sequence and structure of a protein-ligand binding pose with iterative selection-expansion.

Installing NISE Environment

To run NISE, install the dependencies (LASErMPNN and Boltz-2) using either of the methods below:

1. (Recommended) Create a Virtual Environment (venv) inside this repository.

1. Install uv if your system does not already have it installed. See here for instructions on how to do this.

2. Add uv to your path. For example, edit your ~/.bashrc to contain the line export PATH="~/.local/bin/uv:$PATH".

3. Run the setup.sh with the command bash setup.sh

This will install a new python environment containing the dependencies for LASErMPNN located at ./.venv/bin/python (the setup.sh script will print out the install location so you can verify this.)

2. Using separately installed LASErMPNN and Boltz conda environments.

You may wish to not install Boltz-2 again if it is already installed on your HPC. You can separately install LASErMPNN in its own conda environment setup the NISE repo following the instructions below.

1. Install LASErMPNN conda environment following the instructions in the README.md at the linked repository.

2. Follow this set of commands inside the NISE project directory after git clone-ing the project and installing the lasermpnn environment.

git submodule update --init --recursive

tar -xvf hetdict.tar.gz

conda activate lasermpnn

cd ./LigandMPNN
bash get_model_params.sh "./model_params"

3. Activate your conda environment containing Boltz-1x or Boltz-2x and run which boltz to get the path to the executable you call when running boltz predict commands. You will need to update this path in run_nise_boltz1x.py or run_nise_boltz2x.py respectively.

Running NISE with LigandMPNN

You may wish to run NISE trajectories using LigandMPNN in place of LASErMPNN.

To do this, install LigandMPNN into a separate python environment (its dependencies conflict with LASErMPNN) and update ./run_nise_boltz2x_ligandmpnn.py with the path to your LigandMPNN python executable. With the ligandmpnn python environment activated, run which python to get the path to your LigandMPNN python executable and update the ligandmpnn_python parameter at the bottom of ./run_nise_boltz2x_ligandmpnn.py.

Generating input poses:

We recommend generating NISE input poses using the workflow outlined here using CARPdock,. CARPdock is likely the fastest way to get a good starting point and has been experimentally validated on some (currently) unpublished test targets to generate binders with high experimentally determined affinities. Initializations from RFDiffusion2/3, BoltzDesign1 or BoltzGen will almost certainly work as well, but how best to leverage these tools for ligand binder design remains untested. Generating de novo fold topologies will likely decrease experimental success rates as well.

Running NISE:

1. Create a PDB file containing your PROTONATED input ligand with CONECT records encoding bonds (unless using NISE with LigandMPNN, then protonation is not necessary).: If you have a non-protonated ligand/are missing conect records, run protonate_and_add_conect_records.py {input_path}.pdb {smiles_string} {output_path}.pdb. WARNING: This will rename the ligand atoms, ligand chain, and resnum.

2. [Optional] If you want to protonate using reduce (keeps added ligand hydrogen names consistent with input, a bit more finicky than the alternative RDKit), Inject your ligand into REDUCE hetdict by running inject_ligand_into_hetdict.py {output_path}.pdb

3. Create an input directory with a subfolder called input_backbones. Ex: ./debug/input_backbones/.

4. Update the params dictionary at the bottom of ./run_nise_boltz1x.py with the path to your new input dir ex: (input_dir = Path('./debug/')).

5. Update burial and RMSD atom sets and smiles string in ./run_nise_boltz1x.py

6. Update boltz1x_executable_path at bottom of ./run_nise_boltz1x.py

7. If you want to constrain the number of alanine and glycine residues predicted on the surface of the protein in secondary-structured regions, run identify_surface_residues.ipynb and update 'budget_residue_sele_string' in the params dictionary at the bottom of the run_nise_boltz script.

To test out an example run:

# Use protonated smiles string from ChemDraw or OpenBabel prediction
./.venv/bin/python ./protonate_and_add_conect_records.py ./example_pdbs/02_apex_NISE_input-pose_00-seq_0980_model_0_rank_01.pdb "COC1=CC=C(C=C1)N2C3=C(CCN(C3=O)C4=CC=C(C=C4)N5CCCCC5=O)C(=N2)C(=O)N" ./example_pdbs/test_input_protonated_conect.pdb

mkdir -p ./debug/input_backbones/

cp ./example_pdbs/test_input_protonated_conect.pdb ./debug/input_backbones/

./run_nise_boltz2x.py

For a LigandMPNN example run:

conda activate lasermpnn

mkdir -p ./debug/input_backbones/
cp ./example_pdbs/02_apex_NISE_input-pose_00-seq_0980_model_0_rank_01.pdb ./debug/input_backbones/

./run_nise_boltz2x_ligandmpnn.py

For a complete example design campaign (generated a $K_d = 80 \pm 40$ picoMolar binder to apixaban), check out the contents of example_design_campaign