Read ProteinMPNN paper.
To run ProteinMPNN clone this github repo and install Python>=3.0, PyTorch, Numpy.
Full protein backbone models: vanilla_proteinmpnn.
CA only models: ca_proteinmpnn.
Code organization:
vanilla_proteinmpnn/protein_mpnn_run.py- the main script to initialialize and run the model.vanilla_proteinmpnn/protein_mpnn_utils.py- utility functions for the main script.vanilla_proteinmpnn/helper_scripts/- helper functions to parse PDBs, assign which chains to design, which residues to fix, adding AA bias, tying residues etc.vanilla_proteinmpnn/examples/- simple code examples.
Input flags:
argparser.add_argument("--path_to_model_weights", type=str, default="", help="Path to model weights folder;")
argparser.add_argument("--model_name", type=str, default="v_48_020", help="ProteinMPNN model name: v_48_002, v_48_010, v_48_020, v_48_030; v_48_010=version with 48 edges 0.10A noise")
argparser.add_argument("--save_score", type=int, default=0, help="0 for False, 1 for True; save score=-mean[log_probs] to npy files")
argparser.add_argument("--save_probs", type=int, default=0, help="0 for False, 1 for True; save MPNN predicted probabilites per position")
argparser.add_argument("--score_only", type=int, default=0, help="0 for False, 1 for True; score input backbone-sequence pairs")
argparser.add_argument("--conditional_probs_only", type=int, default=0, help="0 for False, 1 for True; output conditional probabilities p(s_i given the rest of the sequence and backbone)")
argparser.add_argument("--conditional_probs_only_backbone", type=int, default=0, help="0 for False, 1 for True; if true output conditional probabilities p(s_i given backbone)")
argparser.add_argument("--unconditional_probs_only", type=int, default=0, help="0 for False, 1 for True; output unconditional probabilities p(s_i given backbone) in one forward pass")
argparser.add_argument("--backbone_noise", type=float, default=0.00, help="Standard deviation of Gaussian noise to add to backbone atoms during the inference.")
argparser.add_argument("--num_seq_per_target", type=int, default=1, help="Number of sequences to generate per target.")
argparser.add_argument("--batch_size", type=int, default=1, help="Batch size when using GPUs.")
argparser.add_argument("--max_length", type=int, default=20000, help="Maximum sequence length.")
argparser.add_argument("--sampling_temp", type=str, default="0.1", help="A string of temperatures, 0.1 0.3 0.5. Sampling temperature for amino acids, T=0.0 means taking argmax, T>>1.0 means sampling randomly.")
argparser.add_argument("--out_folder", type=str, help="Path to a folder to output sequences, e.g. /home/out/")
argparser.add_argument("--pdb_path", type=str, default='', help="Path to a single PDB to be designed.")
argparser.add_argument("--pdb_path_chains", type=str, default='', help="Define which chains need to be designed for a single PDB.")
argparser.add_argument("--jsonl_path", type=str, help="Path to a folder with parsed PDBs into jsonl.")
argparser.add_argument("--chain_id_jsonl",type=str, default='', help="Path to a dictionary specifying which chains need to be designed and which ones are fixed, if not specied all chains will be designed.")
argparser.add_argument("--fixed_positions_jsonl", type=str, default='', help="Path to a dictionary with fixed positions.")
argparser.add_argument("--omit_AAs", type=list, default='X', help="Specify which amino acids should be omitted in the generated sequence, e.g. 'AC' would omit alanine and cystine.")
argparser.add_argument("--bias_AA_jsonl", type=str, default='', help="Path to a dictionary which specifies AA composion bias, e.g. {A: -1.1, F: 0.7} would make A less likely and F more likely.")
argparser.add_argument("--bias_by_res_jsonl", default='', help="Path to dictionary with per position bias.")
argparser.add_argument("--omit_AA_jsonl", type=str, default='', help="Path to a dictionary which specifies which amino acids need to be omited from design at specific chain indices.")
argparser.add_argument("--pssm_jsonl", type=str, default='', help="Path to a dictionary with pssm.")
argparser.add_argument("--pssm_multi", type=float, default=0.0, help="A value between [0.0, 1.0], 0.0 means do not use pssm, 1.0 ignore MPNN predictions.")
argparser.add_argument("--pssm_threshold", type=float, default=0.0, help="A value between -inf + inf to restric per position AAs.")
argparser.add_argument("--pssm_log_odds_flag", type=int, default=0, help="0 for False, 1 for True.")
argparser.add_argument("--pssm_bias_flag", type=int, default=0, help="0 for False, 1 for True.")
argparser.add_argument("--tied_positions_jsonl", type=str, default='', help="Path to a dictionary with tied positions for symmetric design.")
Example from vanilla_proteinmpnn/examples/ to design a single PDB file:
path_to_PDB="../PDB_complexes/pdbs/3HTN.pdb"
output_dir="../PDB_complexes/example_3_outputs"
if [ ! -d $output_dir ]
then
mkdir -p $output_dir
fi
chains_to_design="A B" #design only chains A and B while using the context of other chains
python ../protein_mpnn_run.py \
--pdb_path $path_to_PDB \
--pdb_path_chains "$chains_to_design" \
--out_folder $output_dir \
--num_seq_per_target 2 \
--sampling_temp "0.1" \
--batch_size 1
Example from vanilla_proteinmpnn/examples/ to design some monomers:
folder_with_pdbs="../PDB_monomers/pdbs/"
output_dir="../PDB_monomers/example_1_outputs"
if [ ! -d $output_dir ]
then
mkdir -p $output_dir
fi
path_for_parsed_chains=$output_dir"/parsed_pdbs.jsonl"
python ../helper_scripts/parse_multiple_chains.py --input_path=$folder_with_pdbs --output_path=$path_for_parsed_chains
python ../protein_mpnn_run.py \
--jsonl_path $path_for_parsed_chains \
--out_folder $output_dir \
--num_seq_per_target 2 \
--sampling_temp "0.1" \
--batch_size 1
Example from vanilla_proteinmpnn/examples/ to design some homomers:
folder_with_pdbs="../PDB_homooligomers/pdbs/"
output_dir="../PDB_homooligomers/example_6_outputs"
if [ ! -d $output_dir ]
then
mkdir -p $output_dir
fi
path_for_parsed_chains=$output_dir"/parsed_pdbs.jsonl"
path_for_tied_positions=$output_dir"/tied_pdbs.jsonl"
python ../helper_scripts/parse_multiple_chains.py --input_path=$folder_with_pdbs --output_path=$path_for_parsed_chains
python ../helper_scripts/make_tied_positions_dict.py --input_path=$path_for_parsed_chains --output_path=$path_for_tied_positions --homooligomer 1
python ../protein_mpnn_run.py \
--jsonl_path $path_for_parsed_chains \
--tied_positions_jsonl $path_for_tied_positions \
--out_folder $output_dir \
--num_seq_per_target 2 \
--sampling_temp "0.2" \
--batch_size 1
Example from vanilla_proteinmpnn/examples/ to design some complexes:
folder_with_pdbs="../PDB_complexes/pdbs/"
output_dir="../PDB_complexes/example_4_outputs"
if [ ! -d $output_dir ]
then
mkdir -p $output_dir
fi
path_for_parsed_chains=$output_dir"/parsed_pdbs.jsonl"
path_for_assigned_chains=$output_dir"/assigned_pdbs.jsonl"
path_for_fixed_positions=$output_dir"/fixed_pdbs.jsonl"
chains_to_design="A C"
#The first amino acid in the chain corresponds to 1 and not PDB residues index for now.
fixed_positions="1 2 3 4 5 6 7 8 23 25, 10 11 12 13 14 15 16 17 18 19 20 40" #fixing/not designing residues 1 2 3...25 in chain A and residues 10 11 12...40 in chain C
python ../helper_scripts/parse_multiple_chains.py --input_path=$folder_with_pdbs --output_path=$path_for_parsed_chains
python ../helper_scripts/assign_fixed_chains.py --input_path=$path_for_parsed_chains --output_path=$path_for_assigned_chains --chain_list "$chains_to_design"
python ../helper_scripts/make_fixed_positions_dict.py --input_path=$path_for_parsed_chains --output_path=$path_for_fixed_positions --chain_list "$chains_to_design" --position_list "$fixed_positions"
python ../protein_mpnn_run.py \
--jsonl_path $path_for_parsed_chains \
--chain_id_jsonl $path_for_assigned_chains \
--fixed_positions_jsonl $path_for_fixed_positions \
--out_folder $output_dir \
--num_seq_per_target 2 \
--sampling_temp "0.1" \
--batch_size 1