Check out the paper here!
Warning
A major difference between LASErMPNN and LigandMPNN is that LASErMPNN was trained on protonated structures.
Please make sure your ligand has the appropriate hydrogens (in the expected protonation state) attached when running the model or you may encounter unexpected behavior.
For an example of how to adjust protonation from a smiles string, check out the script ./protonate_and_add_conect_records.py from the NISE repo
- Add Colab notebook.
- Add pdb code dataset split text files to avoid digging into code.
- Add LigandMPNN architecture retraining code.
A minimal version of LASErMPNN could be run in inference mode in any Python environment with PyTorch, torch-scatter, and torch-cluster. ProDy is used internally to read and write PDB files.
To install the training environment, run the following set of commands using a MiniForge installation (recommended) or an existing conda installation with a libmamba solver.
conda env create -f conda_env.yml -yThis will create an environment called lasermpnn.
To ensure your conda installation is using the libmamba solver, run conda config --show-sources
and ensure the output has solver: libmamba at the bottom.
If not, run conda config --set-solver libmamba.
python -m LASErMPNN.run_inference -hThis script outputs a single pdb file named laser_output.pdb and is useful for testing:
usage: run_inference.py [-h] [--model_weights MODEL_WEIGHTS] [--output_path OUTPUT_PATH] [--temp SEQUENCE_TEMP] [--fs_sequence_temp FS_SEQUENCE_TEMP] [--bb_noise BACKBONE_NOISE] [--device DEVICE] [--fix_beta] [--ignore_statedict_mismatch] [--ebd] [--repack_only] [--ignore_ligand] [--noncanonical_aa_ligand]
[--fs_calc_ca_distance FS_CALC_CA_DISTANCE] [--fs_calc_burial_hull_alpha_value FS_CALC_BURIAL_HULL_ALPHA_VALUE] [--fs_no_calc_burial] [--disable_charged_fs]
input_pdb_code
Run LASErMPNN inference on a given PDB file.
positional arguments:
input_pdb_code Path to the input PDB file.
options:
-h, --help show this help message and exit
--model_weights MODEL_WEIGHTS, -w MODEL_WEIGHTS
Path to dictionary of torch.save()ed model state_dict and training parameters. Default: /nfs/polizzi/bfry/programs/LASErMPNN/model_weights/laser_weights_0p1A_noise_ligandmpnn_split.pt
--output_path OUTPUT_PATH, -o OUTPUT_PATH
Path to the output PDB file.
--temp SEQUENCE_TEMP, -t SEQUENCE_TEMP
Sequence sample temperature.
--fs_sequence_temp FS_SEQUENCE_TEMP, -f FS_SEQUENCE_TEMP
Residues around the ligand will be sampled at this temperature, otherwise they default to sequence_temp.
--bb_noise BACKBONE_NOISE, -n BACKBONE_NOISE
Inference backbone noise.
--device DEVICE, -d DEVICE
Pytorch style device string. Ex: "cuda:0" or "cpu".
--fix_beta, -b Residues with B-Factor of 1.0 have sequence and rotamer fixed, residues with B-Factor of 0.0 are designed.
--ignore_statedict_mismatch, -s
Small state_dict mismatches are ignored. Don't use this unless any missing parameters aren't learned during training.
--ebd, -e Uses entropy based decoding order. Decodes all residues and selects the lowest entropy residue as next to decode, then recomputes all remaining residues. Takes longer than normal decoding.
--repack_only Only repack residues, do not design new ones.
--ignore_ligand Ignore ligands in the input PDB file.
--noncanonical_aa_ligand
Featurize a noncanonical amino acid as a ligand.
--fs_calc_ca_distance FS_CALC_CA_DISTANCE
Distance between a ligand heavy atom and CA carbon to consider that carbon first shell.
--fs_calc_burial_hull_alpha_value FS_CALC_BURIAL_HULL_ALPHA_VALUE
Alpha parameter for defining convex hull. May want to try setting to larger values if using folds with larger cavities (ex: ~100.0).
--fs_no_calc_burial Disable using a burial calculation when selecting first shell residues, if true uses only distance from --fs_calc_ca_distance
--disable_charged_fs Disable sampling D,K,R,E residues in the first shell around the ligand.
python -m LASErMPNN.run_batch_inference -hThis script is useful to generate multiple designs for one or multiple inputs. Creates an output directory with subdirectories for each input file (unless run with a single input file).
usage: run_batch_inference.py [-h] [--designs_per_batch DESIGNS_PER_BATCH] [--inputs_processed_simultaneously INPUTS_PROCESSED_SIMULTANEOUSLY] [--model_weights_path MODEL_WEIGHTS_PATH] [--sequence_temp SEQUENCE_TEMP]
[--first_shell_sequence_temp FIRST_SHELL_SEQUENCE_TEMP] [--chi_temp CHI_TEMP] [--chi_min_p CHI_MIN_P] [--seq_min_p SEQ_MIN_P] [--device INFERENCE_DEVICE] [--use_water] [--silent] [--ignore_key_mismatch]
[--disabled_residues DISABLED_RESIDUES] [--fix_beta] [--repack_only_input_sequence] [--ignore_ligand] [--budget_residue_sele_string BUDGET_RESIDUE_SELE_STRING] [--ala_budget ALA_BUDGET] [--gly_budget GLY_BUDGET]
[--noncanonical_aa_ligand] [--fs_calc_ca_distance FS_CALC_CA_DISTANCE] [--fs_calc_burial_hull_alpha_value FS_CALC_BURIAL_HULL_ALPHA_VALUE] [--fs_no_calc_burial] [--disable_charged_fs]
input_pdb_directory output_pdb_directory designs_per_input
Run batch LASErMPNN inference.
positional arguments:
input_pdb_directory Path to directory of input .pdb or .pdb.gz files, a single input .pdb or .pdb.gz file, or a .txt file of paths to input .pdb or .pdb.gz files.
output_pdb_directory Path to directory to output LASErMPNN designs.
designs_per_input Number of designs to generate per input.
options:
-h, --help show this help message and exit
--designs_per_batch DESIGNS_PER_BATCH, -b DESIGNS_PER_BATCH
Number of designs to generate per batch. If designs_per_input > designs_per_batch, chunks up the inference calls in batches of this size. Default is 30, can increase/decrease depending on available GPU memory.
--inputs_processed_simultaneously INPUTS_PROCESSED_SIMULTANEOUSLY, -n INPUTS_PROCESSED_SIMULTANEOUSLY
When passed a list of multiple files, this is the number of input files to process per pass through the GPU. Useful when generating a few sequences for many input files.
--model_weights_path MODEL_WEIGHTS_PATH, -w MODEL_WEIGHTS_PATH
Path to model weights. Default: /nfs/polizzi/bfry/programs/LASErMPNN/model_weights/laser_weights_0p1A_noise_ligandmpnn_split.pt
--sequence_temp SEQUENCE_TEMP
Temperature for sequence sampling.
--first_shell_sequence_temp FIRST_SHELL_SEQUENCE_TEMP
Temperature for first shell sequence sampling. Can be used to disentangle binding site temperature from global sequence temperature for harder folds.
--chi_temp CHI_TEMP Temperature for chi sampling.
--chi_min_p CHI_MIN_P
Minimum probability for chi sampling. Not recommended.
--seq_min_p SEQ_MIN_P
Minimum probability for sequence sampling. Not recommended.
--device INFERENCE_DEVICE, -d INFERENCE_DEVICE
PyTorch style device string (e.g. "cuda:0").
--use_water Parses water (resname HOH) as part of a ligand.
--silent Silences all output except pbar.
--ignore_key_mismatch
Allows mismatched keys in checkpoint statedict
--disabled_residues DISABLED_RESIDUES
Residues to disable in sampling.
--fix_beta If B-factors are set to 1, fixes the residue and rotamer, if not, designs that position.
--repack_only_input_sequence
Repacks the input sequence without changing the sequence.
--ignore_ligand Ignore ligand in sampling.
--budget_residue_sele_string BUDGET_RESIDUE_SELE_STRING
--ala_budget ALA_BUDGET
--gly_budget GLY_BUDGET
--noncanonical_aa_ligand
Featurize a noncanonical amino acid as a ligand.
--fs_calc_ca_distance FS_CALC_CA_DISTANCE
Distance between a ligand heavy atom and CA carbon to consider that carbon first shell.
--fs_calc_burial_hull_alpha_value FS_CALC_BURIAL_HULL_ALPHA_VALUE
Alpha parameter for defining convex hull. May want to try setting to larger values if using folds with larger cavities (ex: ~100.0).
--fs_no_calc_burial Disable using a burial calculation when selecting first shell residues, if true uses only distance from --fs_calc_ca_distance
--disable_charged_fs Disable sampling D,K,R,E residues in the first shell around the ligand.
To retrain the model, download the datasets with download_ligand_encoder_training_dataset.sh and download_protonated_pdb_training_dataset.sh for each respective dataset by running them in the project's root directory.
We used 4x A6000 GPUs to train the LASErMPNN model which takes around 24 hrs for 60k optimizer steps. See train_lasermpnn.py for more information.
Training the Ligand Encoder module can be done with much lower memory and a single GPU. See pretrain_ligand_encoder.py for more information.
see https://www.github.com/polizzilab/NISE for a NISE protocol implementation using Boltz-1.