ligandmpnn 0.1.2

a pip installable version of LigandMPNN with pre-trained models included

LigandMPNN

This package provides inference code for LigandMPNN & ProteinMPNN models. The code and model parameters are available under the MIT license.

Third party code: side chain packing uses helper functions from Openfold.

Installation with pip (PyPi distribution, pretrained models included)

# set up your conda environment
conda create -n ligandmpnn_env python=3.11
conda activate ligandmpnn_env
pip install ligandmpnn

# example usage
ligandmpnn --seed 111 --pdb_path inputs/1BC8.pdb --out_folder outputs/default

Installation with pip (building from GitHub source)

# set up your conda environment
conda create -n ligandmpnn_env python=3.11
conda activate ligandmpnn_env

# download source
git clone https://github.com/dauparas/LigandMPNN
cd LigandMPNN

# local install from source directory
pip install .

# run examples from the scripts directory
# information for each example is provided in the documentation below
cd scripts
bash run_examples.sh
bash sc_examples.sh

Running the code from source

# download source
git clone https://github.com/dauparas/LigandMPNN.git
cd LigandMPNN

# set up your conda/or other environment
conda create -n ligandmpnn_env python=3.11
conda activate ligandmpnn_env
pip3 install -r requirements.txt

# example usage
python ligandmpnn/run.py \
        --seed 111 \
        --pdb_path "inputs/1BC8.pdb" \
        --out_folder "outputs/default"

Dependencies

To run the model you will need to have Python=3.11, PyTorch=2.2.1, and NumPy=1.X installed.

You will also need Prody, which has been known to cause issues during build when installing via pip, depending on your system architecture. See the ProDy documentation for help with installation.

Main differences compared with ProteinMPNN code

• Input PDBs are parsed using Prody preserving protein residue indices, chain letters, and insertion codes. If there are missing residues in the input structure the output fasta file won't have added X to fill the gaps. The script outputs .fasta and .pdb files. It's recommended to use .pdb files since they will hold information about chain letters and residue indices.
• Adding bias, fixing residues, and selecting residues to be redesigned now can be done using residue indices directly, e.g. A23 (means chain A residue with index 23), B42D (chain B, residue 42, insertion code D).
• Model writes to fasta files: overall_confidence, ligand_confidence which reflect the average confidence/probability (with T=1.0) over the redesigned residues overall_confidence=exp[-mean_over_residues(log_probs)]. Higher numbers mean the model is more confident about that sequence. min_value=0.0; max_value=1.0. Sequence recovery with respect to the input sequence is calculated only over the redesigned residues.

Model parameters

Model parameters are included by default in the PyPi pip installation To manually download model parameters run:

bash scripts/get_model_params.sh "liganmpnn/data/model_params"

Available models

To run the model of your choice specify --model_type and optionally the model checkpoint path. Available models:

• ProteinMPNN

--model_type "protein_mpnn"
--checkpoint_protein_mpnn "proteinmpnn_v_48_002.pt" #noised with 0.02A Gaussian noise
--checkpoint_protein_mpnn "proteinmpnn_v_48_010.pt" #noised with 0.10A Gaussian noise
--checkpoint_protein_mpnn "proteinmpnn_v_48_020.pt" #noised with 0.20A Gaussian noise
--checkpoint_protein_mpnn "proteinmpnn_v_48_030.pt" #noised with 0.30A Gaussian noise

• LigandMPNN

--model_type "ligand_mpnn"
--checkpoint_ligand_mpnn "ligandmpnn_v_32_005_25.pt" #noised with 0.05A Gaussian noise
--checkpoint_ligand_mpnn "ligandmpnn_v_32_010_25.pt" #noised with 0.10A Gaussian noise
--checkpoint_ligand_mpnn "ligandmpnn_v_32_020_25.pt" #noised with 0.20A Gaussian noise
--checkpoint_ligand_mpnn "ligandmpnn_v_32_030_25.pt" #noised with 0.30A Gaussian noise

• SolubleMPNN

--model_type "soluble_mpnn"
--checkpoint_soluble_mpnn "solublempnn_v_48_002.pt" #noised with 0.02A Gaussian noise
--checkpoint_soluble_mpnn "solublempnn_v_48_010.pt" #noised with 0.10A Gaussian noise
--checkpoint_soluble_mpnn "solublempnn_v_48_020.pt" #noised with 0.20A Gaussian noise
--checkpoint_soluble_mpnn "solublempnn_v_48_030.pt" #noised with 0.30A Gaussian noise

• ProteinMPNN with global membrane label

--model_type "global_label_membrane_mpnn"
--checkpoint_global_label_membrane_mpnn "global_label_membrane_mpnn_v_48_020.pt" #noised with 0.20A Gaussian noise

• ProteinMPNN with per residue membrane label

--model_type "per_residue_label_membrane_mpnn"
--checkpoint_per_residue_label_membrane_mpnn "per_residue_label_membrane_mpnn_v_48_020.pt" #noised with 0.20A Gaussian noise

• Side chain packing model

--checkpoint_path_sc "ligandmpnn_sc_v_32_002_16.pt"

Design examples

1 default

Default settings will run ProteinMPNN.

ligandmpnn \
        --seed 111 \
        --pdb_path "inputs/1BC8.pdb" \
        --out_folder "outputs/default"

2 --temperature

--temperature 0.05 Change sampling temperature (higher temperature gives more sequence diversity).

ligandmpnn \
        --seed 111 \
        --pdb_path "inputs/1BC8.pdb" \
        --temperature 0.05 \
        --out_folder "outputs/temperature"

3 --seed

--seed Not selecting a seed will run with a random seed. Running this multiple times will give different results.

ligandmpnn \
        --pdb_path "inputs/1BC8.pdb" \
        --out_folder "outputs/random_seed"

4 --verbose

--verbose 0 Do not print any statements.

ligandmpnn \
        --seed 111 \
        --verbose 0 \
        --pdb_path "inputs/1BC8.pdb" \
        --out_folder "outputs/verbose"

5 --save_stats

--save_stats 1 Save sequence design statistics.

#['generated_sequences', 'sampling_probs', 'log_probs', 'decoding_order', 'native_sequence', 'mask', 'chain_mask', 'seed', 'temperature']
ligandmpnn \
        --seed 111 \
        --pdb_path "inputs/1BC8.pdb" \
        --out_folder "outputs/save_stats" \
        --save_stats 1

6 --fixed_residues

--fixed_residues Fixing specific amino acids. This example fixes the first 10 residues in chain C and adds global bias towards A (alanine). The output should have all alanines except the first 10 residues should be the same as in the input sequence since those are fixed.

ligandmpnn \
        --seed 111 \
        --pdb_path "inputs/1BC8.pdb" \
        --out_folder "outputs/fix_residues" \
        --fixed_residues "C1 C2 C3 C4 C5 C6 C7 C8 C9 C10" \
        --bias_AA "A:10.0"

7 --redesigned_residues

--redesigned_residues Specifying which residues need to be designed. This example redesigns the first 10 residues while fixing everything else.

ligandmpnn \
        --seed 111 \
        --pdb_path "inputs/1BC8.pdb" \
        --out_folder "outputs/redesign_residues" \
        --redesigned_residues "C1 C2 C3 C4 C5 C6 C7 C8 C9 C10" \
        --bias_AA "A:10.0"

8 --number_of_batches

Design 15 sequences; with batch size 3 (can be 1 when using CPUs) and the number of batches 5.

ligandmpnn \
        --seed 111 \
        --pdb_path "inputs/1BC8.pdb" \
        --out_folder "outputs/batch_size" \
        --batch_size 3 \
        --number_of_batches 5

9 --bias_AA

Global amino acid bias. In this example, output sequences are biased towards W, P, C and away from A.

ligandmpnn \
        --seed 111 \
        --pdb_path "inputs/1BC8.pdb" \
        --bias_AA "W:3.0,P:3.0,C:3.0,A:-3.0" \
        --out_folder "outputs/global_bias"

10 --bias_AA_per_residue

Specify per residue amino acid bias, e.g. make residues C1, C3, C5, and C7 to be prolines.

# {
# "C1": {"G": -0.3, "C": -2.0, "P": 10.8},
# "C3": {"P": 10.0},
# "C5": {"G": -1.3, "P": 10.0},
# "C7": {"G": -1.3, "P": 10.0}
# }
ligandmpnn \
        --seed 111 \
        --pdb_path "inputs/1BC8.pdb" \
        --bias_AA_per_residue "inputs/bias_AA_per_residue.json" \
        --out_folder "outputs/per_residue_bias"

11 --omit_AA

Global amino acid restrictions. This is equivalent to using --bias_AA and setting bias to be a large negative number. The output should be just made of E, K, A.

ligandmpnn \
        --seed 111 \
        --pdb_path "inputs/1BC8.pdb" \
        --omit_AA "CDFGHILMNPQRSTVWY" \
        --out_folder "outputs/global_omit"

12 --omit_AA_per_residue

Per residue amino acid restrictions.

# {
# "C1": "ACDEFGHIKLMNPQRSTVW",
# "C3": "ACDEFGHIKLMNPQRSTVW",
# "C5": "ACDEFGHIKLMNPQRSTVW",
# "C7": "ACDEFGHIKLMNPQRSTVW"
# }
ligandmpnn \
        --seed 111 \
        --pdb_path "inputs/1BC8.pdb" \
        --omit_AA_per_residue "inputs/omit_AA_per_residue.json" \
        --out_folder "outputs/per_residue_omit"

13 --symmetry_residues

13 --symmetry_weights

Designing sequences with symmetry, e.g. homooligomer/2-state proteins, etc. In this example make C1=C2=C3, also C4=C5, and C6=C7.

#total_logits += symmetry_weights[t]*logits
#probs = torch.nn.functional.softmax((total_logits+bias_t) / temperature, dim=-1)
#total_logits_123 = 0.33*logits_1+0.33*logits_2+0.33*logits_3
#output should be ***ooxx
ligandmpnn \
        --seed 111 \
        --pdb_path "inputs/1BC8.pdb" \
        --out_folder "outputs/symmetry" \
        --symmetry_residues "C1,C2,C3|C4,C5|C6,C7" \
        --symmetry_weights "0.33,0.33,0.33|0.5,0.5|0.5,0.5"

14 --homo_oligomer

Design homooligomer sequences. This automatically sets --symmetry_residues and --symmetry_weights assuming equal weighting from all chains.

ligandmpnn \
        --model_type "ligand_mpnn" \
        --seed 111 \
        --pdb_path "inputs/4GYT.pdb" \
        --out_folder "outputs/homooligomer" \
        --homo_oligomer 1 \
        --number_of_batches 2

15 --file_ending

Outputs will have a specified ending; e.g. 1BC8_xyz.fa instead of 1BC8.fa

ligandmpnn \
        --seed 111 \
        --pdb_path "inputs/1BC8.pdb" \
        --out_folder "outputs/file_ending" \
        --file_ending "_xyz"

16 --zero_indexed

Zero indexed names in /backbones/1BC8_0.pdb, 1BC8_1.pdb, 1BC8_2.pdb etc

ligandmpnn \
        --seed 111 \
        --pdb_path "inputs/1BC8.pdb" \
        --out_folder "outputs/zero_indexed" \
        --zero_indexed 1 \
        --number_of_batches 2

17 --chains_to_design

Specify which chains (e.g. "A,B,C") need to be redesigned, other chains will be kept fixed. Outputs in seqs/backbones will still have atoms/sequences for the whole input PDB.

ligandmpnn \
        --model_type "ligand_mpnn" \
        --seed 111 \
        --pdb_path "inputs/4GYT.pdb" \
        --out_folder "outputs/chains_to_design" \
        --chains_to_design "A,B"

18 --parse_these_chains_only

Parse and design only specified chains (e.g. "A,B,C"). Outputs will have only specified chains.

ligandmpnn \
        --model_type "ligand_mpnn" \
        --seed 111 \
        --pdb_path "inputs/4GYT.pdb" \
        --out_folder "outputs/parse_these_chains_only" \
        --parse_these_chains_only "A,B"

19 --model_type "ligand_mpnn"

Run LigandMPNN with default settings.

ligandmpnn \
        --model_type "ligand_mpnn" \
        --seed 111 \
        --pdb_path "inputs/1BC8.pdb" \
        --out_folder "outputs/ligandmpnn_default"

20 --checkpoint_ligand_mpnn

Run LigandMPNN using 0.05A model by specifying --checkpoint_ligand_mpnn flag.

ligandmpnn \
        --checkpoint_ligand_mpnn "ligandmpnn/data/model_params/ligandmpnn_v_32_005_25.pt" \
        --model_type "ligand_mpnn" \
        --seed 111 \
        --pdb_path "inputs/1BC8.pdb" \
        --out_folder "outputs/ligandmpnn_v_32_005_25"

21 --ligand_mpnn_use_atom_context

Setting --ligand_mpnn_use_atom_context 0 will mask all ligand atoms. This can be used to assess how much ligand atoms affect AA probabilities.

ligandmpnn \
        --model_type "ligand_mpnn" \
        --seed 111 \
        --pdb_path "inputs/1BC8.pdb" \
        --out_folder "outputs/ligandmpnn_no_context" \
        --ligand_mpnn_use_atom_context 0

22 --ligand_mpnn_use_side_chain_context

Use fixed residue side chain atoms as extra ligand atoms.

ligandmpnn \
        --model_type "ligand_mpnn" \
        --seed 111 \
        --pdb_path "inputs/1BC8.pdb" \
        --out_folder "outputs/ligandmpnn_use_side_chain_atoms" \
        --ligand_mpnn_use_side_chain_context 1 \
        --fixed_residues "C1 C2 C3 C4 C5 C6 C7 C8 C9 C10"

23 --model_type "soluble_mpnn"

Run SolubleMPNN (ProteinMPNN-like model with only soluble proteins in the training dataset).

ligandmpnn \
        --model_type "soluble_mpnn" \
        --seed 111 \
        --pdb_path "inputs/1BC8.pdb" \
        --out_folder "outputs/soluble_mpnn_default"

24 --model_type "global_label_membrane_mpnn"

Run global label membrane MPNN (trained with extra input - binary label soluble vs not) --global_transmembrane_label #1 - membrane, 0 - soluble.

ligandmpnn \
        --model_type "global_label_membrane_mpnn" \
        --seed 111 \
        --pdb_path "inputs/1BC8.pdb" \
        --out_folder "outputs/global_label_membrane_mpnn_0" \
        --global_transmembrane_label 0

25 --model_type "per_residue_label_membrane_mpnn"

Run per residue label membrane MPNN (trained with extra input per residue specifying buried (hydrophobic), interface (polar), or other type residues; 3 classes).

ligandmpnn \
        --model_type "per_residue_label_membrane_mpnn" \
        --seed 111 \
        --pdb_path "inputs/1BC8.pdb" \
        --out_folder "outputs/per_residue_label_membrane_mpnn_default" \
        --transmembrane_buried "C1 C2 C3 C11" \
        --transmembrane_interface "C4 C5 C6 C22"

26 --fasta_seq_separation

Choose a symbol to put between different chains in fasta output format. It's recommended to PDB output format to deal with residue jumps and multiple chain parsing.

ligandmpnn \
        --pdb_path "inputs/1BC8.pdb" \
        --out_folder "outputs/fasta_seq_separation" \
        --fasta_seq_separation ":"

27 --pdb_path_multi

Specify multiple PDB input paths. This is more efficient since the model needs to be loaded from the checkpoint once.

#{
#"inputs/1BC8.pdb": "",
#"inputs/4GYT.pdb": ""
#}
ligandmpnn \
        --pdb_path_multi "inputs/pdb_ids.json" \
        --out_folder "outputs/pdb_path_multi" \
        --seed 111

28 --fixed_residues_multi

Specify fixed residues when using --pdb_path_multi flag.

#{
#"inputs/1BC8.pdb": "C1 C2 C3 C4 C5 C10 C22",
#"inputs/4GYT.pdb": "A7 A8 A9 A10 A11 A12 A13 B38"
#}
ligandmpnn \
        --pdb_path_multi "inputs/pdb_ids.json" \
        --fixed_residues_multi "inputs/fix_residues_multi.json" \
        --out_folder "outputs/fixed_residues_multi" \
        --seed 111

29 --redesigned_residues_multi

Specify which residues need to be redesigned when using --pdb_path_multi flag.

#{
#"inputs/1BC8.pdb": "C1 C2 C3 C4 C5 C10",
#"inputs/4GYT.pdb": "A7 A8 A9 A10 A12 A13 B38"
#}
ligandmpnn \
        --pdb_path_multi "inputs/pdb_ids.json" \
        --redesigned_residues_multi "inputs/redesigned_residues_multi.json" \
        --out_folder "outputs/redesigned_residues_multi" \
        --seed 111

30 --omit_AA_per_residue_multi

Specify which residues need to be omitted when using --pdb_path_multi flag.

#{
#"inputs/1BC8.pdb": {"C1":"ACDEFGHILMNPQRSTVWY", "C2":"ACDEFGHILMNPQRSTVWY", "C3":"ACDEFGHILMNPQRSTVWY"},
#"inputs/4GYT.pdb": {"A7":"ACDEFGHILMNPQRSTVWY", "A8":"ACDEFGHILMNPQRSTVWY"}
#}
ligandmpnn \
        --pdb_path_multi "inputs/pdb_ids.json" \
        --omit_AA_per_residue_multi "inputs/omit_AA_per_residue_multi.json" \
        --out_folder "outputs/omit_AA_per_residue_multi" \
        --seed 111

31 --bias_AA_per_residue_multi

Specify amino acid biases per residue when using --pdb_path_multi flag.

#{
#"inputs/1BC8.pdb": {"C1":{"A":3.0, "P":-2.0}, "C2":{"W":10.0, "G":-0.43}},
#"inputs/4GYT.pdb": {"A7":{"Y":5.0, "S":-2.0}, "A8":{"M":3.9, "G":-0.43}}
#}
ligandmpnn \
        --pdb_path_multi "inputs/pdb_ids.json" \
        --bias_AA_per_residue_multi "inputs/bias_AA_per_residue_multi.json" \
        --out_folder "outputs/bias_AA_per_residue_multi" \
        --seed 111

32 --ligand_mpnn_cutoff_for_score

This sets the cutoff distance in angstroms to select residues that are considered to be close to ligand atoms. This flag only affects the num_ligand_res and ligand_confidence in the output fasta files.

ligandmpnn \
        --model_type "ligand_mpnn" \
        --seed 111 \
        --pdb_path "inputs/1BC8.pdb" \
        --ligand_mpnn_cutoff_for_score "6.0" \
        --out_folder "outputs/ligand_mpnn_cutoff_for_score"

33 specifying residues with insertion codes

You can specify residue using chain_id + residue_number + insersion_code; e.g. redesign only residue B82, B82A, B82B, B82C.

ligandmpnn \
        --seed 111 \
        --pdb_path "inputs/2GFB.pdb" \
        --out_folder "outputs/insertion_code" \
        --redesigned_residues "B82 B82A B82B B82C" \
        --parse_these_chains_only "B"

34 parse atoms with zero occupancy

Parse atoms in the PDB files with zero occupancy too.

ligandmpnn \
        --model_type "ligand_mpnn" \
        --seed 111 \
        --pdb_path "inputs/1BC8.pdb" \
        --out_folder "outputs/parse_atoms_with_zero_occupancy" \
        --parse_atoms_with_zero_occupancy 1

Scoring examples

Output dictionary

out_dict = {}
out_dict["logits"] - raw logits from the model
out_dict["probs"] - softmax(logits)
out_dict["log_probs"] - log_softmax(logits)
out_dict["decoding_order"] - decoding order used (logits will depend on the decoding order)
out_dict["native_sequence"] - parsed input sequence in integers
out_dict["mask"] - mask for missing residues (usually all ones)
out_dict["chain_mask"] - controls which residues are decoded first
out_dict["alphabet"] - amino acid alphabet used
out_dict["residue_names"] - dictionary to map integers to residue_names, e.g. {0: "C10", 1: "C11"}
out_dict["sequence"] - parsed input sequence in alphabet
out_dict["mean_of_probs"] - averaged over batch_size*number_of_batches probabilities, [protein_length, 21]
out_dict["std_of_probs"] - same as above, but std

1 autoregressive with sequence info

Get probabilities/scores for backbone-sequence pairs using autoregressive probabilities: p(AA_1|backbone), p(AA_2|backbone, AA_1) etc. These probabilities will depend on the decoding order, so it's recomended to set number_of_batches to at least 10.

python score.py \
        --model_type "ligand_mpnn" \
        --seed 111 \
        --autoregressive_score 1\
        --pdb_path "outputs/ligandmpnn_default/backbones/1BC8_1.pdb" \
        --out_folder "outputs/autoregressive_score_w_seq" \
        --use_sequence 1\
        --batch_size 1 \
        --number_of_batches 10

2 autoregressive with backbone info only

Get probabilities/scores for backbone using probabilities: p(AA_1|backbone), p(AA_2|backbone) etc. These probabilities will depend on the decoding order, so it's recomended to set number_of_batches to at least 10.

python score.py \
        --model_type "ligand_mpnn" \
        --seed 111 \
        --autoregressive_score 1\
        --pdb_path "outputs/ligandmpnn_default/backbones/1BC8_1.pdb" \
        --out_folder "outputs/autoregressive_score_wo_seq" \
        --use_sequence 0\
        --batch_size 1 \
        --number_of_batches 10

3 single amino acid score with sequence info

Get probabilities/scores for backbone-sequence pairs using single aa probabilities: p(AA_1|backbone, AA_{all except AA_1}), p(AA_2|backbone, AA_{all except AA_2}) etc. These probabilities will depend on the decoding order, so it's recomended to set number_of_batches to at least 10.

python score.py \
        --model_type "ligand_mpnn" \
        --seed 111 \
        --single_aa_score 1\
        --pdb_path "outputs/ligandmpnn_default/backbones/1BC8_1.pdb" \
        --out_folder "outputs/single_aa_score_w_seq" \
        --use_sequence 1\
        --batch_size 1 \
        --number_of_batches 10

4 single amino acid score with backbone info only

Get probabilities/scores for backbone-sequence pairs using single aa probabilities: p(AA_1|backbone), p(AA_2|backbone) etc. These probabilities will depend on the decoding order, so it's recomended to set number_of_batches to at least 10.

python score.py \
        --model_type "ligand_mpnn" \
        --seed 111 \
        --single_aa_score 1\
        --pdb_path "outputs/ligandmpnn_default/backbones/1BC8_1.pdb" \
        --out_folder "outputs/single_aa_score_wo_seq" \
        --use_sequence 0\
        --batch_size 1 \
        --number_of_batches 10

Side chain packing examples

1 design a new sequence and pack side chains (return 1 side chain packing sample - fast)

Design a new sequence using any of the available models and also pack side chains of the new sequence. Return only a single solution for the side chain packing.

ligandmpnn \
        --model_type "ligand_mpnn" \
        --seed 111 \
        --pdb_path "inputs/1BC8.pdb" \
        --out_folder "outputs/sc_default_fast" \
        --pack_side_chains 1 \
        --number_of_packs_per_design 0 \
        --pack_with_ligand_context 1

2 design a new sequence and pack side chains (return 4 side chain packing samples)

Same as above, but returns 4 independent samples for side chains. b-factor shows log prob density per chi angle group.

ligandmpnn \
        --model_type "ligand_mpnn" \
        --seed 111 \
        --pdb_path "inputs/1BC8.pdb" \
        --out_folder "outputs/sc_default" \
        --pack_side_chains 1 \
        --number_of_packs_per_design 4 \
        --pack_with_ligand_context 1

3 fix specific residues fors sequence design and packing

This option will not repack side chains of the fixed residues, but use them as a context.

ligandmpnn \
        --model_type "ligand_mpnn" \
        --seed 111 \
        --pdb_path "inputs/1BC8.pdb" \
        --out_folder "outputs/sc_fixed_residues" \
        --pack_side_chains 1 \
        --number_of_packs_per_design 4 \
        --pack_with_ligand_context 1 \
        --fixed_residues "C6 C7 C8 C9 C10 C11 C12 C13 C14 C15" \
        --repack_everything 0

4 fix specific residues for sequence design but repack everything

This option will repacks all the residues.

ligandmpnn \
        --model_type "ligand_mpnn" \
        --seed 111 \
        --pdb_path "inputs/1BC8.pdb" \
        --out_folder "outputs/sc_fixed_residues_full_repack" \
        --pack_side_chains 1 \
        --number_of_packs_per_design 4 \
        --pack_with_ligand_context 1 \
        --fixed_residues "C6 C7 C8 C9 C10 C11 C12 C13 C14 C15" \
        --repack_everything 1

5 design a new sequence using LigandMPNN but pack side chains without considering ligand/DNA etc atoms

You can run side chain packing without taking into account context atoms like DNA atoms. This most likely will results in side chain clashing with context atoms, but it might be interesting to see how model's uncertainty changes when ligand atoms are present vs not for side chain conformations.

ligandmpnn \
        --model_type "ligand_mpnn" \
        --seed 111 \
        --pdb_path "inputs/1BC8.pdb" \
        --out_folder "outputs/sc_no_context" \
        --pack_side_chains 1 \
        --number_of_packs_per_design 4 \
        --pack_with_ligand_context 0

Things to add

• Support for ProteinMPNN CA-only model.
• Examples for scoring sequences only.
• Side-chain packing scripts.
• TER

Citing this work

If you use the code, please cite:

@article{dauparas2023atomic,
  title={Atomic context-conditioned protein sequence design using LigandMPNN},
  author={Dauparas, Justas and Lee, Gyu Rie and Pecoraro, Robert and An, Linna and Anishchenko, Ivan and Glasscock, Cameron and Baker, David},
  journal={Biorxiv},
  pages={2023--12},
  year={2023},
  publisher={Cold Spring Harbor Laboratory}
}

@article{dauparas2022robust,
  title={Robust deep learning--based protein sequence design using ProteinMPNN},
  author={Dauparas, Justas and Anishchenko, Ivan and Bennett, Nathaniel and Bai, Hua and Ragotte, Robert J and Milles, Lukas F and Wicky, Basile IM and Courbet, Alexis and de Haas, Rob J and Bethel, Neville and others},
  journal={Science},
  volume={378},
  number={6615},  
  pages={49--56},
  year={2022},
  publisher={American Association for the Advancement of Science}
}