run.py

import argparse
import copy
import json
import os.path
import random
import sys

import numpy as np
import torch
from data_utils import (
    alphabet,
    element_dict_rev,
    featurize,
    get_score,
    get_seq_rec,
    parse_PDB,
    restype_1to3,
    restype_int_to_str,
    restype_str_to_int,
    write_full_PDB,
)
from model_utils import ProteinMPNN
from prody import writePDB
from sc_utils import Packer, pack_side_chains


def main(args) -> None:
    """
    Inference function
    """
    if args.seed:
        seed = args.seed
    else:
        seed = int(np.random.randint(0, high=99999, size=1, dtype=int)[0])
    torch.manual_seed(seed)
    random.seed(seed)
    np.random.seed(seed)
    device = torch.device("cuda" if (torch.cuda.is_available()) else "cpu")
    folder_for_outputs = args.out_folder
    base_folder = folder_for_outputs
    if base_folder[-1] != "/":
        base_folder = base_folder + "/"
    if not os.path.exists(base_folder):
        os.makedirs(base_folder, exist_ok=True)
    if not os.path.exists(base_folder + "seqs"):
        os.makedirs(base_folder + "seqs", exist_ok=True)
    if not os.path.exists(base_folder + "backbones"):
        os.makedirs(base_folder + "backbones", exist_ok=True)
    if not os.path.exists(base_folder + "packed"):
        os.makedirs(base_folder + "packed", exist_ok=True)
    if args.save_stats:
        if not os.path.exists(base_folder + "stats"):
            os.makedirs(base_folder + "stats", exist_ok=True)
    if args.model_type == "protein_mpnn":
        checkpoint_path = args.checkpoint_protein_mpnn
    elif args.model_type == "ligand_mpnn":
        checkpoint_path = args.checkpoint_ligand_mpnn
    elif args.model_type == "per_residue_label_membrane_mpnn":
        checkpoint_path = args.checkpoint_per_residue_label_membrane_mpnn
    elif args.model_type == "global_label_membrane_mpnn":
        checkpoint_path = args.checkpoint_global_label_membrane_mpnn
    elif args.model_type == "soluble_mpnn":
        checkpoint_path = args.checkpoint_soluble_mpnn
    else:
        print("Choose one of the available models")
        sys.exit()
    checkpoint = torch.load(checkpoint_path, map_location=device)
    if args.model_type == "ligand_mpnn":
        atom_context_num = checkpoint["atom_context_num"]
        ligand_mpnn_use_side_chain_context = args.ligand_mpnn_use_side_chain_context
        k_neighbors = checkpoint["num_edges"]
    else:
        atom_context_num = 1
        ligand_mpnn_use_side_chain_context = 0
        k_neighbors = checkpoint["num_edges"]

    model = ProteinMPNN(
        node_features=128,
        edge_features=128,
        hidden_dim=128,
        num_encoder_layers=3,
        num_decoder_layers=3,
        k_neighbors=k_neighbors,
        device=device,
        atom_context_num=atom_context_num,
        model_type=args.model_type,
        ligand_mpnn_use_side_chain_context=ligand_mpnn_use_side_chain_context,
    )

    model.load_state_dict(checkpoint["model_state_dict"])
    model.to(device)
    model.eval()

    if args.pack_side_chains:
        model_sc = Packer(
            node_features=128,
            edge_features=128,
            num_positional_embeddings=16,
            num_chain_embeddings=16,
            num_rbf=16,
            hidden_dim=128,
            num_encoder_layers=3,
            num_decoder_layers=3,
            atom_context_num=16,
            lower_bound=0.0,
            upper_bound=20.0,
            top_k=32,
            dropout=0.0,
            augment_eps=0.0,
            atom37_order=False,
            device=device,
            num_mix=3,
        )

        checkpoint_sc = torch.load(args.checkpoint_path_sc, map_location=device)
        model_sc.load_state_dict(checkpoint_sc["model_state_dict"])
        model_sc.to(device)
        model_sc.eval()

    if args.pdb_path_multi:
        with open(args.pdb_path_multi, "r") as fh:
            pdb_paths = list(json.load(fh))
    else:
        pdb_paths = [args.pdb_path]

    if args.fixed_residues_multi:
        with open(args.fixed_residues_multi, "r") as fh:
            fixed_residues_multi = json.load(fh)
            fixed_residues_multi = {key:value.split() for key,value in fixed_residues_multi.items()}
    else:
        fixed_residues = [item for item in args.fixed_residues.split()]
        fixed_residues_multi = {}
        for pdb in pdb_paths:
            fixed_residues_multi[pdb] = fixed_residues

    if args.redesigned_residues_multi:
        with open(args.redesigned_residues_multi, "r") as fh:
            redesigned_residues_multi = json.load(fh)
            redesigned_residues_multi = {key:value.split() for key,value in redesigned_residues_multi.items()}
    else:
        redesigned_residues = [item for item in args.redesigned_residues.split()]
        redesigned_residues_multi = {}
        for pdb in pdb_paths:
            redesigned_residues_multi[pdb] = redesigned_residues

    bias_AA = torch.zeros([21], device=device, dtype=torch.float32)
    if args.bias_AA:
        tmp = [item.split(":") for item in args.bias_AA.split(",")]
        a1 = [b[0] for b in tmp]
        a2 = [float(b[1]) for b in tmp]
        for i, AA in enumerate(a1):
            bias_AA[restype_str_to_int[AA]] = a2[i]

    if args.bias_AA_per_residue_multi:
        with open(args.bias_AA_per_residue_multi, "r") as fh:
            bias_AA_per_residue_multi = json.load(
                fh
            )  # {"pdb_path" : {"A12": {"G": 1.1}}}
    else:
        if args.bias_AA_per_residue:
            with open(args.bias_AA_per_residue, "r") as fh:
                bias_AA_per_residue = json.load(fh)  # {"A12": {"G": 1.1}}
            bias_AA_per_residue_multi = {}
            for pdb in pdb_paths:
                bias_AA_per_residue_multi[pdb] = bias_AA_per_residue

    if args.omit_AA_per_residue_multi:
        with open(args.omit_AA_per_residue_multi, "r") as fh:
            omit_AA_per_residue_multi = json.load(
                fh
            )  # {"pdb_path" : {"A12": "PQR", "A13": "QS"}}
    else:
        if args.omit_AA_per_residue:
            with open(args.omit_AA_per_residue, "r") as fh:
                omit_AA_per_residue = json.load(fh)  # {"A12": "PG"}
            omit_AA_per_residue_multi = {}
            for pdb in pdb_paths:
                omit_AA_per_residue_multi[pdb] = omit_AA_per_residue
    omit_AA_list = args.omit_AA
    omit_AA = torch.tensor(
        np.array([AA in omit_AA_list for AA in alphabet]).astype(np.float32),
        device=device,
    )

    if len(args.parse_these_chains_only) != 0:
        parse_these_chains_only_list = args.parse_these_chains_only.split(",")
    else:
        parse_these_chains_only_list = []


    # loop over PDB paths
    for pdb in pdb_paths:
        if args.verbose:
            print("Designing protein from this path:", pdb)
        fixed_residues = fixed_residues_multi[pdb]
        redesigned_residues = redesigned_residues_multi[pdb]
        parse_all_atoms_flag = args.ligand_mpnn_use_side_chain_context or (
            args.pack_side_chains and not args.repack_everything
        )
        protein_dict, backbone, other_atoms, icodes, _ = parse_PDB(
            pdb,
            device=device,
            chains=parse_these_chains_only_list,
            parse_all_atoms=parse_all_atoms_flag,
            parse_atoms_with_zero_occupancy=args.parse_atoms_with_zero_occupancy,
        )
        # make chain_letter + residue_idx + insertion_code mapping to integers
        R_idx_list = list(protein_dict["R_idx"].cpu().numpy())  # residue indices
        chain_letters_list = list(protein_dict["chain_letters"])  # chain letters
        encoded_residues = []
        for i, R_idx_item in enumerate(R_idx_list):
            tmp = str(chain_letters_list[i]) + str(R_idx_item) + icodes[i]
            encoded_residues.append(tmp)
        encoded_residue_dict = dict(zip(encoded_residues, range(len(encoded_residues))))
        encoded_residue_dict_rev = dict(
            zip(list(range(len(encoded_residues))), encoded_residues)
        )

        bias_AA_per_residue = torch.zeros(
            [len(encoded_residues), 21], device=device, dtype=torch.float32
        )
        if args.bias_AA_per_residue_multi or args.bias_AA_per_residue:
            bias_dict = bias_AA_per_residue_multi[pdb]
            for residue_name, v1 in bias_dict.items():
                if residue_name in encoded_residues:
                    i1 = encoded_residue_dict[residue_name]
                    for amino_acid, v2 in v1.items():
                        if amino_acid in alphabet:
                            j1 = restype_str_to_int[amino_acid]
                            bias_AA_per_residue[i1, j1] = v2

        omit_AA_per_residue = torch.zeros(
            [len(encoded_residues), 21], device=device, dtype=torch.float32
        )
        if args.omit_AA_per_residue_multi or args.omit_AA_per_residue:
            omit_dict = omit_AA_per_residue_multi[pdb]
            for residue_name, v1 in omit_dict.items():
                if residue_name in encoded_residues:
                    i1 = encoded_residue_dict[residue_name]
                    for amino_acid in v1:
                        if amino_acid in alphabet:
                            j1 = restype_str_to_int[amino_acid]
                            omit_AA_per_residue[i1, j1] = 1.0

        fixed_positions = torch.tensor(
            [int(item not in fixed_residues) for item in encoded_residues],
            device=device,
        )
        redesigned_positions = torch.tensor(
            [int(item not in redesigned_residues) for item in encoded_residues],
            device=device,
        )

        # specify which residues are buried for checkpoint_per_residue_label_membrane_mpnn model
        if args.transmembrane_buried:
            buried_residues = [item for item in args.transmembrane_buried.split()]
            buried_positions = torch.tensor(
                [int(item in buried_residues) for item in encoded_residues],
                device=device,
            )
        else:
            buried_positions = torch.zeros_like(fixed_positions)

        if args.transmembrane_interface:
            interface_residues = [item for item in args.transmembrane_interface.split()]
            interface_positions = torch.tensor(
                [int(item in interface_residues) for item in encoded_residues],
                device=device,
            )
        else:
            interface_positions = torch.zeros_like(fixed_positions)
        protein_dict["membrane_per_residue_labels"] = 2 * buried_positions * (
            1 - interface_positions
        ) + 1 * interface_positions * (1 - buried_positions)

        if args.model_type == "global_label_membrane_mpnn":
            protein_dict["membrane_per_residue_labels"] = (
                args.global_transmembrane_label + 0 * fixed_positions
            )
        if len(args.chains_to_design) != 0:
            chains_to_design_list = args.chains_to_design.split(",")
        else:
            chains_to_design_list = protein_dict["chain_letters"]

        chain_mask = torch.tensor(
            np.array(
                [
                    item in chains_to_design_list
                    for item in protein_dict["chain_letters"]
                ],
                dtype=np.int32,
            ),
            device=device,
        )

        # create chain_mask to notify which residues are fixed (0) and which need to be designed (1)
        if redesigned_residues:
            protein_dict["chain_mask"] = chain_mask * (1 - redesigned_positions)
        elif fixed_residues:
            protein_dict["chain_mask"] = chain_mask * fixed_positions
        else:
            protein_dict["chain_mask"] = chain_mask

        if args.verbose:
            PDB_residues_to_be_redesigned = [
                encoded_residue_dict_rev[item]
                for item in range(protein_dict["chain_mask"].shape[0])
                if protein_dict["chain_mask"][item] == 1
            ]
            PDB_residues_to_be_fixed = [
                encoded_residue_dict_rev[item]
                for item in range(protein_dict["chain_mask"].shape[0])
                if protein_dict["chain_mask"][item] == 0
            ]
            print("These residues will be redesigned: ", PDB_residues_to_be_redesigned)
            print("These residues will be fixed: ", PDB_residues_to_be_fixed)

        # specify which residues are linked
        if args.symmetry_residues:
            symmetry_residues_list_of_lists = [
                x.split(",") for x in args.symmetry_residues.split("|")
            ]
            remapped_symmetry_residues = []
            for t_list in symmetry_residues_list_of_lists:
                tmp_list = []
                for t in t_list:
                    tmp_list.append(encoded_residue_dict[t])
                remapped_symmetry_residues.append(tmp_list)
        else:
            remapped_symmetry_residues = [[]]

        # specify linking weights
        if args.symmetry_weights:
            symmetry_weights = [
                [float(item) for item in x.split(",")]
                for x in args.symmetry_weights.split("|")
            ]
        else:
            symmetry_weights = [[]]

        if args.homo_oligomer:
            if args.verbose:
                print("Designing HOMO-OLIGOMER")
            chain_letters_set = list(set(chain_letters_list))
            reference_chain = chain_letters_set[0]
            lc = len(reference_chain)
            residue_indices = [
                item[lc:] for item in encoded_residues if item[:lc] == reference_chain
            ]
            remapped_symmetry_residues = []
            symmetry_weights = []
            for res in residue_indices:
                tmp_list = []
                tmp_w_list = []
                for chain in chain_letters_set:
                    name = chain + res
                    tmp_list.append(encoded_residue_dict[name])
                    tmp_w_list.append(1 / len(chain_letters_set))
                remapped_symmetry_residues.append(tmp_list)
                symmetry_weights.append(tmp_w_list)

        # set other atom bfactors to 0.0
        if other_atoms:
            other_bfactors = other_atoms.getBetas()
            other_atoms.setBetas(other_bfactors * 0.0)

        # adjust input PDB name by dropping .pdb if it does exist
        name = pdb[pdb.rfind("/") + 1 :]
        if name[-4:] == ".pdb":
            name = name[:-4]

        with torch.no_grad():
            # run featurize to remap R_idx and add batch dimension
            if args.verbose:
                if "Y" in list(protein_dict):
                    atom_coords = protein_dict["Y"].cpu().numpy()
                    atom_types = list(protein_dict["Y_t"].cpu().numpy())
                    atom_mask = list(protein_dict["Y_m"].cpu().numpy())
                    number_of_atoms_parsed = np.sum(atom_mask)
                else:
                    print("No ligand atoms parsed")
                    number_of_atoms_parsed = 0
                    atom_types = ""
                    atom_coords = []
                if number_of_atoms_parsed == 0:
                    print("No ligand atoms parsed")
                elif args.model_type == "ligand_mpnn":
                    print(
                        f"The number of ligand atoms parsed is equal to: {number_of_atoms_parsed}"
                    )
                    for i, atom_type in enumerate(atom_types):
                        print(
                            f"Type: {element_dict_rev[atom_type]}, Coords {atom_coords[i]}, Mask {atom_mask[i]}"
                        )
            feature_dict = featurize(
                protein_dict,
                cutoff_for_score=args.ligand_mpnn_cutoff_for_score,
                use_atom_context=args.ligand_mpnn_use_atom_context,
                number_of_ligand_atoms=atom_context_num,
                model_type=args.model_type,
            )
            feature_dict["batch_size"] = args.batch_size
            B, L, _, _ = feature_dict["X"].shape  # batch size should be 1 for now.
            # add additional keys to the feature dictionary
            feature_dict["temperature"] = args.temperature
            feature_dict["bias"] = (
                (-1e8 * omit_AA[None, None, :] + bias_AA).repeat([1, L, 1])
                + bias_AA_per_residue[None]
                - 1e8 * omit_AA_per_residue[None]
            )
            feature_dict["symmetry_residues"] = remapped_symmetry_residues
            feature_dict["symmetry_weights"] = symmetry_weights

            sampling_probs_list = []
            log_probs_list = []
            decoding_order_list = []
            S_list = []
            loss_list = []
            loss_per_residue_list = []
            loss_XY_list = []
            for _ in range(args.number_of_batches):
                feature_dict["randn"] = torch.randn(
                    [feature_dict["batch_size"], feature_dict["mask"].shape[1]],
                    device=device,
                )
                output_dict = model.sample(feature_dict)

                # compute confidence scores
                loss, loss_per_residue = get_score(
                    output_dict["S"],
                    output_dict["log_probs"],
                    feature_dict["mask"] * feature_dict["chain_mask"],
                )
                if args.model_type == "ligand_mpnn":
                    combined_mask = (
                        feature_dict["mask"]
                        * feature_dict["mask_XY"]
                        * feature_dict["chain_mask"]
                    )
                else:
                    combined_mask = feature_dict["mask"] * feature_dict["chain_mask"]
                loss_XY, _ = get_score(
                    output_dict["S"], output_dict["log_probs"], combined_mask
                )
                # -----
                S_list.append(output_dict["S"])
                log_probs_list.append(output_dict["log_probs"])
                sampling_probs_list.append(output_dict["sampling_probs"])
                decoding_order_list.append(output_dict["decoding_order"])
                loss_list.append(loss)
                loss_per_residue_list.append(loss_per_residue)
                loss_XY_list.append(loss_XY)
            S_stack = torch.cat(S_list, 0)
            log_probs_stack = torch.cat(log_probs_list, 0)
            sampling_probs_stack = torch.cat(sampling_probs_list, 0)
            decoding_order_stack = torch.cat(decoding_order_list, 0)
            loss_stack = torch.cat(loss_list, 0)
            loss_per_residue_stack = torch.cat(loss_per_residue_list, 0)
            loss_XY_stack = torch.cat(loss_XY_list, 0)
            rec_mask = feature_dict["mask"][:1] * feature_dict["chain_mask"][:1]
            rec_stack = get_seq_rec(feature_dict["S"][:1], S_stack, rec_mask)

            native_seq = "".join(
                [restype_int_to_str[AA] for AA in feature_dict["S"][0].cpu().numpy()]
            )
            seq_np = np.array(list(native_seq))
            seq_out_str = []
            for mask in protein_dict["mask_c"]:
                seq_out_str += list(seq_np[mask.cpu().numpy()])
                seq_out_str += [args.fasta_seq_separation]
            seq_out_str = "".join(seq_out_str)[:-1]

            output_fasta = base_folder + "/seqs/" + name + args.file_ending + ".fa"
            output_backbones = base_folder + "/backbones/"
            output_packed = base_folder + "/packed/"
            output_stats_path = base_folder + "stats/" + name + args.file_ending + ".pt"

            out_dict = {}
            out_dict["generated_sequences"] = S_stack.cpu()
            out_dict["sampling_probs"] = sampling_probs_stack.cpu()
            out_dict["log_probs"] = log_probs_stack.cpu()
            out_dict["decoding_order"] = decoding_order_stack.cpu()
            out_dict["native_sequence"] = feature_dict["S"][0].cpu()
            out_dict["mask"] = feature_dict["mask"][0].cpu()
            out_dict["chain_mask"] = feature_dict["chain_mask"][0].cpu()
            out_dict["seed"] = seed
            out_dict["temperature"] = args.temperature
            if args.save_stats:
                torch.save(out_dict, output_stats_path)

            if args.pack_side_chains:
                if args.verbose:
                    print("Packing side chains...")
                feature_dict_ = featurize(
                    protein_dict,
                    cutoff_for_score=8.0,
                    use_atom_context=args.pack_with_ligand_context,
                    number_of_ligand_atoms=16,
                    model_type="ligand_mpnn",
                )
                sc_feature_dict = copy.deepcopy(feature_dict_)
                B = args.batch_size
                for k, v in sc_feature_dict.items():
                    if k != "S":
                        try:
                            num_dim = len(v.shape)
                            if num_dim == 2:
                                sc_feature_dict[k] = v.repeat(B, 1)
                            elif num_dim == 3:
                                sc_feature_dict[k] = v.repeat(B, 1, 1)
                            elif num_dim == 4:
                                sc_feature_dict[k] = v.repeat(B, 1, 1, 1)
                            elif num_dim == 5:
                                sc_feature_dict[k] = v.repeat(B, 1, 1, 1, 1)
                        except:
                            pass
                X_stack_list = []
                X_m_stack_list = []
                b_factor_stack_list = []
                for _ in range(args.number_of_packs_per_design):
                    X_list = []
                    X_m_list = []
                    b_factor_list = []
                    for c in range(args.number_of_batches):
                        sc_feature_dict["S"] = S_list[c]
                        sc_dict = pack_side_chains(
                            sc_feature_dict,
                            model_sc,
                            args.sc_num_denoising_steps,
                            args.sc_num_samples,
                            args.repack_everything,
                        )
                        X_list.append(sc_dict["X"])
                        X_m_list.append(sc_dict["X_m"])
                        b_factor_list.append(sc_dict["b_factors"])

                    X_stack = torch.cat(X_list, 0)
                    X_m_stack = torch.cat(X_m_list, 0)
                    b_factor_stack = torch.cat(b_factor_list, 0)

                    X_stack_list.append(X_stack)
                    X_m_stack_list.append(X_m_stack)
                    b_factor_stack_list.append(b_factor_stack)

            with open(output_fasta, "w") as f:
                f.write(
                    ">{}, T={}, seed={}, num_res={}, num_ligand_res={}, use_ligand_context={}, ligand_cutoff_distance={}, batch_size={}, number_of_batches={}, model_path={}\n{}\n".format(
                        name,
                        args.temperature,
                        seed,
                        torch.sum(rec_mask).cpu().numpy(),
                        torch.sum(combined_mask[:1]).cpu().numpy(),
                        bool(args.ligand_mpnn_use_atom_context),
                        float(args.ligand_mpnn_cutoff_for_score),
                        args.batch_size,
                        args.number_of_batches,
                        checkpoint_path,
                        seq_out_str,
                    )
                )
                for ix in range(S_stack.shape[0]):
                    ix_suffix = ix
                    if not args.zero_indexed:
                        ix_suffix += 1
                    seq_rec_print = np.format_float_positional(
                        rec_stack[ix].cpu().numpy(), unique=False, precision=4
                    )
                    loss_np = np.format_float_positional(
                        np.exp(-loss_stack[ix].cpu().numpy()), unique=False, precision=4
                    )
                    loss_XY_np = np.format_float_positional(
                        np.exp(-loss_XY_stack[ix].cpu().numpy()),
                        unique=False,
                        precision=4,
                    )
                    seq = "".join(
                        [restype_int_to_str[AA] for AA in S_stack[ix].cpu().numpy()]
                    )

                    # write new sequences into PDB with backbone coordinates
                    seq_prody = np.array([restype_1to3[AA] for AA in list(seq)])[
                        None,
                    ].repeat(4, 1)
                    bfactor_prody = (
                        loss_per_residue_stack[ix].cpu().numpy()[None, :].repeat(4, 1)
                    )
                    backbone.setResnames(seq_prody)
                    backbone.setBetas(
                        np.exp(-bfactor_prody)
                        * (bfactor_prody > 0.01).astype(np.float32)
                    )
                    if other_atoms:
                        writePDB(
                            output_backbones
                            + name
                            + "_"
                            + str(ix_suffix)
                            + args.file_ending
                            + ".pdb",
                            backbone + other_atoms,
                        )
                    else:
                        writePDB(
                            output_backbones
                            + name
                            + "_"
                            + str(ix_suffix)
                            + args.file_ending
                            + ".pdb",
                            backbone,
                        )

                    # write full PDB files
                    if args.pack_side_chains:
                        for c_pack in range(args.number_of_packs_per_design):
                            X_stack = X_stack_list[c_pack]
                            X_m_stack = X_m_stack_list[c_pack]
                            b_factor_stack = b_factor_stack_list[c_pack]
                            write_full_PDB(
                                output_packed
                                + name
                                + args.packed_suffix
                                + "_"
                                + str(ix_suffix)
                                + "_"
                                + str(c_pack + 1)
                                + args.file_ending
                                + ".pdb",
                                X_stack[ix].cpu().numpy(),
                                X_m_stack[ix].cpu().numpy(),
                                b_factor_stack[ix].cpu().numpy(),
                                feature_dict["R_idx_original"][0].cpu().numpy(),
                                protein_dict["chain_letters"],
                                S_stack[ix].cpu().numpy(),
                                other_atoms=other_atoms,
                                icodes=icodes,
                                force_hetatm=args.force_hetatm,
                            )
                    # -----

                    # write fasta lines
                    seq_np = np.array(list(seq))
                    seq_out_str = []
                    for mask in protein_dict["mask_c"]:
                        seq_out_str += list(seq_np[mask.cpu().numpy()])
                        seq_out_str += [args.fasta_seq_separation]
                    seq_out_str = "".join(seq_out_str)[:-1]
                    if ix == S_stack.shape[0] - 1:
                        # final 2 lines
                        f.write(
                            ">{}, id={}, T={}, seed={}, overall_confidence={}, ligand_confidence={}, seq_rec={}\n{}".format(
                                name,
                                ix_suffix,
                                args.temperature,
                                seed,
                                loss_np,
                                loss_XY_np,
                                seq_rec_print,
                                seq_out_str,
                            )
                        )
                    else:
                        f.write(
                            ">{}, id={}, T={}, seed={}, overall_confidence={}, ligand_confidence={}, seq_rec={}\n{}\n".format(
                                name,
                                ix_suffix,
                                args.temperature,
                                seed,
                                loss_np,
                                loss_XY_np,
                                seq_rec_print,
                                seq_out_str,
                            )
                        )


if __name__ == "__main__":
    argparser = argparse.ArgumentParser(
        formatter_class=argparse.ArgumentDefaultsHelpFormatter
    )

    argparser.add_argument(
        "--model_type",
        type=str,
        default="protein_mpnn",
        help="Choose your model: protein_mpnn, ligand_mpnn, per_residue_label_membrane_mpnn, global_label_membrane_mpnn, soluble_mpnn",
    )
    # protein_mpnn - original ProteinMPNN trained on the whole PDB exluding non-protein atoms
    # ligand_mpnn - atomic context aware model trained with small molecules, nucleotides, metals etc on the whole PDB
    # per_residue_label_membrane_mpnn - ProteinMPNN model trained with addition label per residue specifying if that residue is buried or exposed
    # global_label_membrane_mpnn - ProteinMPNN model trained with global label per PDB id to specify if protein is transmembrane
    # soluble_mpnn - ProteinMPNN trained only on soluble PDB ids
    argparser.add_argument(
        "--checkpoint_protein_mpnn",
        type=str,
        default="./model_params/proteinmpnn_v_48_020.pt",
        help="Path to model weights.",
    )
    argparser.add_argument(
        "--checkpoint_ligand_mpnn",
        type=str,
        default="./model_params/ligandmpnn_v_32_010_25.pt",
        help="Path to model weights.",
    )
    argparser.add_argument(
        "--checkpoint_per_residue_label_membrane_mpnn",
        type=str,
        default="./model_params/per_residue_label_membrane_mpnn_v_48_020.pt",
        help="Path to model weights.",
    )
    argparser.add_argument(
        "--checkpoint_global_label_membrane_mpnn",
        type=str,
        default="./model_params/global_label_membrane_mpnn_v_48_020.pt",
        help="Path to model weights.",
    )
    argparser.add_argument(
        "--checkpoint_soluble_mpnn",
        type=str,
        default="./model_params/solublempnn_v_48_020.pt",
        help="Path to model weights.",
    )

    argparser.add_argument(
        "--fasta_seq_separation",
        type=str,
        default=":",
        help="Symbol to use between sequences from different chains",
    )
    argparser.add_argument("--verbose", type=int, default=1, help="Print stuff")

    argparser.add_argument(
        "--pdb_path", type=str, default="", help="Path to the input PDB."
    )
    argparser.add_argument(
        "--pdb_path_multi",
        type=str,
        default="",
        help="Path to json listing PDB paths. {'/path/to/pdb': ''} - only keys will be used.",
    )

    argparser.add_argument(
        "--fixed_residues",
        type=str,
        default="",
        help="Provide fixed residues, A12 A13 A14 B2 B25",
    )
    argparser.add_argument(
        "--fixed_residues_multi",
        type=str,
        default="",
        help="Path to json mapping of fixed residues for each pdb i.e., {'/path/to/pdb': 'A12 A13 A14 B2 B25'}",
    )

    argparser.add_argument(
        "--redesigned_residues",
        type=str,
        default="",
        help="Provide to be redesigned residues, everything else will be fixed, A12 A13 A14 B2 B25",
    )
    argparser.add_argument(
        "--redesigned_residues_multi",
        type=str,
        default="",
        help="Path to json mapping of redesigned residues for each pdb i.e., {'/path/to/pdb': 'A12 A13 A14 B2 B25'}",
    )

    argparser.add_argument(
        "--bias_AA",
        type=str,
        default="",
        help="Bias generation of amino acids, e.g. 'A:-1.024,P:2.34,C:-12.34'",
    )
    argparser.add_argument(
        "--bias_AA_per_residue",
        type=str,
        default="",
        help="Path to json mapping of bias {'A12': {'G': -0.3, 'C': -2.0, 'H': 0.8}, 'A13': {'G': -1.3}}",
    )
    argparser.add_argument(
        "--bias_AA_per_residue_multi",
        type=str,
        default="",
        help="Path to json mapping of bias {'pdb_path': {'A12': {'G': -0.3, 'C': -2.0, 'H': 0.8}, 'A13': {'G': -1.3}}}",
    )

    argparser.add_argument(
        "--omit_AA",
        type=str,
        default="",
        help="Bias generation of amino acids, e.g. 'ACG'",
    )
    argparser.add_argument(
        "--omit_AA_per_residue",
        type=str,
        default="",
        help="Path to json mapping of bias {'A12': 'APQ', 'A13': 'QST'}",
    )
    argparser.add_argument(
        "--omit_AA_per_residue_multi",
        type=str,
        default="",
        help="Path to json mapping of bias {'pdb_path': {'A12': 'QSPC', 'A13': 'AGE'}}",
    )

    argparser.add_argument(
        "--symmetry_residues",
        type=str,
        default="",
        help="Add list of lists for which residues need to be symmetric, e.g. 'A12,A13,A14|C2,C3|A5,B6'",
    )
    argparser.add_argument(
        "--symmetry_weights",
        type=str,
        default="",
        help="Add weights that match symmetry_residues, e.g. '1.01,1.0,1.0|-1.0,2.0|2.0,2.3'",
    )
    argparser.add_argument(
        "--homo_oligomer",
        type=int,
        default=0,
        help="Setting this to 1 will automatically set --symmetry_residues and --symmetry_weights to do homooligomer design with equal weighting.",
    )

    argparser.add_argument(
        "--out_folder",
        type=str,
        help="Path to a folder to output sequences, e.g. /home/out/",
    )
    argparser.add_argument(
        "--file_ending", type=str, default="", help="adding_string_to_the_end"
    )
    argparser.add_argument(
        "--zero_indexed",
        type=str,
        default=0,
        help="1 - to start output PDB numbering with 0",
    )
    argparser.add_argument(
        "--seed",
        type=int,
        default=0,
        help="Set seed for torch, numpy, and python random.",
    )
    argparser.add_argument(
        "--batch_size",
        type=int,
        default=1,
        help="Number of sequence to generate per one pass.",
    )
    argparser.add_argument(
        "--number_of_batches",
        type=int,
        default=1,
        help="Number of times to design sequence using a chosen batch size.",
    )
    argparser.add_argument(
        "--temperature",
        type=float,
        default=0.1,
        help="Temperature to sample sequences.",
    )
    argparser.add_argument(
        "--save_stats", type=int, default=0, help="Save output statistics"
    )

    argparser.add_argument(
        "--ligand_mpnn_use_atom_context",
        type=int,
        default=1,
        help="1 - use atom context, 0 - do not use atom context.",
    )
    argparser.add_argument(
        "--ligand_mpnn_cutoff_for_score",
        type=float,
        default=8.0,
        help="Cutoff in angstroms between protein and context atoms to select residues for reporting score.",
    )
    argparser.add_argument(
        "--ligand_mpnn_use_side_chain_context",
        type=int,
        default=0,
        help="Flag to use side chain atoms as ligand context for the fixed residues",
    )
    argparser.add_argument(
        "--chains_to_design",
        type=str,
        default="",
        help="Specify which chains to redesign, all others will be kept fixed, 'A,B,C,F'",
    )

    argparser.add_argument(
        "--parse_these_chains_only",
        type=str,
        default="",
        help="Provide chains letters for parsing backbones, 'A,B,C,F'",
    )

    argparser.add_argument(
        "--transmembrane_buried",
        type=str,
        default="",
        help="Provide buried residues when using checkpoint_per_residue_label_membrane_mpnn model, A12 A13 A14 B2 B25",
    )
    argparser.add_argument(
        "--transmembrane_interface",
        type=str,
        default="",
        help="Provide interface residues when using checkpoint_per_residue_label_membrane_mpnn model, A12 A13 A14 B2 B25",
    )

    argparser.add_argument(
        "--global_transmembrane_label",
        type=int,
        default=0,
        help="Provide global label for global_label_membrane_mpnn model. 1 - transmembrane, 0 - soluble",
    )

    argparser.add_argument(
        "--parse_atoms_with_zero_occupancy",
        type=int,
        default=0,
        help="To parse atoms with zero occupancy in the PDB input files. 0 - do not parse, 1 - parse atoms with zero occupancy",
    )

    argparser.add_argument(
        "--pack_side_chains",
        type=int,
        default=0,
        help="1 - to run side chain packer, 0 - do not run it",
    )

    argparser.add_argument(
        "--checkpoint_path_sc",
        type=str,
        default="./model_params/ligandmpnn_sc_v_32_002_16.pt",
        help="Path to model weights.",
    )

    argparser.add_argument(
        "--number_of_packs_per_design",
        type=int,
        default=4,
        help="Number of independent side chain packing samples to return per design",
    )

    argparser.add_argument(
        "--sc_num_denoising_steps",
        type=int,
        default=3,
        help="Number of denoising/recycling steps to make for side chain packing",
    )

    argparser.add_argument(
        "--sc_num_samples",
        type=int,
        default=16,
        help="Number of samples to draw from a mixture distribution and then take a sample with the highest likelihood.",
    )

    argparser.add_argument(
        "--repack_everything",
        type=int,
        default=0,
        help="1 - repacks side chains of all residues including the fixed ones; 0 - keeps the side chains fixed for fixed residues",
    )

    argparser.add_argument(
        "--force_hetatm",
        type=int,
        default=0,
        help="To force ligand atoms to be written as HETATM to PDB file after packing.",
    )

    argparser.add_argument(
        "--packed_suffix",
        type=str,
        default="_packed",
        help="Suffix for packed PDB paths",
    )

    argparser.add_argument(
        "--pack_with_ligand_context",
        type=int,
        default=1,
        help="1-pack side chains using ligand context, 0 - do not use it.",
    )

    args = argparser.parse_args()
    main(args)