import copy import re import os.path import torch import sys import gradio as gr import pandas as pd import numpy as np import plotly.express as px import matplotlib.pyplot as plt import plotly.graph_objects as go import tempfile import requests from moleculekit.molecule import Molecule sys.path.append("/home/user/app/ProteinMPNN/vanilla_proteinmpnn") # this is for local sys.path.append(os.path.join(os.getcwd(), "ProteinMPNN/vanilla_proteinmpnn")) def make_tied_positions_for_homomers(pdb_dict_list): my_dict = {} for result in pdb_dict_list: all_chain_list = sorted( [item[-1:] for item in list(result) if item[:9] == "seq_chain"] ) # A, B, C, ... tied_positions_list = [] chain_length = len(result[f"seq_chain_{all_chain_list[0]}"]) for i in range(1, chain_length + 1): temp_dict = {} for j, chain in enumerate(all_chain_list): temp_dict[chain] = [i] # needs to be a list tied_positions_list.append(temp_dict) my_dict[result["name"]] = tied_positions_list return my_dict def align_structures(pdb1, pdb2, index): """Take two structure and superimpose pdb1 on pdb2""" import Bio.PDB import subprocess pdb_parser = Bio.PDB.PDBParser(QUIET=True) # Get the structures ref_structure = pdb_parser.get_structure("ref", pdb1) sample_structure = pdb_parser.get_structure("sample", pdb2) sample_structure_ca = [ atom for atom in sample_structure.get_atoms() if atom.name == "CA" ] plddts = [atom.get_bfactor() for atom in sample_structure_ca] aligner = Bio.PDB.CEAligner() aligner.set_reference(ref_structure) aligner.align(sample_structure) io = Bio.PDB.PDBIO() io.set_structure(ref_structure) hash = os.path.splitext(os.path.basename(pdb2))[0] io.save(f"outputs/{hash}_ref_{index}.pdb") io.set_structure(sample_structure) io.save(f"outputs/{hash}_align_{index}.pdb") # Doing this to get around biopython CEALIGN bug # subprocess.call("pymol -c -Q -r cealign.pml", shell=True) return ( aligner.rms, f"outputs/{hash}_ref_{index}.pdb", f"outputs/{hash}_align_{index}.pdb", plddts, ) if not os.path.exists("/home/user/app/ProteinMPNN/"): path_to_model_weights = os.path.join( os.getcwd(), "ProteinMPNN/vanilla_proteinmpnn/vanilla_model_weights" ) is_local = True else: path_to_model_weights = ( "/home/user/app/ProteinMPNN/vanilla_proteinmpnn/vanilla_model_weights" ) is_local = False if is_local: print("Running locally") from transformers import AutoTokenizer, EsmForProteinFolding def setup_proteinmpnn(model_name="v_48_020", backbone_noise=0.00): from protein_mpnn_utils import ( loss_nll, loss_smoothed, gather_edges, gather_nodes, gather_nodes_t, cat_neighbors_nodes, _scores, _S_to_seq, tied_featurize, parse_PDB, ) from protein_mpnn_utils import StructureDataset, StructureDatasetPDB, ProteinMPNN device = torch.device( "cpu" ) # torch.device("cuda:0" if (torch.cuda.is_available()) else "cpu") #fix for memory issues # ProteinMPNN model name: v_48_002, v_48_010, v_48_020, v_48_030, v_32_002, v_32_010; v_32_020, v_32_030; v_48_010=version with 48 edges 0.10A noise # Standard deviation of Gaussian noise to add to backbone atoms hidden_dim = 128 num_layers = 3 model_folder_path = path_to_model_weights if model_folder_path[-1] != "/": model_folder_path = model_folder_path + "/" checkpoint_path = model_folder_path + f"{model_name}.pt" checkpoint = torch.load(checkpoint_path, map_location=device) noise_level_print = checkpoint["noise_level"] model = ProteinMPNN( num_letters=21, node_features=hidden_dim, edge_features=hidden_dim, hidden_dim=hidden_dim, num_encoder_layers=num_layers, num_decoder_layers=num_layers, augment_eps=backbone_noise, k_neighbors=checkpoint["num_edges"], ) model.to(device) model.load_state_dict(checkpoint["model_state_dict"]) model.eval() return model, device def get_pdb(pdb_code="", filepath=""): if pdb_code is None or pdb_code == "": try: return filepath.name except AttributeError as e: return None else: os.system(f"wget -qnc https://files.rcsb.org/view/{pdb_code}.pdb") return f"{pdb_code}.pdb" def preprocess_mol(pdb_code="", filepath=""): if pdb_code is None or pdb_code == "": try: mol = Molecule(filepath.name) except AttributeError as e: return None else: mol = Molecule(pdb_code) mol.write("original.pdb") # clean messy files and only include protein itself mol.filter("protein") # renumber using moleculekit 0...len(protein) df = mol.renumberResidues(returnMapping=True) # add proteinMPNN index col which used 1..len(chain), 1...len(chain) indexes = [] for chain, g in df.groupby("chain"): j = 1 for i, row in g.iterrows(): indexes.append(j) j += 1 df["proteinMPNN_index"] = indexes mol.write("cleaned.pdb") return "cleaned.pdb", df def assign_sasa(mol): from moleculekit.projections.metricsasa import MetricSasa metr = MetricSasa(mode="residue", filtersel="protein") sasaR = metr.project(mol)[0] is_prot = mol.atomselect("protein") resids = pd.DataFrame.from_dict({"resid": mol.resid, "is_prot": is_prot}) new_masses = [] i_without_non_prot = 0 for i, g in resids.groupby((resids["resid"].shift() != resids["resid"]).cumsum()): if g["is_prot"].unique()[0] == True: g["sasa"] = sasaR[i_without_non_prot] i_without_non_prot += 1 else: g["sasa"] = 0 new_masses.extend(list(g.sasa)) return np.array(new_masses) def process_atomsel(atomsel): """everything lowercase and replace some keywords not relevant for protein design""" atomsel = re.sub("sasa", "mass", atomsel, flags=re.I) atomsel = re.sub("plddt", "beta", atomsel, flags=re.I) return atomsel def make_fixed_positions_dict(atomsel, residue_index_df): # we use the uploaded file for the selection mol = Molecule("original.pdb") # use index for selection as resids will change # set sasa to 0 for all non protein atoms (all non protein atoms are deleted later) mol.masses = assign_sasa(mol) print(mol.masses.shape) print(assign_sasa(mol).shape) atomsel = process_atomsel(atomsel) selected_residues = mol.get("index", atomsel) # clean up mol.filter("protein") mol.renumberResidues() # based on selected index now get resids selected_residues = [str(i) for i in selected_residues] if len(selected_residues) == 0: return None, [] selected_residues_str = " ".join(selected_residues) selected_residues = set(mol.get("resid", sel=f"index {selected_residues_str}")) # use the proteinMPNN index nomenclature to assemble fixed_positions_dict fixed_positions_df = residue_index_df[ residue_index_df["new_resid"].isin(selected_residues) ] chains = set(mol.get("chain", sel="all")) fixed_position_dict = {"cleaned": {}} # store the selected residues in a list for the visualization later with cleaned.pdb selected_residues = list(fixed_positions_df["new_resid"]) for c in chains: fixed_position_dict["cleaned"][c] = [] for i, row in fixed_positions_df.iterrows(): fixed_position_dict["cleaned"][row["chain"]].append(row["proteinMPNN_index"]) return fixed_position_dict, selected_residues def update( inp, file, designed_chain, fixed_chain, homomer, num_seqs, sampling_temp, model_name, backbone_noise, atomsel, ): from protein_mpnn_utils import ( loss_nll, loss_smoothed, gather_edges, gather_nodes, gather_nodes_t, cat_neighbors_nodes, _scores, _S_to_seq, tied_featurize, parse_PDB, ) from protein_mpnn_utils import StructureDataset, StructureDatasetPDB, ProteinMPNN # pdb_path = get_pdb(pdb_code=inp, filepath=file) pdb_path, mol_index = preprocess_mol(pdb_code=inp, filepath=file) if pdb_path == None: return "Error processing PDB" model, device = setup_proteinmpnn( model_name=model_name, backbone_noise=backbone_noise ) if designed_chain == "": designed_chain_list = [] else: designed_chain_list = re.sub("[^A-Za-z]+", ",", designed_chain).split(",") if fixed_chain == "": fixed_chain_list = [] else: fixed_chain_list = re.sub("[^A-Za-z]+", ",", fixed_chain).split(",") chain_list = list(set(designed_chain_list + fixed_chain_list)) num_seq_per_target = num_seqs save_score = 0 # 0 for False, 1 for True; save score=-log_prob to npy files save_probs = ( 0 # 0 for False, 1 for True; save MPNN predicted probabilites per position ) score_only = 0 # 0 for False, 1 for True; score input backbone-sequence pairs conditional_probs_only = 0 # 0 for False, 1 for True; output conditional probabilities p(s_i given the rest of the sequence and backbone) conditional_probs_only_backbone = 0 # 0 for False, 1 for True; if true output conditional probabilities p(s_i given backbone) batch_size = 1 # Batch size; can set higher for titan, quadro GPUs, reduce this if running out of GPU memory max_length = 20000 # Max sequence length out_folder = "." # Path to a folder to output sequences, e.g. /home/out/ jsonl_path = "" # Path to a folder with parsed pdb into jsonl omit_AAs = "X" # Specify which amino acids should be omitted in the generated sequence, e.g. 'AC' would omit alanine and cystine. pssm_multi = 0.0 # A value between [0.0, 1.0], 0.0 means do not use pssm, 1.0 ignore MPNN predictions pssm_threshold = 0.0 # A value between -inf + inf to restric per position AAs pssm_log_odds_flag = 0 # 0 for False, 1 for True pssm_bias_flag = 0 # 0 for False, 1 for True folder_for_outputs = out_folder NUM_BATCHES = num_seq_per_target // batch_size BATCH_COPIES = batch_size temperatures = [sampling_temp] omit_AAs_list = omit_AAs alphabet = "ACDEFGHIKLMNPQRSTVWYX" omit_AAs_np = np.array([AA in omit_AAs_list for AA in alphabet]).astype(np.float32) chain_id_dict = None if atomsel == "": fixed_positions_dict, selected_residues = None, [] else: fixed_positions_dict, selected_residues = make_fixed_positions_dict( atomsel, mol_index ) pssm_dict = None omit_AA_dict = None bias_AA_dict = None bias_by_res_dict = None bias_AAs_np = np.zeros(len(alphabet)) ############################################################### pdb_dict_list = parse_PDB(pdb_path, input_chain_list=chain_list) dataset_valid = StructureDatasetPDB( pdb_dict_list, truncate=None, max_length=max_length ) if homomer: tied_positions_dict = make_tied_positions_for_homomers(pdb_dict_list) else: tied_positions_dict = None chain_id_dict = {} chain_id_dict[pdb_dict_list[0]["name"]] = (designed_chain_list, fixed_chain_list) with torch.no_grad(): for ix, prot in enumerate(dataset_valid): score_list = [] all_probs_list = [] all_log_probs_list = [] S_sample_list = [] batch_clones = [copy.deepcopy(prot) for i in range(BATCH_COPIES)] ( X, S, mask, lengths, chain_M, chain_encoding_all, chain_list_list, visible_list_list, masked_list_list, masked_chain_length_list_list, chain_M_pos, omit_AA_mask, residue_idx, dihedral_mask, tied_pos_list_of_lists_list, pssm_coef, pssm_bias, pssm_log_odds_all, bias_by_res_all, tied_beta, ) = tied_featurize( batch_clones, device, chain_id_dict, fixed_positions_dict, omit_AA_dict, tied_positions_dict, pssm_dict, bias_by_res_dict, ) pssm_log_odds_mask = ( pssm_log_odds_all > pssm_threshold ).float() # 1.0 for true, 0.0 for false name_ = batch_clones[0]["name"] randn_1 = torch.randn(chain_M.shape, device=X.device) log_probs = model( X, S, mask, chain_M * chain_M_pos, residue_idx, chain_encoding_all, randn_1, ) mask_for_loss = mask * chain_M * chain_M_pos scores = _scores(S, log_probs, mask_for_loss) native_score = scores.cpu().data.numpy() message = "" seq_list = [] seq_recovery = [] seq_score = [] for temp in temperatures: for j in range(NUM_BATCHES): randn_2 = torch.randn(chain_M.shape, device=X.device) if tied_positions_dict == None: sample_dict = model.sample( X, randn_2, S, chain_M, chain_encoding_all, residue_idx, mask=mask, temperature=temp, omit_AAs_np=omit_AAs_np, bias_AAs_np=bias_AAs_np, chain_M_pos=chain_M_pos, omit_AA_mask=omit_AA_mask, pssm_coef=pssm_coef, pssm_bias=pssm_bias, pssm_multi=pssm_multi, pssm_log_odds_flag=bool(pssm_log_odds_flag), pssm_log_odds_mask=pssm_log_odds_mask, pssm_bias_flag=bool(pssm_bias_flag), bias_by_res=bias_by_res_all, ) S_sample = sample_dict["S"] else: sample_dict = model.tied_sample( X, randn_2, S, chain_M, chain_encoding_all, residue_idx, mask=mask, temperature=temp, omit_AAs_np=omit_AAs_np, bias_AAs_np=bias_AAs_np, chain_M_pos=chain_M_pos, omit_AA_mask=omit_AA_mask, pssm_coef=pssm_coef, pssm_bias=pssm_bias, pssm_multi=pssm_multi, pssm_log_odds_flag=bool(pssm_log_odds_flag), pssm_log_odds_mask=pssm_log_odds_mask, pssm_bias_flag=bool(pssm_bias_flag), tied_pos=tied_pos_list_of_lists_list[0], tied_beta=tied_beta, bias_by_res=bias_by_res_all, ) # Compute scores S_sample = sample_dict["S"] log_probs = model( X, S_sample, mask, chain_M * chain_M_pos, residue_idx, chain_encoding_all, randn_2, use_input_decoding_order=True, decoding_order=sample_dict["decoding_order"], ) mask_for_loss = mask * chain_M * chain_M_pos scores = _scores(S_sample, log_probs, mask_for_loss) scores = scores.cpu().data.numpy() all_probs_list.append(sample_dict["probs"].cpu().data.numpy()) all_log_probs_list.append(log_probs.cpu().data.numpy()) S_sample_list.append(S_sample.cpu().data.numpy()) for b_ix in range(BATCH_COPIES): masked_chain_length_list = masked_chain_length_list_list[b_ix] masked_list = masked_list_list[b_ix] seq_recovery_rate = torch.sum( torch.sum( torch.nn.functional.one_hot(S[b_ix], 21) * torch.nn.functional.one_hot(S_sample[b_ix], 21), axis=-1, ) * mask_for_loss[b_ix] ) / torch.sum(mask_for_loss[b_ix]) seq = _S_to_seq(S_sample[b_ix], chain_M[b_ix]) score = scores[b_ix] score_list.append(score) native_seq = _S_to_seq(S[b_ix], chain_M[b_ix]) if b_ix == 0 and j == 0 and temp == temperatures[0]: start = 0 end = 0 list_of_AAs = [] for mask_l in masked_chain_length_list: end += mask_l list_of_AAs.append(native_seq[start:end]) start = end native_seq = "".join( list(np.array(list_of_AAs)[np.argsort(masked_list)]) ) l0 = 0 for mc_length in list( np.array(masked_chain_length_list)[ np.argsort(masked_list) ] )[:-1]: l0 += mc_length native_seq = native_seq[:l0] + "/" + native_seq[l0:] l0 += 1 sorted_masked_chain_letters = np.argsort( masked_list_list[0] ) print_masked_chains = [ masked_list_list[0][i] for i in sorted_masked_chain_letters ] sorted_visible_chain_letters = np.argsort( visible_list_list[0] ) print_visible_chains = [ visible_list_list[0][i] for i in sorted_visible_chain_letters ] native_score_print = np.format_float_positional( np.float32(native_score.mean()), unique=False, precision=4, ) line = ">{}, score={}, fixed_chains={}, designed_chains={}, model_name={}\n{}\n".format( name_, native_score_print, print_visible_chains, print_masked_chains, model_name, native_seq, ) message += f"{line}\n" start = 0 end = 0 list_of_AAs = [] for mask_l in masked_chain_length_list: end += mask_l list_of_AAs.append(seq[start:end]) start = end seq = "".join( list(np.array(list_of_AAs)[np.argsort(masked_list)]) ) # add non designed chains to predicted sequence l0 = 0 for mc_length in list( np.array(masked_chain_length_list)[np.argsort(masked_list)] )[:-1]: l0 += mc_length seq = seq[:l0] + "/" + seq[l0:] l0 += 1 score_print = np.format_float_positional( np.float32(score), unique=False, precision=4 ) seq_rec_print = np.format_float_positional( np.float32(seq_recovery_rate.detach().cpu().numpy()), unique=False, precision=4, ) chain_s = "" if len(visible_list_list[0]) > 0: chain_M_bool = chain_M.bool() not_designed = _S_to_seq(S[b_ix], ~chain_M_bool[b_ix]) labels = ( chain_encoding_all[b_ix][~chain_M_bool[b_ix]] .detach() .cpu() .numpy() ) for c in set(labels): chain_s += ":" nd_mask = labels == c for i, x in enumerate(not_designed): if nd_mask[i]: chain_s += x seq_recovery.append(seq_rec_print) seq_score.append(score_print) line = ( ">T={}, sample={}, score={}, seq_recovery={}\n{}\n".format( temp, b_ix, score_print, seq_rec_print, seq ) ) seq_list.append(seq + chain_s) message += f"{line}\n" if fixed_positions_dict != None: message += f"\nfixed positions:* {fixed_positions_dict['cleaned']} \n\n*uses CHAIN:[1..len(chain)] residue numbering" # somehow sequences still contain X, remove again for i, x in enumerate(seq_list): for aa in omit_AAs: seq_list[i] = x.replace(aa, "") all_probs_concat = np.concatenate(all_probs_list) all_log_probs_concat = np.concatenate(all_log_probs_list) np.savetxt("all_probs_concat.csv", all_probs_concat.mean(0).T, delimiter=",") np.savetxt( "all_log_probs_concat.csv", np.exp(all_log_probs_concat).mean(0).T, delimiter=",", ) S_sample_concat = np.concatenate(S_sample_list) fig = px.imshow( np.exp(all_log_probs_concat).mean(0).T, labels=dict(x="positions", y="amino acids", color="probability"), y=list(alphabet), template="simple_white", ) fig.update_xaxes(side="top") fig_tadjusted = px.imshow( all_probs_concat.mean(0).T, labels=dict(x="positions", y="amino acids", color="probability"), y=list(alphabet), template="simple_white", ) fig_tadjusted.update_xaxes(side="top") seq_dict = {"seq_list": seq_list, "recovery": seq_recovery, "seq_score": seq_score} mol = structure_pred(seq_dict, pdb_path, selected_residues) print(seq_list) return ( message, fig, fig_tadjusted, gr.File.update(value="all_log_probs_concat.csv", visible=True), gr.File.update(value="all_probs_concat.csv", visible=True), pdb_path, gr.Dropdown.update(choices=seq_list, value=seq_list[0], interactive=True), selected_residues, seq_dict, mol, ) def updateseq(seq, seq_dict, pdb_path, selected_residues): # find index of seq in seq_dict seq_list = seq_dict["seq_list"] seq_index = seq_list.index(seq) print(seq, seq_index) mol = structure_pred(seq_dict, pdb_path, selected_residues, index=seq_index) return mol from transformers.models.esm.openfold_utils.protein import to_pdb, Protein as OFProtein from transformers.models.esm.openfold_utils.feats import atom14_to_atom37 def convert_outputs_to_pdb(outputs): final_atom_positions = atom14_to_atom37(outputs["positions"][-1], outputs) outputs = {k: v.to("cpu").numpy() for k, v in outputs.items()} final_atom_positions = final_atom_positions.cpu().numpy() final_atom_mask = outputs["atom37_atom_exists"] pdbs = [] for i in range(outputs["aatype"].shape[0]): aa = outputs["aatype"][i] pred_pos = final_atom_positions[i] mask = final_atom_mask[i] resid = outputs["residue_index"][i] + 1 pred = OFProtein( aatype=aa, atom_positions=pred_pos, atom_mask=mask, residue_index=resid, b_factors=outputs["plddt"][i], chain_index=outputs["chain_index"][i] if "chain_index" in outputs else None, ) pdbs.append(to_pdb(pred)) return pdbs def get_esmfold_local(sequence): filename = "outputs/" + hashlib.md5(str.encode(sequence)).hexdigest() + ".pdb" if not os.path.exists(filename): tokenizer = AutoTokenizer.from_pretrained("facebook/esmfold_v1") model = EsmForProteinFolding.from_pretrained( "facebook/esmfold_v1", low_cpu_mem_usage=True ) model = model.cuda() model.esm = model.esm.half() import torch torch.backends.cuda.matmul.allow_tf32 = True model.trunk.set_chunk_size(64) position_id_offsets = [] linker_mask = [] for i, s in enumerate(sequence.split("/")): linker = 25 if i < sequence.count("/") else 0 offsets = [i * 512] * (len(s) + linker) linker_mask.extend([1] * len(s) + [0] * linker) position_id_offsets.extend(offsets) sequence = sequence.replace("/", "G" * 25) tokenized = tokenizer([sequence], return_tensors="pt", add_special_tokens=False) with torch.no_grad(): position_ids = torch.arange(len(sequence), dtype=torch.long) position_ids = position_ids + torch.torch.LongTensor(position_id_offsets) linker_mask = torch.Tensor(linker_mask).unsqueeze(1) tokenized["position_ids"] = position_ids.unsqueeze(0) tokenized = {key: tensor.cuda() for key, tensor in tokenized.items()} with torch.no_grad(): output = model(**tokenized) output["atom37_atom_exists"] = output["atom37_atom_exists"] * linker_mask.to( output["atom37_atom_exists"].device ) pdb = convert_outputs_to_pdb(output) with open(filename, "w+") as f: f.write("".join(pdb)) print("local prediction", filename) else: print("prediction already on disk") return filename def structure_pred(seq_dict, pdb, selectedResidues, index=0): allSeqs = seq_dict["seq_list"] lenSeqs = len(allSeqs) if len(allSeqs[index]) > 400: return """ """ if "/" in allSeqs[index] and not is_local: return """ """ i = 0 sequences = {} if is_local: pdb_file = get_esmfold_local(allSeqs[index]) else: pdb_file = get_esmfold(allSeqs[index]) rms, input_pdb, aligned_pdb, plddts = align_structures(pdb, pdb_file, index) sequences[i] = { "Seq": index, "RMSD": f"{rms:.2f}", "Score": seq_dict["seq_score"][i], "Recovery": seq_dict["recovery"][i], "Mean pLDDT": f"{np.mean(plddts):.4f}", } num_res = len(allSeqs[index]) return molecule( input_pdb, aligned_pdb, lenSeqs, num_res, selectedResidues, allSeqs, sequences, ) def read_mol(molpath): with open(molpath, "r") as fp: lines = fp.readlines() mol = "" for l in lines: mol += l return mol def molecule( input_pdb, aligned_pdb, lenSeqs, num_res, selectedResidues, allSeqs, sequences ): print("mol updated") print("filenames", input_pdb, aligned_pdb) mol = read_mol(input_pdb) options = "" pred_mol = "[" seqdata = "{" selected = "selected" for i in range(1): # lenSeqs): seqdata += ( str(sequences[i]["Seq"]) + ': { "score": ' + sequences[i]["Score"] + ', "rmsd": ' + sequences[i]["RMSD"] + ', "recovery": ' + sequences[i]["Recovery"] + ', "plddt": ' + sequences[i]["Mean pLDDT"] + ', "seq":"' + allSeqs[i] + '"}' ) pred_mol += f"`{read_mol(aligned_pdb)}`" selected = "" # if i != lenSeqs - 1: # pred_mol += "," # seqdata += "," pred_mol += "]" seqdata += "}" x = ( """
> seq , score , RMSD , Recovery , pLDDT

RMSD AlphaFold vs. native: Å computed using CEAlign on the aligned fragment
AF2 model of redesigned sequence
AlphaFold model confidence:
 Very high (pLDDT > 90)
 Confident (90 > pLDDT > 70)
 Low (70 > pLDDT > 50)
 Very low (pLDDT < 50)
AlphaFold produces a per-residue confidence score (pLDDT) between 0 and 100. Some regions below 50 pLDDT may be unstructured in isolation.
Input structure
 Fixed positions
""" ) return f"""""" def set_examples(example): ( label, inp, designed_chain, fixed_chain, homomer, num_seqs, sampling_temp, atomsel, ) = example return [ label, inp, designed_chain, fixed_chain, homomer, gr.Slider.update(value=num_seqs), gr.Radio.update(value=sampling_temp), atomsel, ] import hashlib def get_esmfold(sequence): headers = { "Content-Type": "application/x-www-form-urlencoded", } sequence = sequence.replace("/", ":") filename = "outputs/" + hashlib.md5(str.encode(sequence)).hexdigest() + ".pdb" if not os.path.exists(filename): response = requests.post( "https://api.esmatlas.com/foldSequence/v1/pdb/", headers=headers, data=sequence, ) name = sequence[:3] + sequence[-3:] pdb_string = response.content.decode("utf-8") with open(filename, "w+") as f: f.write(pdb_string) print("retrieved prediction", filename) else: print("prediction already on disk") return filename proteinMPNN = gr.Blocks() with proteinMPNN: gr.Markdown("# ProteinMPNN + ESMFold") gr.Markdown( """This model takes as input a protein structure and based on its backbone predicts new sequences that will fold into that backbone. It will then run [ESMFold](https://esmatlas.com/about) by MetaAI on the predicted structures and align the predicted structure for the designed sequence with the original backbone. **Note, there is a 400 residue limit in this version and multimeric structures can only be predicted locally. Follow, [README](https://huggingface.co/spaces/simonduerr/ProteinMPNNESM/) for instructions on how to run locally.** """ ) with gr.Tabs(): with gr.TabItem("Input"): inp = gr.Textbox( placeholder="PDB Code or upload file below", label="Input structure" ) file = gr.File(file_count="single") with gr.TabItem("Settings"): with gr.Row(): designed_chain = gr.Textbox(value="A", label="Designed chain") fixed_chain = gr.Textbox( placeholder="Use commas to fix multiple chains", label="Fixed chain" ) with gr.Row(): num_seqs = gr.Slider( minimum=1, maximum=15, value=1, step=1, label="Number of sequences" ) sampling_temp = gr.Radio( choices=[0.1, 0.15, 0.2, 0.25, 0.3], value=0.1, label="Sampling temperature", ) gr.Markdown( """ Sampling temperature for amino acids, `T=0.0` means taking argmax, `T>>1.0` means sample randomly. Suggested values `0.1, 0.15, 0.2, 0.25, 0.3`. Higher values will lead to more diversity. """ ) with gr.Row(): model_name = gr.Dropdown( choices=[ "v_48_002", "v_48_010", "v_48_020", "v_48_030", ], label="Model", value="v_48_020", ) backbone_noise = gr.Dropdown( choices=[0, 0.02, 0.10, 0.20, 0.30], label="Backbone noise", value=0 ) with gr.Row(): homomer = gr.Checkbox(value=False, label="Homomer?") gr.Markdown( "for correct symmetric tying lenghts of homomer chains should be the same" ) gr.Markdown("## Fixed positions") gr.Markdown( """You can fix important positions in the protein. Resid should be specified with the same numbering as in the input pdb file. The fixed residues will be highlighted in the output. The [VMD selection](http://www.ks.uiuc.edu/Research/vmd/vmd-1.9.2/ug/node89.html) synthax is used. You can also select based on ligands or chains in the input structure to specify interfaces to be fixed. - within 5 of resid 94 All residues that have >1 atom closer than 5 Å to any atom of residue 94 - name CA and within 5 of resid 94 All residues that have CA atom closer than 5 Å to any atom of residue 94 - resid 94 96 119 Residues 94, 94 and 119 - within 5 of resname ZN All residues with any atom <5 Å of zinc ion - chain A and within 5 of chain B All residues of chain A that are part of the interface with chain B - protein and within 5 of nucleic All residues that bind to DNA (if present in structure) - not (chain A and within 5 of chain B) only modify residues that are in the interface with the fixed chain, not further away - chain A or (chain B and sasa < 20) Keep chain A and all core residues fixeds - pLDDT >70 Redesign all residues with low pLDDT Note that sasa and pLDDT selectors modify default VMD behavior. SASA is calculated using moleculekit and written to the mass attribute. Selections based on mass do not work. pLDDT is an alias for beta, it only works correctly with structures that contain the appropriate values in the beta column of the PDB file. """ ) atomsel = gr.Textbox( placeholder="Specify atom selection ", label="Fixed positions" ) btn = gr.Button("Run") label = gr.Textbox(label="Label", visible=False) samples = [["Monomer design", "6MRR", "A", "", False, 2, 0.1, ""]] if is_local: samples.extend( [ ["Homomer design", "1O91", "A,B,C", "", True, 2, 0.1, ""], [ "Redesign of Homomer to Heteromer", "3HTN", "A,B", "C", False, 2, 0.1, "", ], [ "Redesign of MID1 scaffold keeping binding site fixed", "3V1C", "A,B", "", False, 2, 0.1, "within 5 of resname ZN", ], [ "Redesign of DNA binding protein", "3JRD", "A,B", "", False, 2, 0.1, "within 8 of nucleic", ], [ "Surface Redesign of miniprotein", "7JZM", "A,B", "", False, 2, 0.1, "chain B or (chain A and sasa < 20)", ], ] ) examples = gr.Dataset( components=[ label, inp, designed_chain, fixed_chain, homomer, num_seqs, sampling_temp, atomsel, ], samples=samples, ) gr.Markdown("# Output") with gr.Tabs(): with gr.TabItem("Designed sequences"): chosen_seq = gr.Dropdown( choices=[], label="Select a sequence for validation", ) mol = gr.HTML() out = gr.Textbox(label="Fasta Output") with gr.TabItem("Amino acid probabilities"): plot = gr.Plot() all_log_probs = gr.File(visible=False) with gr.TabItem("T adjusted probabilities"): gr.Markdown("Sampling temperature adjusted amino acid probabilties") plot_tadjusted = gr.Plot() all_probs = gr.File(visible=False) tempFile = gr.Variable() selectedResidues = gr.Variable() seq_dict = gr.Variable() btn.click( fn=update, inputs=[ inp, file, designed_chain, fixed_chain, homomer, num_seqs, sampling_temp, model_name, backbone_noise, atomsel, ], outputs=[ out, plot, plot_tadjusted, all_log_probs, all_probs, tempFile, chosen_seq, selectedResidues, seq_dict, mol, ], ) chosen_seq.change( updateseq, inputs=[chosen_seq, seq_dict, tempFile, selectedResidues], outputs=mol, ) examples.click(fn=set_examples, inputs=examples, outputs=examples.components) gr.Markdown( """Citation: **Robust deep learning based protein sequence design using ProteinMPNN**
Justas Dauparas, Ivan Anishchenko, Nathaniel Bennett, Hua Bai, Robert J. Ragotte, Lukas F. Milles, Basile I. M. Wicky, Alexis Courbet, Robbert J. de Haas, Neville Bethel, Philip J. Y. Leung, Timothy F. Huddy, Sam Pellock, Doug Tischer, Frederick Chan, Brian Koepnick, Hannah Nguyen, Alex Kang, Banumathi Sankaran, Asim Bera, Neil P. King, David Baker
Science Vol 378, Issue 6615, pp. 49 -56; doi: [10.1126/science.add2187](https://doi.org/10.1126/science.add2187

Server built by [@simonduerr](https://twitter.com/simonduerr) and hosted by Huggingface""" ) proteinMPNN.launch()