import json, time, os, sys, glob import urllib import shutil import warnings import copy import random import re import os.path import torch import ray import jax import gradio as gr import pandas as pd import numpy as np import plotly.express as px import jax.numpy as jnp import tensorflow as tf import matplotlib.pyplot as plt import colabfold as cf import plotly.graph_objects as go import torch.nn as nn import torch.nn.functional as F if "/home/user/app/af_backprop" not in sys.path: sys.path.append("/home/user/app/af_backprop") # local only if "/home/duerr/phd/08_Code/ProteinMPNN/af_backprop" not in sys.path: sys.path.append("/home/duerr/phd/08_Code/ProteinMPNN/af_backprop") from torch import optim from torch.utils.data import DataLoader from torch.utils.data.dataset import random_split, Subset from moleculekit.molecule import Molecule from alphafold.common import protein from alphafold.data import pipeline from alphafold.model import data, config from alphafold.model import model as afmodel from alphafold.common import residue_constants from utils import * sys.path.append("/home/user/app/ProteinMPNN/vanilla_proteinmpnn") sys.path.append("/home/duerr/phd/08_Code/ProteinMPNN/ProteinMPNN/vanilla_proteinmpnn") # tf.config.set_visible_devices([], "GPU") def chain_break(idx_res, Ls, length=200): # Minkyung's code # add big enough number to residue index to indicate chain breaks L_prev = 0 for L_i in Ls[:-1]: idx_res[L_prev + L_i :] += length L_prev += L_i return idx_res def clear_mem(): backend = jax.lib.xla_bridge.get_backend() for buf in backend.live_buffers(): buf.delete() print("Is cuda available", torch.cuda.is_available()) # stream = os.popen("nvcc --version") # output = stream.read() # print(output) def setup_af(seq, model_name="model_5_ptm"): clear_mem() # setup model cfg = config.model_config("model_5_ptm") cfg.model.num_recycle = 0 cfg.data.common.num_recycle = 0 cfg.data.eval.max_msa_clusters = 1 cfg.data.common.max_extra_msa = 1 cfg.data.eval.masked_msa_replace_fraction = 0 cfg.model.global_config.subbatch_size = None if os.path.exists("/home/duerr"): datadir = "/home/duerr/phd/08_Code/ProteinMPNN" else: datadir = "/home/user/app/" model_params = data.get_model_haiku_params(model_name=model_name, data_dir=datadir) model_runner = afmodel.RunModel(cfg, model_params, is_training=False) Ls = [len(s) for s in seq.split("/")] seq = re.sub("[^A-Z]", "", seq.upper()) length = len(seq) feature_dict = { **pipeline.make_sequence_features( sequence=seq, description="none", num_res=length ), **pipeline.make_msa_features(msas=[[seq]], deletion_matrices=[[[0] * length]]), } feature_dict["residue_index"] = chain_break(feature_dict["residue_index"], Ls) inputs = model_runner.process_features(feature_dict, random_seed=0) def runner(seq, opt): # update sequence inputs = opt["inputs"] inputs.update(opt["prev"]) update_seq(seq, inputs) update_aatype(inputs["target_feat"][..., 1:], inputs) # mask prediction mask = seq.sum(-1) inputs["seq_mask"] = inputs["seq_mask"].at[:].set(mask) inputs["msa_mask"] = inputs["msa_mask"].at[:].set(mask) inputs["residue_index"] = jnp.where(mask == 1, inputs["residue_index"], 0) # get prediction key = jax.random.PRNGKey(0) outputs = model_runner.apply(opt["params"], key, inputs) prev = { "init_msa_first_row": outputs["representations"]["msa_first_row"][None], "init_pair": outputs["representations"]["pair"][None], "init_pos": outputs["structure_module"]["final_atom_positions"][None], } aux = { "final_atom_positions": outputs["structure_module"]["final_atom_positions"], "final_atom_mask": outputs["structure_module"]["final_atom_mask"], "plddt": get_plddt(outputs), "pae": get_pae(outputs), "inputs": inputs, "prev": prev, } return aux return jax.jit(runner), {"inputs": inputs, "params": model_params} 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, lenRes, 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) aligner = Bio.PDB.CEAligner() aligner.set_reference(ref_structure) aligner.align(sample_structure) io = Bio.PDB.PDBIO() io.set_structure(ref_structure) io.save(f"outputs/reference.pdb") io.set_structure(sample_structure) io.save(f"outputs/out_{index}_aligned.pdb") # Doing this to get around biopython CEALIGN bug # subprocess.call("pymol -c -Q -r cealign.pml", shell=True) return aligner.rms, "outputs/reference.pdb", f"outputs/out_{index}_aligned.pdb" def save_pdb(outs, filename, LEN): """save pdb coordinates""" p = { "residue_index": outs["inputs"]["residue_index"][0][:LEN], "aatype": outs["inputs"]["aatype"].argmax(-1)[0][:LEN], "atom_positions": outs["final_atom_positions"][:LEN], "atom_mask": outs["final_atom_mask"][:LEN], } b_factors = 100.0 * outs["plddt"][:LEN, None] * p["atom_mask"] p = protein.Protein(**p, b_factors=b_factors) pdb_lines = protein.to_pdb(p) with open(filename, "w") as f: f.write(pdb_lines) @ray.remote(num_gpus=1, max_calls=1) def run_alphafold(sequences, num_recycles): recycles = num_recycles RUNNER, OPT = setup_af(sequences[0]) plddts = [] paes = [] for i, sequence in enumerate(sequences): SEQ = re.sub("[^A-Z]", "", sequence.upper()) MAX_LEN = len(SEQ) LEN = len(SEQ) x = np.array([residue_constants.restype_order.get(aa, -1) for aa in SEQ]) x = np.pad(x, [0, MAX_LEN - LEN], constant_values=-1) x = jax.nn.one_hot(x, 20) OPT["prev"] = { "init_msa_first_row": np.zeros([1, MAX_LEN, 256]), "init_pair": np.zeros([1, MAX_LEN, MAX_LEN, 128]), "init_pos": np.zeros([1, MAX_LEN, 37, 3]), } positions = [] for r in range(recycles + 1): outs = RUNNER(x, OPT) outs = jax.tree_map(lambda x: np.asarray(x), outs) positions.append(outs["prev"]["init_pos"][0, :LEN]) OPT["prev"] = outs["prev"] plddts.append(outs["plddt"][:LEN]) paes.append(outs["pae"]) if os.path.exists("/home/duerr/phd/08_Code/ProteinMPNN"): save_pdb( outs, f"/home/duerr/phd/08_Code/ProteinMPNN/outputs/out_{i}.pdb", LEN ) else: save_pdb(outs, f"/home/user/app/outputs/out_{i}.pdb", LEN) return plddts, paes, LEN def setup_proteinmpnn(model_name="vanilla—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, model_name = model_name.split("—") if os.path.exists("/home/duerr"): dir = "/home/duerr/phd/08_Code/ProteinMPNN" else: dir = "/home/user/app" path_to_model_weights = ( f"{dir}/ProteinMPNN/{model}_model_weights" ) 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, omit_AAs, 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 if omit_AAs == "": 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} 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), selected_residues, seq_dict, ) def update_AF(seq_dict, pdb, num_recycles, selectedResidues): # # run alphafold using ray # plddts, pae, num_res = run_alphafold( # startsequence, num_recycles # ) allSeqs = seq_dict["seq_list"] lenSeqs = len(allSeqs) if len(allSeqs[0]) > 700: return ( """ """, plt.figure(), plt.figure(), ) plddts, paes, num_res = ray.get(run_alphafold.remote(allSeqs, num_recycles)) sequences = {} for i in range(lenSeqs): rms, input_pdb, aligned_pdb = align_structures( pdb, f"outputs/out_{i}.pdb", num_res, i ) sequences[i] = { "Seq": i, "RMSD": f"{rms:.2f}", "Score": seq_dict["seq_score"][i], "Recovery": seq_dict["recovery"][i], "Mean pLDDT": f"{np.mean(plddts[i]):.4f}", } results = pd.DataFrame.from_dict(sequences, orient="index") print(results) plots = [] for index, plddts_val in enumerate(plddts): # if recycle == 0 or recycle == len(plddts) - 1: # visible = True # else: # visible = "legendonly" visible = True plots.append( go.Scatter( x=np.arange(len(plddts_val)), y=plddts_val, hovertemplate="pLDDT: %{y:.2f}
Residue index: %{x}
Sequence " + str(index), name=f"seq {index}", visible=visible, ) ) plotAF_plddt = go.Figure(data=plots) plotAF_plddt.update_layout( title="pLDDT", xaxis_title="Residue index", yaxis_title="pLDDT", height=500, template="simple_white", legend=dict(yanchor="bottom", y=0.01, xanchor="left", x=0.99), ) pae_plots = [] for i, pae in enumerate(paes): plt.figure() plt.title(f"Predicted Aligned Error sequence {i}") Ln = pae.shape[0] plt.imshow(pae, cmap="bwr", vmin=0, vmax=30, extent=(0, Ln, Ln, 0)) plt.colorbar() plt.xlabel("Scored residue") plt.ylabel("Aligned residue") plt.savefig(f"outputs/pae_plot_{i}.png", dpi=300) plt.close() pae_plots.append(f"outputs/pae_plot_{i}.png") # doesnt work (likely because too large) # plotAF_pae = px.imshow( # pae, # labels=dict(x="Scored residue", y="Aligned residue", color=""), # template="simple_white", # y=np.arange(len(plddts_val)), # ) # plotAF_pae.write_html("test.html") # plotAF_pae.update_layout(title="Predicted Aligned Error", template="simple_white") return ( molecule( input_pdb, aligned_pdb, lenSeqs, num_res, selectedResidues, allSeqs, sequences, ), plotAF_plddt, pae_plots, results, ) 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 ): mol = read_mol("outputs/reference.pdb") options = "" pred_mol = "[" seqdata = "{" selected = "selected" for i in range(lenSeqs): seqdata += ( str(i) + ': { "score": ' + sequences[i]["Score"] + ', "rmsd": ' + sequences[i]["RMSD"] + ', "recovery": ' + sequences[i]["Recovery"] + ', "plddt": ' + sequences[i]["Mean pLDDT"] + ', "seq":"' + allSeqs[i] + '"}' ) options += f'' # RMSD {sequences[i]["RMSD"]}, score {sequences[i]["Score"]}, recovery {sequences[i]["Recovery"]} pLDDT {sequences[i]["Mean pLDDT"]} p = f"outputs/out_{i}_aligned.pdb" pred_mol += f"`{read_mol(p)}`" 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, ] proteinMPNN = gr.Blocks() with proteinMPNN: gr.Markdown("# ProteinMPNN") gr.Markdown( """This model takes as input a protein structure and based on its backbone predicts new sequences that will fold into that backbone. Optionally, we can run AlphaFold2 on the predicted sequence to check whether the predicted sequences adopt the same backbone. If you use this space please cite the ProteinMPNN paper > J. Dauparas, I. Anishchenko, N. Bennett, H. Bai, R. J. Ragotte, L. F. Milles, B. I. M. Wicky, A. Courbet, R. J. de Haas, N. Bethel, P. J. Y. Leung, T. F. Huddy, S. Pellock, D. Tischer, F. Chan, B. Koepnick, H. Nguyen, A. Kang, B. Sankaran, A. K. Bera, N. P. King, D. Baker, Robust deep learning–based protein sequence design using ProteinMPNN. Science 378, 49–56 (2022). and this webapp: > Simon L. Dürr. (2023). ProteinMPNN Gradio Webapp (v0.3). Zenodo. https://doi.org/10.5281/zenodo.7630417 """ ) gr.Markdown("![](https://simonduerr.eu/ProteinMPNN.png)") 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=[ "vanilla—v_48_002", "vanilla—v_48_010", "vanilla—v_48_020", "vanilla—v_48_030", "soluble—v_48_010", "soluble—v_48_020", ], label="Model", value="vanilla—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" ) with gr.Row(): omit_AAs = gr.Textbox( placeholder="Specify omitted amino acids ", label="Omitted amino acids" ) 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) examples = gr.Dataset( components=[ label, inp, designed_chain, fixed_chain, homomer, num_seqs, sampling_temp, atomsel, ], samples=[ ["Homomer design", "1O91", "A,B,C", "", True, 2, 0.1, ""], ["Monomer design", "6MRR", "A", "", False, 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)", ], ], ) gr.Markdown("# Output") with gr.Tabs(): with gr.TabItem("Designed sequences"): out = gr.Textbox(label="Status") 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) with gr.TabItem("Structure validation w/ AF2"): gr.HTML( """

AF2 code is experimental and relies on @sokrypton's trick to speed up compile/module runtime. Results might differ from DeepMind's published results. Predictions are made using model_5_ptm and without MSA based on the selected single sequence (designed_chain + fixed_chain).

""" ) with gr.Row(): with gr.Row(): chosen_seq = gr.Dropdown( choices=[], label="Select a sequence for validation", visible=False, ) num_recycles = gr.Dropdown( choices=[0, 1, 3, 5], value=3, label="num Recycles" ) btnAF = gr.Button("Run AlphaFold on all sequences") with gr.Row(): mol = gr.HTML() with gr.Column(): gr.Markdown("## Metrics") p = { 0: { "Seq": "NA", "RMSD": "NA", "Score": "NA", "Recovery": "NA", "Mean pLDDT": "NA", } } placeholder = pd.DataFrame.from_dict(p, orient="index") results = gr.Dataframe( placeholder, interactive=False, row_count=(1, "dynamic"), headers=["Seq", "RMSD", "Score", "Recovery", "Mean pLDDT"], ) plotAF_plddt = gr.Plot(label="pLDDT") # remove maxh80 class from css plotAF_pae = gr.Gallery(label="PAE plots") # gr.Plot(label="PAE") 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, omit_AAs, atomsel, ], outputs=[ out, plot, plot_tadjusted, all_log_probs, all_probs, tempFile, chosen_seq, selectedResidues, seq_dict, ], ) btnAF.click( fn=update_AF, inputs=[seq_dict, tempFile, num_recycles, selectedResidues], outputs=[mol, plotAF_plddt, plotAF_pae, results], ) 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
bioRxiv 2022.06.03.494563; doi: [10.1101/2022.06.03.494563](https://doi.org/10.1101/2022.06.03.494563)

Server built by [@simonduerr](https://twitter.com/simonduerr) and hosted by Huggingface""" ) ray.init(runtime_env={"working_dir": "./af_backprop"}) proteinMPNN.launch()