initial commit

.gitattributes

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+*.pt filter=lfs diff=lfs merge=lfs -text

LICENSE

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+MIT License
+
+Copyright (c) 2022 Justas Dauparas,Sergey Ovichinnikov, Simon Duerr
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

ProteinMPNN/.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/

ProteinMPNN/LICENSE

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+MIT License
+
+Copyright (c) 2022 Justas Dauparas
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

ProteinMPNN/README.md

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+# ProteinMPNN + ESM
+
+You can run this repo locally with the following easy steps. 
+
+In a new python environment, do:
+
+```
+pip install -r requirements.txt
+
+pip install --upgrade transformers accelerate
+
+python app.py
+```
+
+This will launch a local webserver and a share URL (`xxx.gradio.app`, helpful if running this on a cluster), open the url in your webbrowser and start designing proteins. 
+
+

ProteinMPNN/colab_notebooks/README.md

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+<a href="https://colab.research.google.com/github/dauparas/ProteinMPNN/blob/main/colab_notebooks/quickdemo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>

ProteinMPNN/colab_notebooks/quickdemo.ipynb

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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "quickdemo.ipynb",
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/dauparas/ProteinMPNN/blob/main/colab_notebooks/quickdemo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#ProteinMPNN\n",
+        "This notebook is intended as a quick demo, more features to come!"
+      ],
+      "metadata": {
+        "id": "AYZebfKn8gef"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@title Setup Model\n",
+        "import json, time, os, sys, glob\n",
+        "\n",
+        "if not os.path.isdir(\"ProteinMPNN\"):\n",
+        "  os.system(\"git clone -q https://github.com/dauparas/ProteinMPNN.git\")\n",
+        "sys.path.append('/content/ProteinMPNN/vanilla_proteinmpnn')\n",
+        "\n",
+        "import matplotlib.pyplot as plt\n",
+        "import shutil\n",
+        "import warnings\n",
+        "import numpy as np\n",
+        "import torch\n",
+        "from torch import optim\n",
+        "from torch.utils.data import DataLoader\n",
+        "from torch.utils.data.dataset import random_split, Subset\n",
+        "import copy\n",
+        "import torch.nn as nn\n",
+        "import torch.nn.functional as F\n",
+        "import random\n",
+        "import os.path\n",
+        "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\n",
+        "from protein_mpnn_utils import StructureDataset, StructureDatasetPDB, ProteinMPNN\n",
+        "\n",
+        "device = torch.device(\"cuda:0\" if (torch.cuda.is_available()) else \"cpu\")\n",
+        "model_name=\"v_48_020\"             # 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\n",
+        "backbone_noise=0.00               # Standard deviation of Gaussian noise to add to backbone atoms\n",
+        "\n",
+        "path_to_model_weights='/content/ProteinMPNN/vanilla_proteinmpnn/vanilla_model_weights'          \n",
+        "hidden_dim = 128\n",
+        "num_layers = 3 \n",
+        "model_folder_path = path_to_model_weights\n",
+        "if model_folder_path[-1] != '/':\n",
+        "    model_folder_path = model_folder_path + '/'\n",
+        "checkpoint_path = model_folder_path + f'{model_name}.pt'\n",
+        "\n",
+        "checkpoint = torch.load(checkpoint_path, map_location=device) \n",
+        "print('Number of edges:', checkpoint['num_edges'])\n",
+        "noise_level_print = checkpoint['noise_level']\n",
+        "print(f'Training noise level: {noise_level_print}A')\n",
+        "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'])\n",
+        "model.to(device)\n",
+        "model.load_state_dict(checkpoint['model_state_dict'])\n",
+        "model.eval()\n",
+        "print(\"Model loaded\")"
+      ],
+      "metadata": {
+        "id": "iYDU3ftml2k5",
+        "cellView": "form"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import re\n",
+        "from google.colab import files\n",
+        "import numpy as np\n",
+        "\n",
+        "#########################\n",
+        "def get_pdb(pdb_code=\"\"):\n",
+        "  if pdb_code is None or pdb_code == \"\":\n",
+        "    upload_dict = files.upload()\n",
+        "    pdb_string = upload_dict[list(upload_dict.keys())[0]]\n",
+        "    with open(\"tmp.pdb\",\"wb\") as out: out.write(pdb_string)\n",
+        "    return \"tmp.pdb\"\n",
+        "  else:\n",
+        "    os.system(f\"wget -qnc https://files.rcsb.org/view/{pdb_code}.pdb\")\n",
+        "    return f\"{pdb_code}.pdb\"\n",
+        "\n",
+        "#@markdown ### Input Options\n",
+        "pdb='6MRR' #@param {type:\"string\"}\n",
+        "pdb_path = get_pdb(pdb)\n",
+        "#@markdown - pdb code (leave blank to get an upload prompt)\n",
+        "\n",
+        "designed_chain = \"A\" #@param {type:\"string\"}\n",
+        "fixed_chain = \"\" #@param {type:\"string\"}\n",
+        "\n",
+        "if designed_chain == \"\":\n",
+        "  designed_chain_list = []\n",
+        "else:\n",
+        "  designed_chain_list = re.sub(\"[^A-Za-z]+\",\",\", designed_chain).split(\",\")\n",
+        "\n",
+        "if fixed_chain == \"\":\n",
+        "  fixed_chain_list = []\n",
+        "else:\n",
+        "  fixed_chain_list = re.sub(\"[^A-Za-z]+\",\",\", fixed_chain).split(\",\")\n",
+        "\n",
+        "chain_list = list(set(designed_chain_list + fixed_chain_list))\n",
+        "\n",
+        "#@markdown - specified which chain(s) to design and which chain(s) to keep fixed. \n",
+        "#@markdown   Use comma:`A,B` to specifiy more than one chain\n",
+        "\n",
+        "#chain = \"A\" #@param {type:\"string\"}\n",
+        "#pdb_path_chains = chain\n",
+        "##@markdown - Define which chain to redesign\n",
+        "\n",
+        "#@markdown ### Design Options\n",
+        "num_seqs = 1 #@param [\"1\", \"2\", \"4\", \"8\", \"16\", \"32\", \"64\"] {type:\"raw\"}\n",
+        "num_seq_per_target = num_seqs\n",
+        "sampling_temp = \"0.1\" #@param [\"0.1\", \"0.15\", \"0.2\", \"0.25\", \"0.3\"]\n",
+        "#@markdown - Sampling temperature for amino acids, T=0.0 means taking \n",
+        "#@markdown   argmax, T>>1.0 means sample randomly. Suggested values \n",
+        "#@markdown   0.1, 0.15, 0.2, 0.25, 0.3. Higher values will lead to more diversity.\n",
+        "\n",
+        "\n",
+        "save_score=0                      # 0 for False, 1 for True; save score=-log_prob to npy files\n",
+        "save_probs=0                      # 0 for False, 1 for True; save MPNN predicted probabilites per position\n",
+        "score_only=0                      # 0 for False, 1 for True; score input backbone-sequence pairs\n",
+        "conditional_probs_only=0          # 0 for False, 1 for True; output conditional probabilities p(s_i given the rest of the sequence and backbone)\n",
+        "conditional_probs_only_backbone=0 # 0 for False, 1 for True; if true output conditional probabilities p(s_i given backbone)\n",
+        "    \n",
+        "batch_size=1                      # Batch size; can set higher for titan, quadro GPUs, reduce this if running out of GPU memory\n",
+        "max_length=20000                  # Max sequence length\n",
+        "    \n",
+        "out_folder='.'                    # Path to a folder to output sequences, e.g. /home/out/\n",
+        "jsonl_path=''                     # Path to a folder with parsed pdb into jsonl\n",
+        "omit_AAs='X'                      # Specify which amino acids should be omitted in the generated sequence, e.g. 'AC' would omit alanine and cystine.\n",
+        "   \n",
+        "pssm_multi=0.0                    # A value between [0.0, 1.0], 0.0 means do not use pssm, 1.0 ignore MPNN predictions\n",
+        "pssm_threshold=0.0                # A value between -inf + inf to restric per position AAs\n",
+        "pssm_log_odds_flag=0               # 0 for False, 1 for True\n",
+        "pssm_bias_flag=0                   # 0 for False, 1 for True\n",
+        "\n",
+        "\n",
+        "##############################################################\n",
+        "\n",
+        "folder_for_outputs = out_folder\n",
+        "\n",
+        "NUM_BATCHES = num_seq_per_target//batch_size\n",
+        "BATCH_COPIES = batch_size\n",
+        "temperatures = [float(item) for item in sampling_temp.split()]\n",
+        "omit_AAs_list = omit_AAs\n",
+        "alphabet = 'ACDEFGHIKLMNPQRSTVWYX'\n",
+        "\n",
+        "omit_AAs_np = np.array([AA in omit_AAs_list for AA in alphabet]).astype(np.float32)\n",
+        "\n",
+        "chain_id_dict = None\n",
+        "fixed_positions_dict = None\n",
+        "pssm_dict = None\n",
+        "omit_AA_dict = None\n",
+        "bias_AA_dict = None\n",
+        "tied_positions_dict = None\n",
+        "bias_by_res_dict = None\n",
+        "bias_AAs_np = np.zeros(len(alphabet))\n",
+        "\n",
+        "\n",
+        "###############################################################\n",
+        "pdb_dict_list = parse_PDB(pdb_path, input_chain_list=chain_list)\n",
+        "dataset_valid = StructureDatasetPDB(pdb_dict_list, truncate=None, max_length=max_length)\n",
+        "\n",
+        "chain_id_dict = {}\n",
+        "chain_id_dict[pdb_dict_list[0]['name']]= (designed_chain_list, fixed_chain_list)\n",
+        "\n",
+        "print(chain_id_dict)"
+      ],
+      "metadata": {
+        "cellView": "form",
+        "id": "k4o6w2Y23wxO"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@title RUN\n",
+        "with torch.no_grad():\n",
+        "  print('Generating sequences...')\n",
+        "  for ix, protein in enumerate(dataset_valid):\n",
+        "    score_list = []\n",
+        "    all_probs_list = []\n",
+        "    all_log_probs_list = []\n",
+        "    S_sample_list = []\n",
+        "    batch_clones = [copy.deepcopy(protein) for i in range(BATCH_COPIES)]\n",
+        "    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)\n",
+        "    pssm_log_odds_mask = (pssm_log_odds_all > pssm_threshold).float() #1.0 for true, 0.0 for false\n",
+        "    name_ = batch_clones[0]['name']\n",
+        "\n",
+        "    randn_1 = torch.randn(chain_M.shape, device=X.device)\n",
+        "    log_probs = model(X, S, mask, chain_M*chain_M_pos, residue_idx, chain_encoding_all, randn_1)\n",
+        "    mask_for_loss = mask*chain_M*chain_M_pos\n",
+        "    scores = _scores(S, log_probs, mask_for_loss)\n",
+        "    native_score = scores.cpu().data.numpy()\n",
+        "\n",
+        "    for temp in temperatures:\n",
+        "        for j in range(NUM_BATCHES):\n",
+        "            randn_2 = torch.randn(chain_M.shape, device=X.device)\n",
+        "            if tied_positions_dict == None:\n",
+        "                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)\n",
+        "                S_sample = sample_dict[\"S\"] \n",
+        "            else:\n",
+        "                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)\n",
+        "            # Compute scores\n",
+        "                S_sample = sample_dict[\"S\"]\n",
+        "            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\"])\n",
+        "            mask_for_loss = mask*chain_M*chain_M_pos\n",
+        "            scores = _scores(S_sample, log_probs, mask_for_loss)\n",
+        "            scores = scores.cpu().data.numpy()\n",
+        "            all_probs_list.append(sample_dict[\"probs\"].cpu().data.numpy())\n",
+        "            all_log_probs_list.append(log_probs.cpu().data.numpy())\n",
+        "            S_sample_list.append(S_sample.cpu().data.numpy())\n",
+        "            for b_ix in range(BATCH_COPIES):\n",
+        "                masked_chain_length_list = masked_chain_length_list_list[b_ix]\n",
+        "                masked_list = masked_list_list[b_ix]\n",
+        "                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])\n",
+        "                seq = _S_to_seq(S_sample[b_ix], chain_M[b_ix])\n",
+        "                score = scores[b_ix]\n",
+        "                score_list.append(score)\n",
+        "                native_seq = _S_to_seq(S[b_ix], chain_M[b_ix])\n",
+        "                if b_ix == 0 and j==0 and temp==temperatures[0]:\n",
+        "                    start = 0\n",
+        "                    end = 0\n",
+        "                    list_of_AAs = []\n",
+        "                    for mask_l in masked_chain_length_list:\n",
+        "                        end += mask_l\n",
+        "                        list_of_AAs.append(native_seq[start:end])\n",
+        "                        start = end\n",
+        "                    native_seq = \"\".join(list(np.array(list_of_AAs)[np.argsort(masked_list)]))\n",
+        "                    l0 = 0\n",
+        "                    for mc_length in list(np.array(masked_chain_length_list)[np.argsort(masked_list)])[:-1]:\n",
+        "                        l0 += mc_length\n",
+        "                        native_seq = native_seq[:l0] + '/' + native_seq[l0:]\n",
+        "                        l0 += 1\n",
+        "                    sorted_masked_chain_letters = np.argsort(masked_list_list[0])\n",
+        "                    print_masked_chains = [masked_list_list[0][i] for i in sorted_masked_chain_letters]\n",
+        "                    sorted_visible_chain_letters = np.argsort(visible_list_list[0])\n",
+        "                    print_visible_chains = [visible_list_list[0][i] for i in sorted_visible_chain_letters]\n",
+        "                    native_score_print = np.format_float_positional(np.float32(native_score.mean()), unique=False, precision=4)\n",
+        "                    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)\n",
+        "                    print(line.rstrip())\n",
+        "                start = 0\n",
+        "                end = 0\n",
+        "                list_of_AAs = []\n",
+        "                for mask_l in masked_chain_length_list:\n",
+        "                    end += mask_l\n",
+        "                    list_of_AAs.append(seq[start:end])\n",
+        "                    start = end\n",
+        "\n",
+        "                seq = \"\".join(list(np.array(list_of_AAs)[np.argsort(masked_list)]))\n",
+        "                l0 = 0\n",
+        "                for mc_length in list(np.array(masked_chain_length_list)[np.argsort(masked_list)])[:-1]:\n",
+        "                    l0 += mc_length\n",
+        "                    seq = seq[:l0] + '/' + seq[l0:]\n",
+        "                    l0 += 1\n",
+        "                score_print = np.format_float_positional(np.float32(score), unique=False, precision=4)\n",
+        "                seq_rec_print = np.format_float_positional(np.float32(seq_recovery_rate.detach().cpu().numpy()), unique=False, precision=4)\n",
+        "                line = '>T={}, sample={}, score={}, seq_recovery={}\\n{}\\n'.format(temp,b_ix,score_print,seq_rec_print,seq)\n",
+        "                print(line.rstrip())\n",
+        "\n",
+        "\n",
+        "all_probs_concat = np.concatenate(all_probs_list)\n",
+        "all_log_probs_concat = np.concatenate(all_log_probs_list)\n",
+        "S_sample_concat = np.concatenate(S_sample_list)"
+      ],
+      "metadata": {
+        "id": "xMVlYh8Fv2of",
+        "cellView": "form"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# experimental output\n",
+        "plt.figure(figsize=(20,5), dpi=100)\n",
+        "plt.imshow(all_probs_concat.mean(0).T,vmin=0,vmax=1)\n",
+        "plt.xlabel(\"positions\")\n",
+        "plt.ylabel(\"amino acids\")\n",
+        "plt.yticks(range(21),list(alphabet))\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "4jSKLU3L17Sf"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_2_outputs/assigned_pdbs.jsonl

@@ -0,0 +1 @@
+{"4YOW": [["A", "B"], ["C", "D", "E", "F"]], "3HTN": [["A", "B"], ["C"]]}

ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_2_outputs/fixed_pdbs.jsonl

@@ -0,0 +1 @@
+{"4YOW": {"A": [1, 2, 3, 4, 5, 6, 7, 8, 23, 25], "C": [10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 40], "B": [], "D": [], "E": [], "F": []}, "3HTN": {"A": [1, 2, 3, 4, 5, 6, 7, 8, 23, 25], "C": [10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 40], "B": []}}

ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_2_outputs/seqs/3HTN.fa

@@ -0,0 +1,6 @@
+>3HTN, score=1.1538, fixed_chains=['C'], designed_chains=['A', 'B'], model_name=v_48_020
+NMYSYKKIGNKYIVSINNHTEIVKALNAFCKEKGILSGSINGIGAIGELTLRFFNPKTKAYDDKTFREQMEISNLTGNISSMNEQVYLHLHITVGRSDYSALAGHLLSAIQNGAGEFVVEDYSERISRTYNPDLGLNIYDFER/NMYSYKKIGNKYIVSINNHTEIVKALNAFCKEKGILSGSINGIGAIGELTLRFFNPKXXXXDDKTFREQMEISNLTGNISSMNEQVYLHLHITVGRSDYSALAGHLLSAIQNGAGEFVVEDYSERISRTYNPDLGLNIYDFER
+>T=0.1, sample=0, score=0.7465, seq_recovery=0.5426
+KMYEYKKIGNDYIVSIKNNTDLVTAIKEFCKEKKIKSGTINGIGQVKQVTLRFYNFETKEYEEKTFNENLDISNLTGIISTHNNEIFLHLHGTFGKENFSALAGHLISAIVNGKGILKVEDFKEEISTKYDEKLGLYLLDFNK/SMYKYKKIGNDYIVKINNGKNLVESLLEFVKDKNIKSGTINGTGSVSKVTLEFFDPEXXXXKTKTFNDNFDISNLTGFISTKDGKPLIDLHITIGDSDFSALAGHLIDAIVNGEANIKVEDYNVEINVRYDEELGLWLLDFNL
+>T=0.1, sample=0, score=0.7500, seq_recovery=0.5851
+NMYTYEKIGNKYIVSINNNTELITAIKNFCKEKKIKSGTINGIGQVSSVTLRFYNYETKTYENKTFNAQFTISNLTGIISTYNNEIFLHLHITIGDSNFSALAGHLLSAVVNGTCILIVEDFKEKISRKYNEELGLYLLDFNK/NMYKYKKIGNKYIVSINNGKELYEALLDFVKDKNIKSGSVNGTGMISKLTLSFFDPNXXXXTTKTFNMNMDISNLTGFISTKNGEPLLDLHVTVGDSDFSALAGHLVSAVVNGEADVIIENFNKEINVKYNEELGLWLLDFNL

ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_2_outputs/seqs/4YOW.fa

@@ -0,0 +1,6 @@
+>4YOW, score=1.1024, fixed_chains=['C', 'D', 'E', 'F'], designed_chains=['A', 'B'], model_name=v_48_020
+MRIVAADTGGAVLDESFQPVGLIATVAVLVEKPYKTSKRFLVKYADPYNYDLSGRQAIRDEIELAIELAREVSPDVIHLNSTLGGIEVRKLDESTIDALQISDRGKEIWKELSKDLQPLAKKFWEETGIEIIAIGKSSVPVRIAEIYAGIFSVKWALDNVKEKGGLLVGLPRYMEVEIKKDKIIGKSLDPREGGLYGEVKTEVPQGIKWELYPNPLVRRFMVFEITS/XXXX
+>T=0.1, sample=0, score=0.7444, seq_recovery=0.5903
+MKIVAADTGGALADENYNPIGKIATVAVLVTKPYRTSDTFLVEYLDPTKYDLSSHEGIKRELELAIELAEQVKPDLIHLDINLGGVPVAELNPEVIDKLQISEETKKILKELAKTLTPLAQAYLAKTGIPILATGDDSVPVHIAHIYASGAAVKWALENVKELKGLRVLLEEATSVEIKEDSIVVRSLDPRDGGLYGEIKTEIPEGITTELYPNPLRSNHMIFEVKT/XXXX
+>T=0.1, sample=0, score=0.7336, seq_recovery=0.5551
+MKIVAADTGGYLLDENYRPIGRIATVAVLVEKPYRTSDVFLVEYLDPTNYDLSSHEGILREFRLAVELAERVKPDLIHLDIDLGGVPVAELTPEVIEALQISEETKATLKELAKTLTPAAQAFLARTGIPVLAMGSRSVPVRIADIAASVAAVKYALENVKKRKGLRVGLEEAVSVEIEEKSIVGRSLDPRDGGLYFRIETEIPEGVTYELYPNPLRTNHLVFEVKV/XXXX

ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_3_outputs/seqs/3HTN.fa

@@ -0,0 +1,6 @@
+>3HTN, score=1.1501, fixed_chains=['C'], designed_chains=['A', 'B'], model_name=v_48_020
+NMYSYKKIGNKYIVSINNHTEIVKALNAFCKEKGILSGSINGIGAIGELTLRFFNPKTKAYDDKTFREQMEISNLTGNISSMNEQVYLHLHITVGRSDYSALAGHLLSAIQNGAGEFVVEDYSERISRTYNPDLGLNIYDFER/NMYSYKKIGNKYIVSINNHTEIVKALNAFCKEKGILSGSINGIGAIGELTLRFFNPKXXXXDDKTFREQMEISNLTGNISSMNEQVYLHLHITVGRSDYSALAGHLLSAIQNGAGEFVVEDYSERISRTYNPDLGLNIYDFER
+>T=0.1, sample=0, score=0.7254, seq_recovery=0.5355
+HMYEYKEIGNKYIVSINNNTDIVEAIKKFCEEKNIKSGTINGIGQVKSVTLRFYNFETKESKEVTINDNLTISNLTGIISTYNNEIFLDLHITIGDSNFSALAGHLLSAIVNGDCILIIEDYKENISKKYDKELGLWLLDFNK/KMYSYKKIGNKYIVNINNGKDLVTSILKFCEDKKIKSGTINGTGMISKLTLEFFDPEXXXXTTKTFNDILDISNLTGFISTKDGKVFVKLYITAGKKDFSALAGKLVSAIVNGEMNLTIEDFNVEINVEYNEELGLYLLNFNK
+>T=0.1, sample=0, score=0.7494, seq_recovery=0.5745
+SMYEYKKIGNSYIVSVNNNTELVEALTAFCTEKGIKSGTVNGIGQVKSVTLRFYNFETKEYKEKTFEENLEISNLTGIISTYNNKVFLDIHGTFGKSDFSALAGHLVSAIVNGKAILKVEDYKEEISRTYNEETGLWLLDFNK/KMYKYKKIGNDYIVSIKNGKNLVEAIKKFCEDKNIKSGSVNGTGQISKVTLRFFDPEXXXXTTKTFNENMDISNLTGFISTKNGEVLVDLHITVGKSNFSALAGHLVDAIVNGEADLKIEDYNVEINVEYDEKTGLWLLNFNK

ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_4_outputs/assigned_pdbs.jsonl

@@ -0,0 +1 @@
+{"4YOW": [["A", "C"], ["B", "D", "E", "F"]], "3HTN": [["A", "C"], ["B"]]}

ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_4_outputs/fixed_pdbs.jsonl

@@ -0,0 +1 @@
+{"4YOW": {"A": [1, 2, 3, 4, 5, 6, 7, 8, 23, 25], "C": [10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 40], "B": [], "D": [], "E": [], "F": []}, "3HTN": {"A": [1, 2, 3, 4, 5, 6, 7, 8, 23, 25], "C": [10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 40], "B": []}}

ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_4_outputs/seqs/3HTN.fa

@@ -0,0 +1,6 @@
+>3HTN, score=1.1682, fixed_chains=['B'], designed_chains=['A', 'C'], model_name=v_48_020
+NMYSYKKIGNKYIVSINNHTEIVKALNAFCKEKGILSGSINGIGAIGELTLRFFNPKTKAYDDKTFREQMEISNLTGNISSMNEQVYLHLHITVGRSDYSALAGHLLSAIQNGAGEFVVEDYSERISRTYNPDLGLNIYDFER/NMYSYKKIGNKYIVSINNHTEIVKALNAFCKEKGILSGSINGIGAIGELTLRFFNPKTKAYDDKTFREQMEISNLTGNISSMNEQVYLHLHITVGRSDYSALAGHLLSAIQNGAGEFVVEDYSERISRTYNPDLGLNIYDFER
+>T=0.1, sample=0, score=0.7425, seq_recovery=0.5606
+NMYSYKKIGNKYIVNINNNTELVEAIKKFCKDENIKSGSINGTGQVSKVTLRFYNPETKEYKETTFNDNFDISNLTGFISTYNNEVFLDLHITIGKSNFSALAGHLLSAVVNGEMTLVVEDYNELLSMKYNEELGLYLLDFNK/NLYSYKKIGNKYIVSINNHTDIVTALKTFCEDKNIKSGTINGIGQVSSVTLRFFNIETKEVKEVTFNENLEISNLTGIISEKDGKVFLHLHGTFGKSDFSALAGHLLSAVVNGKALFEIEDFKEKVNVEYDEELGLWLLNFNK
+>T=0.1, sample=0, score=0.7474, seq_recovery=0.5644
+NMYSYKKIGNKYIVSINNNTNLVTAIKKFCEDKNIKSGTINGTGQVSKVTLRFYNPETKTYTDKTFNDNFDISNLTGFISTYNGKIFLHLHITIGDSNFSALAGHLIDAIVNGTADLVIEDYNENISMKYDEELGLYLLDFNK/NLYSYKKIGNKYIVSINNHTDIVEALKTFCEEKNIKSGTINGIGTVSSLTLAFRNIETGEVDRKTFNQNLEISNLTGIISTKNGKVFLDLHVTFGDSNFSALAGHLESAIVNGTALVVVEDYNEEVNVEYDEKLGLNLLNFNK

ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_4_outputs/seqs/4YOW.fa

@@ -0,0 +1,6 @@
+>4YOW, score=1.1328, fixed_chains=['B', 'D', 'E', 'F'], designed_chains=['A', 'C'], model_name=v_48_020
+MRIVAADTGGAVLDESFQPVGLIATVAVLVEKPYKTSKRFLVKYADPYNYDLSGRQAIRDEIELAIELAREVSPDVIHLNSTLGGIEVRKLDESTIDALQISDRGKEIWKELSKDLQPLAKKFWEETGIEIIAIGKSSVPVRIAEIYAGIFSVKWALDNVKEKGGLLVGLPRYMEVEIKKDKIIGKSLDPREGGLYGEVKTEVPQGIKWELYPNPLVRRFMVFEITS/MRIVAADTGGAVLDESFQPVGLIATVAVLVEKPYKTSKRFLVKYADPYNYDLSGRQAIRDEIELAIELAREVSPDVIHLNSTLGGIEVRKLDESTIDALQISDRGKEIWKELSKDLQPLAKKFWEETGIEIIAIGKSSVPVRIAEIYAGIFSVKWALDNVKEKGGLLVGLPRYMEVEIKKDKIIGKSLDPREGGLYGEVKTEVPQGIKWELYPNPLVRRFMVFEITS
+>T=0.1, sample=0, score=0.7592, seq_recovery=0.6088
+MRIVAADTGGALLNENYEPIGKIATVAVLVEKPYRTSKEFLVKYHDPLNYDLSSNQGIRDEVLLAIELARKVRPDMIHLDIDLGGVPLAELTPEVIEALQISEETKATLKELAKELTPLAQAFLAETGIPILCIGSRSVPVHIADIYASAEAVRWALENVKKLKGLLVGLEYATRVEIGKDSIKATSLDPRDGGLYAEVKTKIPEGITYELYPDPLRTGHMVFKITT/MKIVAADTGGAVLDESFQPVGRIATVAVVVEEPYRTSKEFLVKYLDPFKYDLSSHEGILEELELAIELAEKVKPDLIHLDLDLGGVELGELDPEVIDALQISPETRATLKELAKTLAPKARAFKEKTGIPILLTGEASVPVRIAEIYASIASVAWALEHVKELKGLRVLLEEAVSVEIEADKIVGRSLDPRDGGLYQELPTAVPEGITWELFPNPLRANHLVFEVTV
+>T=0.1, sample=0, score=0.7365, seq_recovery=0.5972
+MRIVAADTGGYLLDENYRPIGPIATVAVLVEKPYRTSKEFLVRYHDPENYDLTGNQGLYDEFELAIELAEKVKPDLIHLDIDLGGVPVAELTPEVINKLPISEETKKTLIELSKTLTPKAQAFYKKTGIPILAIGDRSVPVKIADIYASIAAVKWALENVKERKGLRVLLEEGVRVEIKENSIVGTSLDPRDGGLYGEIETEVPEGVTYKLYPNPLRLGHLVFEIST/MKIVAADTGGAVLDESFQPVGRIATVAVVVEEPYTTSKEFLVRYLDPFAYDLSSHEGLREEVELAIELARKVKPDLIHLDIDLGGVDVADLDEAVIDALQISPETKAVLKELAKELAPLAKAFKAETGIPILATGHRSVPVHIAHIAASGAAVKWALEHVKELKGLRVLLEEATAVEIKENKIVVTSLDPRHGGLHFEIETEVPEGIEYELFPNPLDAGHMVFEVTV

ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_5_outputs/assigned_pdbs.jsonl

@@ -0,0 +1 @@
+{"4YOW": [["A", "C"], ["B", "D", "E", "F"]], "3HTN": [["A", "C"], ["B"]]}

ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_5_outputs/fixed_pdbs.jsonl

@@ -0,0 +1 @@
+{"4YOW": {"A": [9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], "C": [10, 11, 18, 19, 20, 22], "B": [], "D": [], "E": [], "F": []}, "3HTN": {"A": [9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], "C": [10, 11, 18, 19, 20, 22], "B": []}}

ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_5_outputs/seqs/3HTN.fa

@@ -0,0 +1,6 @@
+>3HTN, score=1.1439, fixed_chains=['B'], designed_chains=['A', 'C'], model_name=v_48_020
+NMYSYKKIGNKYIVSINNHTEIVKALNAFCKEKGILSGSINGIGAIGELTLRFFNPKTKAYDDKTFREQMEISNLTGNISSMNEQVYLHLHITVGRSDYSALAGHLLSAIQNGAGEFVVEDYSERISRTYNPDLGLNIYDFER/NMYSYKKIGNKYIVSINNHTEIVKALNAFCKEKGILSGSINGIGAIGELTLRFFNPKTKAYDDKTFREQMEISNLTGNISSMNEQVYLHLHITVGRSDYSALAGHLLSAIQNGAGEFVVEDYSERISRTYNPDLGLNIYDFER
+>T=0.1, sample=0, score=0.7451, seq_recovery=0.5849
+NMYKYKKIGNKYIVSINNHTEIVKAIKEFCKEKNIKSGTVNGTGQISKLTLRFYNMETKTSTDTTFNQNLDISNLTGFISEHENEVFLDLHITAGDSNFSALAGHLISAISNGTCELVVEDFKEKLSTKYDEETGLYLLDFEK/NMYKYKKIGNKYIVSINNHTEIVKAIKKFCEDKNIKSGTINGIGTISSLTLEFYDIKTKKKKLKTFNAQLEISNLTGIISTKNGEVFLDLHVTVGDSNFSALAGHLVSAVVNGTAKLVVEDYKEEVNVKYDEELGLYLLDFNL
+>T=0.1, sample=0, score=0.7454, seq_recovery=0.5887
+NMYKYKKIGNKYIVSINNHTEIVKAIKEFCKEKNIKSGTVNGIGQVSSVTLKFYNPETKESTLKTFNKLLDISNLTGFISTYNNEVFLDLHITFGDSDFSALAGHLVSAIVDGYAELIVEDYNENISMKYDEELGLWKLDFEK/NMYKYKKIGNKYIVSINNHTNIVKAIKKFCEDKKIKSGTINGIGQISKLTLAFRNIETGEVDLKTFNDNYTISNLTGFISTINGKVFLDLHITVGNSNFSALAGHLIDAISNGTVNLVIEDYKEEINKKYNEELGLWLLDFNL

ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_5_outputs/seqs/4YOW.fa

@@ -0,0 +1,6 @@
+>4YOW, score=1.1085, fixed_chains=['B', 'D', 'E', 'F'], designed_chains=['A', 'C'], model_name=v_48_020
+MRIVAADTGGAVLDESFQPVGLIATVAVLVEKPYKTSKRFLVKYADPYNYDLSGRQAIRDEIELAIELAREVSPDVIHLNSTLGGIEVRKLDESTIDALQISDRGKEIWKELSKDLQPLAKKFWEETGIEIIAIGKSSVPVRIAEIYAGIFSVKWALDNVKEKGGLLVGLPRYMEVEIKKDKIIGKSLDPREGGLYGEVKTEVPQGIKWELYPNPLVRRFMVFEITS/MRIVAADTGGAVLDESFQPVGLIATVAVLVEKPYKTSKRFLVKYADPYNYDLSGRQAIRDEIELAIELAREVSPDVIHLNSTLGGIEVRKLDESTIDALQISDRGKEIWKELSKDLQPLAKKFWEETGIEIIAIGKSSVPVRIAEIYAGIFSVKWALDNVKEKGGLLVGLPRYMEVEIKKDKIIGKSLDPREGGLYGEVKTEVPQGIKWELYPNPLVRRFMVFEITS
+>T=0.1, sample=0, score=0.7336, seq_recovery=0.6074
+MKIVAADTGGAVLDESFQPVGLIATVAVVVEKPYRTSERYKVEYLDPFNYDLTGHEGIYREIRLAIELAREVKPDLIHLDIDLGGVNVAELTPEVIDALQISAETKEVLKELAKELTPLAQEFLAETGIPILAIGDRSVPVHIADIAASGAAVKWALEHVKERKGLRVGLVYATEVEIKEDKIIVRSLDPRDGGLYFEIETEIPEGITWELYPNPLELNHMVFEVTV/MKIVAADTGGALLDENYQPVGLIATVAVVVTYPYRTSDVFKVRYLDPLAYDLASDEHLRLELELAIELAKEVKPDEIHLDLDLGGVDVASLTPEVINALQISPETKARLLELAKELAPLAAAFRKETGIPIKAVGERSVAVRIAEIYASAEAVRWALEHVKERGGLRVLLEEAVSVEIGEDSITARSLDPRHGGLYQEVPVEVPEGVTWELYPNPLRANHMIFEVTV
+>T=0.1, sample=0, score=0.7137, seq_recovery=0.6143
+MKIVAADTGGAVLDESFQPVGLIATVAVLVEKPYRTSDEYLVRYHDPYKYDLTGHQDLRDEVELAIELAEKVKPDLIHLDVDLGGVELATLTPEVIDALPISAETKATLKELAKTLTPLAQKFLAKTGIPIRLIGDRSVPVHIADIAASVYAVKWALENVKKHKGLRVRLVEATEVEIGENEIIGRSLDPRDGGLYFRVETKIPEGIEYKLYPDPLRRHHMVFEVTV/MKIVAADTGGALLDENYQPVGLIATVAVVVTYPYTTSDVFKVRYLDPTAYDLSSDEHLRHEVELAIELAKEVNPDEIHLDLDLGGVDVADLTPEVIDALQISPETRARLKELAKELAPLAAAFKAETGIPIKAIGERSVAVHIAEIYASIYSVKWALEHVKERKGLRVGLEEAVSVEIKEDRIIGRSLDPRDGGLYGEVEVEVPEGIEWELYPNPLRSGHMVFEVTV

ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_5_outputs/tied_pdbs.jsonl

@@ -0,0 +1 @@
+{"4YOW": [{"A": [1], "C": [1]}, {"A": [2], "C": [2]}, {"A": [3], "C": [3]}, {"A": [4], "C": [4]}, {"A": [5], "C": [5]}, {"A": [6], "C": [6]}, {"A": [7], "C": [7]}, {"A": [8], "C": [8]}], "3HTN": [{"A": [1], "C": [1]}, {"A": [2], "C": [2]}, {"A": [3], "C": [3]}, {"A": [4], "C": [4]}, {"A": [5], "C": [5]}, {"A": [6], "C": [6]}, {"A": [7], "C": [7]}, {"A": [8], "C": [8]}]}

ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_7_outputs/bias_pdbs.jsonl

@@ -0,0 +1 @@
+{"G": 40.1, "P": 0.3, "A": -0.05}

ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_7_outputs/seqs/3HTN.fa

@@ -0,0 +1,6 @@
+>3HTN, score=1.2048, fixed_chains=[], designed_chains=['A', 'B', 'C'], model_name=v_48_020
+NMYSYKKIGNKYIVSINNHTEIVKALNAFCKEKGILSGSINGIGAIGELTLRFFNPKTKAYDDKTFREQMEISNLTGNISSMNEQVYLHLHITVGRSDYSALAGHLLSAIQNGAGEFVVEDYSERISRTYNPDLGLNIYDFER/NMYSYKKIGNKYIVSINNHTEIVKALNAFCKEKGILSGSINGIGAIGELTLRFFNPKXXXXDDKTFREQMEISNLTGNISSMNEQVYLHLHITVGRSDYSALAGHLLSAIQNGAGEFVVEDYSERISRTYNPDLGLNIYDFER/NMYSYKKIGNKYIVSINNHTEIVKALNAFCKEKGILSGSINGIGAIGELTLRFFNPKTKAYDDKTFREQMEISNLTGNISSMNEQVYLHLHITVGRSDYSALAGHLLSAIQNGAGEFVVEDYSERISRTYNPDLGLNIYDFER
+>T=0.1, sample=0, score=2.4061, seq_recovery=0.0847
+GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG/GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGXXXXGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG/GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG
+>T=0.1, sample=0, score=2.4041, seq_recovery=0.0847
+GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG/GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGXXXXGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG/GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG

ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_7_outputs/seqs/4YOW.fa

@@ -0,0 +1,6 @@
+>4YOW, score=1.1322, fixed_chains=[], designed_chains=['A', 'B', 'C', 'D', 'E', 'F'], model_name=v_48_020
+MRIVAADTGGAVLDESFQPVGLIATVAVLVEKPYKTSKRFLVKYADPYNYDLSGRQAIRDEIELAIELAREVSPDVIHLNSTLGGIEVRKLDESTIDALQISDRGKEIWKELSKDLQPLAKKFWEETGIEIIAIGKSSVPVRIAEIYAGIFSVKWALDNVKEKGGLLVGLPRYMEVEIKKDKIIGKSLDPREGGLYGEVKTEVPQGIKWELYPNPLVRRFMVFEITS/XXXX/MRIVAADTGGAVLDESFQPVGLIATVAVLVEKPYKTSKRFLVKYADPYNYDLSGRQAIRDEIELAIELAREVSPDVIHLNSTLGGIEVRKLDESTIDALQISDRGKEIWKELSKDLQPLAKKFWEETGIEIIAIGKSSVPVRIAEIYAGIFSVKWALDNVKEKGGLLVGLPRYMEVEIKKDKIIGKSLDPREGGLYGEVKTEVPQGIKWELYPNPLVRRFMVFEITS/XXXX/MRIVAADTGGAVLDESFQPVGLIATVAVLVEKPYKTSKRFLVKYADPYNYDLSGRQAIRDEIELAIELAREVSPDVIHLNSTLGGIEVRKLDESTIDALQISDRGKEIWKELSKDLQPLAKKFWEETGIEIIAIGKSSVPVRIAEIYAGIFSVKWALDNVKEKGGLLVGLPRYMEVEIKKDKIIGKSLDPREGGLYGEVKTEVPQGIKWELYPNPLVRRFMVFEITS/XXXX
+>T=0.1, sample=0, score=2.7441, seq_recovery=0.0793
+GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG/XXXX/GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG/XXXX/GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG/XXXX
+>T=0.1, sample=0, score=2.6962, seq_recovery=0.0793
+GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG/XXXX/GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG/XXXX/GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG/XXXX

ProteinMPNN/vanilla_proteinmpnn/PDB_homooligomers/example_6_outputs/seqs/4GYT.fa

@@ -0,0 +1,6 @@
+>4GYT, score=1.6782, fixed_chains=[], designed_chains=['A', 'B'], model_name=v_48_020
+SLHLPKYDDFVQSISVLALTMSGSELHGIMCGYLCAGADSQGEAYIRALLNNKKDEQSRNALLSMFSVFSISQQQMNNFDFEFEMLLPDDDESLVTRAQAFSEWCEGFTQGLTIAGVGMEQFYEEESQDALQHLMEFAELDCESLEVGEEDERALMEVSEYTRMAVLRLHSDLVLHE/SLHLPKYDDFVQSISVLALTMSGSELHGIMCGYLCAGADSQGEAYIRALLNNKKDEQSRNALLSMFSVFSISQQQMNNFDFEFEMLLPDDDESLVTRAQAFSEWCEGFTQGLTIAGVGMEQFYEEESQDALQHLMEFAELDCESLEVGEEDERALMEVSEYTRMAVLRLHSDLVLHE
+>T=0.2, sample=0, score=0.8880, seq_recovery=0.4463
+ELRLPPYEEFAAAIAVLQLPVSPSELAGLILGYLAAGKIDLGRAWIRSLLGGRTDAASQAALAALLEVFDILEEQLNNPELELELLLPPPDASLRERARALSEFARGFALGLELAGVDKESFKTEESKEAYERILELARLDASALREGPADRARLAELEEWLREAILQIHDDLVNHG/ELRLPPYEEFAAAIAVLQLPVSPSELAGLILGYLAAGKIDLGRAWIRSLLGGRTDAASQAALAALLEVFDILEEQLNNPELELELLLPPPDASLRERARALSEFARGFALGLELAGVDKESFKTEESKEAYERILELARLDASALREGPADRARLAELEEWLREAILQIHDDLVNHG
+>T=0.2, sample=0, score=0.8673, seq_recovery=0.3898
+SLALPPYDEFAAAVAPLKLPFSASYLAGLILGFIVAGKLELGRAWIKSLAKGKTDAATQAAVAALLDVFEILTRQLNDSSLELELLLPPKDASLKERAKALSEFAKGFVEGLELAGVTEESFSKESSKKAYKEIKELAKMDVSKLKEGEEDEKELEEKKEWLKNSILEIHKDLKENK/SLALPPYDEFAAAVAPLKLPFSASYLAGLILGFIVAGKLELGRAWIKSLAKGKTDAATQAAVAALLDVFEILTRQLNDSSLELELLLPPKDASLKERAKALSEFAKGFVEGLELAGVTEESFSKESSKKAYKEIKELAKMDVSKLKEGEEDEKELEEKKEWLKNSILEIHKDLKENK

ProteinMPNN/vanilla_proteinmpnn/PDB_homooligomers/example_6_outputs/seqs/6EHB.fa

@@ -0,0 +1,6 @@
+>6EHB, score=1.3507, fixed_chains=[], designed_chains=['A', 'B', 'C'], model_name=v_48_020
+DGINQSGDKAGSTVYSAKGTSLEVGGRAEARLSLKDGKAQDNSRVRLNFLGKAEINDSLYGVGFYEGEFTTNDQGKNASNNSLDNRYTYAGIGGTYGEVTYGKNDGALGVITDFTDIMSYHGNTAAEKIAVADRVDNMLAYKGQFGDLGVKASYRFADRNAVDAMGNVVTETNAAKYSDNGEDGYSLSAIYTFGDTGFNVGAGYADQDDQNEYMLAASYRMENLYFAGLFTDGELAKDVDYTGYELAAGYKLGQAAFTATYNNAETAKKTSADNFAIDATYYFKPNFRSYISYQFNLLDSXXASKVASEDELAIGLRYDF/DGINQSGDKAGSTVYSAKGTSLEVGGRAEARLSLKDGKAQDNSRVRLNFLGKAEINDSLYGVGFYEGEFTTNDQGKNASNNSLDNRYTYAGIGGTYGEVTYGKNDGALGVITDFTDIMSYHGNTAAEKIAVADRVDNMLAYKGQFGDLGVKASYRFADRNAVDAMGNVVTETNAAKYSDNGEDGYSLSAIYTFGDTGFNVGAGYADQDDQNEYMLAASYRMENLYFAGLFTDGELAKDVDYTGYELAAGYKLGQAAFTATYNNAETAKKTSADNFAIDATYYFKPNFRSYISYQFNLLDXXXASKVASEDELAIGLRYDF/DGINQSGDKAGSTVYSAKGTSLEVGGRAEARLSLKDGKAQDNSRVRLNFLGKAEINDSLYGVGFYEGEFTTNDQGKNASNNSLDNRYTYAGIGGTYGEVTYGKNDGALGVITDFTDIMSYHGNTAAEKIAVADRVDNMLAYKGQFGDLGVKASYRFADRNAVDAMGNVVTETNAAKYSDNGEDGYSLSAIYTFGDTGFNVGAGYADQDDQNEYMLAASYRMENLYFAGLFTDGELAKDVDYTGYELAAGYKLGQAAFTATYNNAETAKKTSADNFAIDATYYFKPNFRSYISYQFNLLDSDKASKVASEDELAIGLRYDF
+>T=0.2, sample=0, score=0.8934, seq_recovery=0.5435
+GGRNLSGPKPGQTVYSSNGSTLKIGGFADANLDIVDGKAKDNSKGRVSLLRTDKINDDLYGVGYIEVELTTNDNGTNAINNNLNVKKLYAGIGGKWGTVTYGKNDGALQIIADFTDIMPYGGNKAAPLIPVADNVDNTLSYSATYGDLTVRAAYQFATQVAVDSNGNVVDEENAAKYSDNGKDGWSASAVYDFGDSGWSVGAGAAKQGDQWAVAVAASYEKDNFYVAALYVAGDLAEGVPYAGLSLAASYKVGNTTYTVNYDVAFVDGKVSEDVLSYGVTYKFTDRFSTYVEYEDNLLDXXXASYVDSADVLRVGIRTDF/GGRNLSGPKPGQTVYSSNGSTLKIGGFADANLDIVDGKAKDNSKGRVSLLRTDKINDDLYGVGYIEVELTTNDNGTNAINNNLNVKKLYAGIGGKWGTVTYGKNDGALQIIADFTDIMPYGGNKAAPLIPVADNVDNTLSYSATYGDLTVRAAYQFATQVAVDSNGNVVDEENAAKYSDNGKDGWSASAVYDFGDSGWSVGAGAAKQGDQWAVAVAASYEKDNFYVAALYVAGDLAEGVPYAGLSLAASYKVGNTTYTVNYDVAFVDGKVSEDVLSYGVTYKFTDRFSTYVEYEDNLLDXXXASYVDSADVLRVGIRTDF/GGRNLSGPKPGQTVYSSNGSTLKIGGFADANLDIVDGKAKDNSKGRVSLLRTDKINDDLYGVGYIEVELTTNDNGTNAINNNLNVKKLYAGIGGKWGTVTYGKNDGALQIIADFTDIMPYGGNKAAPLIPVADNVDNTLSYSATYGDLTVRAAYQFATQVAVDSNGNVVDEENAAKYSDNGKDGWSASAVYDFGDSGWSVGAGAAKQGDQWAVAVAASYEKDNFYVAALYVAGDLAEGVPYAGLSLAASYKVGNTTYTVNYDVAFVDGKVSEDVLSYGVTYKFTDRFSTYVEYEDNLLDXXXASYVDSADVLRVGIRTDF
+>T=0.2, sample=0, score=0.8911, seq_recovery=0.5497
+GGLNLSSPKAGKTVYESGGSTLELGGRADAILKVVDGKFEDKSYGTVSLLKTDQINDDLYGTGYVELEFTVNDNGTNAVNNNLNNVKLYAGIGGKWGTVTYGKNDGALKPIRDFTDIMPYGGNRAAPLIPVADNIDNTLSYSATYGNLSVRASYRFANRIYVDENGNVVAKEEAARVSDNGNDGWSASAIYDFGDTGISVGAGAAHQGDQWQVALAASYKKDNFYVAALLTAGQLAKDVPYLGLSLAASYDFGNWRFTASYDLALVDGKVSEDRLTYGVTYDFTPNFSVSVEYTDNLLDXXXSSYVDSLDELVLGVRTDF/GGLNLSSPKAGKTVYESGGSTLELGGRADAILKVVDGKFEDKSYGTVSLLKTDQINDDLYGTGYVELEFTVNDNGTNAVNNNLNNVKLYAGIGGKWGTVTYGKNDGALKPIRDFTDIMPYGGNRAAPLIPVADNIDNTLSYSATYGNLSVRASYRFANRIYVDENGNVVAKEEAARVSDNGNDGWSASAIYDFGDTGISVGAGAAHQGDQWQVALAASYKKDNFYVAALLTAGQLAKDVPYLGLSLAASYDFGNWRFTASYDLALVDGKVSEDRLTYGVTYDFTPNFSVSVEYTDNLLDXXXSSYVDSLDELVLGVRTDF/GGLNLSSPKAGKTVYESGGSTLELGGRADAILKVVDGKFEDKSYGTVSLLKTDQINDDLYGTGYVELEFTVNDNGTNAVNNNLNNVKLYAGIGGKWGTVTYGKNDGALKPIRDFTDIMPYGGNRAAPLIPVADNIDNTLSYSATYGNLSVRASYRFANRIYVDENGNVVAKEEAARVSDNGNDGWSASAIYDFGDTGISVGAGAAHQGDQWQVALAASYKKDNFYVAALLTAGQLAKDVPYLGLSLAASYDFGNWRFTASYDLALVDGKVSEDRLTYGVTYDFTPNFSVSVEYTDNLLDXXXSSYVDSLDELVLGVRTDF

ProteinMPNN/vanilla_proteinmpnn/PDB_homooligomers/example_6_outputs/tied_pdbs.jsonl

@@ -0,0 +1 @@
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ProteinMPNN/vanilla_proteinmpnn/PDB_monomers/example_1_outputs/parsed_pdbs.jsonl

@@ -0,0 +1,2 @@
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27.467, 56.337], [20.162, 25.477, 60.718], [19.425, 23.084, 56.754], [18.544, 19.031, 57.959], [18.427, 17.355, 53.535], [16.231, 13.89, 55.157], [18.022, 10.811, 52.366], [14.608, 8.257, 53.699], [16.601, 4.461, 54.699], [12.391, 2.3, 55.229], [14.338, -1.043, 57.557], [10.101, -3.503, 56.86], [8.668, -6.468, 55.697], [8.959, -4.394, 51.905], [10.569, -1.042, 54.499], [8.934, 2.298, 51.817], [12.103, 5.252, 53.305], [9.722, 9.241, 53.893], [13.499, 11.135, 54.676], [12.845, 13.511, 58.513], [16.983, 15.964, 58.026], [16.401, 17.994, 61.976], [19.882, 20.939, 60.945], [20.986, 21.581, 65.294], [23.86, 24.873, 64.065], [25.32, 24.562, 68.418], [28.641, 24.59, 68.998], [28.902, 20.843, 67.213], [24.492, 21.583, 67.413], [22.176, 19.279, 69.665], [18.494, 20.423, 69.772], [17.819, 19.495, 65.079], [14.236, 16.503, 65.911], [13.432, 15.7, 61.239], [9.814, 13.225, 62.192], [9.69, 11.926, 57.664], [5.503, 9.336, 58.905], [3.96, 9.844, 54.754]]}, "name": "5L33", "num_of_chains": 1, "seq": "HMPEEEKAARLFIEALEKGDPELMRKVISPDTRMEDNGREFTGDEVVEYVKEIQKRGEQWHLRRYTKEGNSWRFEVQVDNNGQTEQWEVQIEVRNGRIKRVTITHV"}

ProteinMPNN/vanilla_proteinmpnn/PDB_monomers/example_1_outputs/seqs/5L33.fa

@@ -0,0 +1,6 @@
+>5L33, score=1.5848, fixed_chains=[], designed_chains=['A'], model_name=v_48_020
+HMPEEEKAARLFIEALEKGDPELMRKVISPDTRMEDNGREFTGDEVVEYVKEIQKRGEQWHLRRYTKEGNSWRFEVQVDNNGQTEQWEVQIEVRNGRIKRVTITHV
+>T=0.1, sample=0, score=0.8419, seq_recovery=0.4245
+SVDPETAKALAFVKALEKADPELMAKVITPDTEMEVNGKKYKGDEIVEYVKKLKEEGIKYKLLSYKKDGDKYVFTMEKSYKGKTYTVTIEIEVKDGKVAKVVITEK
+>T=0.1, sample=0, score=0.8087, seq_recovery=0.4811
+SINEEEKKALDFIEALEKADPELMKKVIEPDTKMEVNGKKYEGEEIVKFVEELKKSGVKYKLKSYKKEGDKYVFTVEKSENGKTYTVTIEVKVENGKVKEVKITEE

ProteinMPNN/vanilla_proteinmpnn/PDB_monomers/example_1_outputs/seqs/6MRR.fa

@@ -0,0 +1,6 @@
+>6MRR, score=1.4854, fixed_chains=[], designed_chains=['A'], model_name=v_48_020
+GWSTELEKHREELKEFLKKEGITNVEIRIDNGRLEVRVEGGTERLKRFLEELRQKLEKKGYTVDIKIE
+>T=0.1, sample=0, score=0.9197, seq_recovery=0.5147
+GIDPELEEKVEELKKFLKEKGIDNVEIEVEDGVLKIKVKGASEELKEFLKKLKEELEEKGYEVEVEIE
+>T=0.1, sample=0, score=0.9356, seq_recovery=0.5588
+GKDPELEKYVKELKEFLKKQGITNVKIEVKDGTLTITTKGASEELKKFLEKLKKELEAKGYKVNVKIE

ProteinMPNN/vanilla_proteinmpnn/examples/submit_example_1.sh

@@ -0,0 +1,27 @@
+#!/bin/bash
+#SBATCH -p gpu
+#SBATCH --mem=32g
+#SBATCH --gres=gpu:rtx2080:1
+#SBATCH -c 2
+#SBATCH --output=example_1.out
+
+source activate mlfold
+
+folder_with_pdbs="../PDB_monomers/pdbs/"
+
+output_dir="../PDB_monomers/example_1_outputs"
+if [ ! -d $output_dir ]
+then
+    mkdir -p $output_dir
+fi
+
+path_for_parsed_chains=$output_dir"/parsed_pdbs.jsonl"
+
+python ../helper_scripts/parse_multiple_chains.py --input_path=$folder_with_pdbs --output_path=$path_for_parsed_chains
+
+python ../protein_mpnn_run.py \
+        --jsonl_path $path_for_parsed_chains \
+        --out_folder $output_dir \
+        --num_seq_per_target 2 \
+        --sampling_temp "0.1" \
+        --batch_size 1

ProteinMPNN/vanilla_proteinmpnn/examples/submit_example_2.sh

@@ -0,0 +1,32 @@
+#!/bin/bash
+#SBATCH -p gpu
+#SBATCH --mem=32g
+#SBATCH --gres=gpu:rtx2080:1
+#SBATCH -c 2
+#SBATCH --output=example_2.out
+
+source activate mlfold
+
+folder_with_pdbs="../PDB_complexes/pdbs/"
+
+output_dir="../PDB_complexes/example_2_outputs"
+if [ ! -d $output_dir ] 
+then
+    mkdir -p $output_dir
+fi
+
+path_for_parsed_chains=$output_dir"/parsed_pdbs.jsonl"
+path_for_assigned_chains=$output_dir"/assigned_pdbs.jsonl"
+chains_to_design="A B"
+
+python ../helper_scripts/parse_multiple_chains.py --input_path=$folder_with_pdbs --output_path=$path_for_parsed_chains
+
+python ../helper_scripts/assign_fixed_chains.py --input_path=$path_for_parsed_chains --output_path=$path_for_assigned_chains --chain_list "$chains_to_design"
+
+python ../protein_mpnn_run.py \
+        --jsonl_path $path_for_parsed_chains \
+        --chain_id_jsonl $path_for_assigned_chains \
+        --out_folder $output_dir \
+        --num_seq_per_target 2 \
+        --sampling_temp "0.1" \
+        --batch_size 1

ProteinMPNN/vanilla_proteinmpnn/examples/submit_example_3.sh

@@ -0,0 +1,26 @@
+#!/bin/bash
+#SBATCH -p gpu
+#SBATCH --mem=32g
+#SBATCH --gres=gpu:rtx2080:1
+#SBATCH -c 3
+#SBATCH --output=example_3.out
+
+source activate mlfold
+
+path_to_PDB="../PDB_complexes/pdbs/3HTN.pdb"
+
+output_dir="../PDB_complexes/example_3_outputs"
+if [ ! -d $output_dir ]
+then
+    mkdir -p $output_dir
+fi
+
+chains_to_design="A B"
+
+python ../protein_mpnn_run.py \
+        --pdb_path $path_to_PDB \
+        --pdb_path_chains "$chains_to_design" \
+        --out_folder $output_dir \
+        --num_seq_per_target 2 \
+        --sampling_temp "0.1" \
+        --batch_size 1

ProteinMPNN/vanilla_proteinmpnn/examples/submit_example_4.sh

@@ -0,0 +1,39 @@
+#!/bin/bash
+#SBATCH -p gpu
+#SBATCH --mem=32g
+#SBATCH --gres=gpu:rtx2080:1
+#SBATCH -c 3
+#SBATCH --output=example_4.out
+
+source activate mlfold
+
+folder_with_pdbs="../PDB_complexes/pdbs/"
+
+output_dir="../PDB_complexes/example_4_outputs"
+if [ ! -d $output_dir ]
+then
+    mkdir -p $output_dir
+fi
+
+
+path_for_parsed_chains=$output_dir"/parsed_pdbs.jsonl"
+path_for_assigned_chains=$output_dir"/assigned_pdbs.jsonl"
+path_for_fixed_positions=$output_dir"/fixed_pdbs.jsonl"
+chains_to_design="A C"
+#The first amino acid in the chain corresponds to 1 and not PDB residues index for now.
+fixed_positions="1 2 3 4 5 6 7 8 23 25, 10 11 12 13 14 15 16 17 18 19 20 40" #fixing/not designing residues 1 2 3...25 in chain A and residues 10 11 12...40 in chain C
+
+python ../helper_scripts/parse_multiple_chains.py --input_path=$folder_with_pdbs --output_path=$path_for_parsed_chains
+
+python ../helper_scripts/assign_fixed_chains.py --input_path=$path_for_parsed_chains --output_path=$path_for_assigned_chains --chain_list "$chains_to_design"
+
+python ../helper_scripts/make_fixed_positions_dict.py --input_path=$path_for_parsed_chains --output_path=$path_for_fixed_positions --chain_list "$chains_to_design" --position_list "$fixed_positions"
+
+python ../protein_mpnn_run.py \
+        --jsonl_path $path_for_parsed_chains \
+        --chain_id_jsonl $path_for_assigned_chains \
+        --fixed_positions_jsonl $path_for_fixed_positions \
+        --out_folder $output_dir \
+        --num_seq_per_target 2 \
+        --sampling_temp "0.1" \
+        --batch_size 1

ProteinMPNN/vanilla_proteinmpnn/examples/submit_example_5.sh

@@ -0,0 +1,43 @@
+#!/bin/bash
+#SBATCH -p gpu
+#SBATCH --mem=32g
+#SBATCH --gres=gpu:rtx2080:1
+#SBATCH -c 3
+#SBATCH --output=example_5.out
+
+source activate mlfold
+
+folder_with_pdbs="../PDB_complexes/pdbs/"
+
+output_dir="../PDB_complexes/example_5_outputs"
+if [ ! -d $output_dir ]
+then
+    mkdir -p $output_dir
+fi
+
+
+path_for_parsed_chains=$output_dir"/parsed_pdbs.jsonl"
+path_for_assigned_chains=$output_dir"/assigned_pdbs.jsonl"
+path_for_fixed_positions=$output_dir"/fixed_pdbs.jsonl"
+path_for_tied_positions=$output_dir"/tied_pdbs.jsonl"
+chains_to_design="A C"
+fixed_positions="9 10 11 12 13 14 15 16 17 18 19 20 21 22 23, 10 11 18 19 20 22"
+tied_positions="1 2 3 4 5 6 7 8, 1 2 3 4 5 6 7 8" #two list must match in length; residue 1 in chain A and C will be sampled togther;
+
+python ../helper_scripts/parse_multiple_chains.py --input_path=$folder_with_pdbs --output_path=$path_for_parsed_chains
+
+python ../helper_scripts/assign_fixed_chains.py --input_path=$path_for_parsed_chains --output_path=$path_for_assigned_chains --chain_list "$chains_to_design"
+
+python ../helper_scripts/make_fixed_positions_dict.py --input_path=$path_for_parsed_chains --output_path=$path_for_fixed_positions --chain_list "$chains_to_design" --position_list "$fixed_positions"
+
+python ../helper_scripts/make_tied_positions_dict.py --input_path=$path_for_parsed_chains --output_path=$path_for_tied_positions --chain_list "$chains_to_design" --position_list "$tied_positions"
+
+python ../protein_mpnn_run.py \
+        --jsonl_path $path_for_parsed_chains \
+        --chain_id_jsonl $path_for_assigned_chains \
+        --fixed_positions_jsonl $path_for_fixed_positions \
+        --tied_positions_jsonl $path_for_tied_positions \
+        --out_folder $output_dir \
+        --num_seq_per_target 2 \
+        --sampling_temp "0.1" \
+        --batch_size 1

ProteinMPNN/vanilla_proteinmpnn/examples/submit_example_6.sh

@@ -0,0 +1,33 @@
+#!/bin/bash
+#SBATCH -p gpu
+#SBATCH --mem=32g
+#SBATCH --gres=gpu:rtx2080:1
+#SBATCH -c 3
+#SBATCH --output=example_6.out
+
+source activate mlfold
+
+folder_with_pdbs="../PDB_homooligomers/pdbs/"
+
+output_dir="../PDB_homooligomers/example_6_outputs"
+if [ ! -d $output_dir ]
+then
+    mkdir -p $output_dir
+fi
+
+
+path_for_parsed_chains=$output_dir"/parsed_pdbs.jsonl"
+path_for_tied_positions=$output_dir"/tied_pdbs.jsonl"
+path_for_designed_sequences=$output_dir"/temp_0.1"
+
+python ../helper_scripts/parse_multiple_chains.py --input_path=$folder_with_pdbs --output_path=$path_for_parsed_chains
+
+python ../helper_scripts/make_tied_positions_dict.py --input_path=$path_for_parsed_chains --output_path=$path_for_tied_positions --homooligomer 1
+
+python ../protein_mpnn_run.py \
+        --jsonl_path $path_for_parsed_chains \
+        --tied_positions_jsonl $path_for_tied_positions \
+        --out_folder $output_dir \
+        --num_seq_per_target 2 \
+        --sampling_temp "0.2" \
+        --batch_size 1

ProteinMPNN/vanilla_proteinmpnn/examples/submit_example_7.sh

@@ -0,0 +1,40 @@
+#!/bin/bash
+#SBATCH -p gpu
+#SBATCH --mem=32g
+#SBATCH --gres=gpu:rtx2080:1
+#SBATCH -c 3
+#SBATCH --output=example_7.out
+
+source activate mlfold
+
+folder_with_pdbs="../PDB_complexes/pdbs/"
+
+output_dir="../PDB_complexes/example_7_outputs"
+if [ ! -d $output_dir ]
+then
+    mkdir -p $output_dir
+fi
+
+
+path_for_parsed_chains=$output_dir"/parsed_pdbs.jsonl"
+path_for_assigned_chains=$output_dir"/PDB_complexes/assigned_pdbs.jsonl"
+path_for_bias=$output_dir"/bias_pdbs.jsonl"
+AA_list="G P A"
+bias_list="40.1 0.3 -0.05" #for G P A respectively; global AA bias in the logit space
+chains_to_design="A B"
+
+
+python ../helper_scripts/parse_multiple_chains.py --input_path=$folder_with_pdbs --output_path=$path_for_parsed_chains
+
+python ../helper_scripts/assign_fixed_chains.py --input_path=$path_for_parsed_chains --output_path=$path_for_assigned_chains --chain_list "$chains_to_design"
+
+python ../helper_scripts/make_bias_AA.py --output_path=$path_for_bias --AA_list="$AA_list" --bias_list="$bias_list"
+
+python ../protein_mpnn_run.py \
+        --jsonl_path $path_for_parsed_chains \
+        --chain_id_jsonl $path_for_assigned_chains \
+        --out_folder $output_dir \
+        --bias_AA_jsonl $path_for_bias \
+        --num_seq_per_target 2 \
+        --sampling_temp "0.1" \
+        --batch_size 1

ProteinMPNN/vanilla_proteinmpnn/helper_scripts/assign_fixed_chains.py

@@ -0,0 +1,39 @@
+import argparse
+
+def main(args):
+    import json
+
+    with open(args.input_path, 'r') as json_file:
+        json_list = list(json_file)
+    
+    global_designed_chain_list = []
+    if args.chain_list != '':
+        global_designed_chain_list = [str(item) for item in args.chain_list.split()]
+    my_dict = {}
+    for json_str in json_list:
+        result = json.loads(json_str)
+        all_chain_list = [item[-1:] for item in list(result) if item[:9]=='seq_chain'] #['A','B', 'C',...]
+        if len(global_designed_chain_list) > 0:
+            designed_chain_list = global_designed_chain_list
+        else:
+            #manually specify, e.g.
+            designed_chain_list = ["A"]
+        fixed_chain_list = [letter for letter in all_chain_list if letter not in designed_chain_list] #fix/do not redesign these chains 
+        my_dict[result['name']]= (designed_chain_list, fixed_chain_list)
+    
+    with open(args.output_path, 'w') as f:
+        f.write(json.dumps(my_dict) + '\n')
+
+
+if __name__ == "__main__":
+    argparser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+    argparser.add_argument("--input_path", type=str, help="Path to the parsed PDBs")
+    argparser.add_argument("--output_path", type=str, help="Path to the output dictionary")
+    argparser.add_argument("--chain_list", type=str, default='', help="List of the chains that need to be designed")    
+
+    args = argparser.parse_args()
+    main(args) 
+
+# Output looks like this:
+# {"5TTA": [["A"], ["B"]], "3LIS": [["A"], ["B"]]}
+

ProteinMPNN/vanilla_proteinmpnn/helper_scripts/make_bias_AA.py

@@ -0,0 +1,27 @@
+import argparse 
+
+def main(args):
+
+    import numpy as np
+    import json
+
+    bias_list = [float(item) for item in args.bias_list.split()]
+    AA_list = [str(item) for item in args.AA_list.split()]
+
+    my_dict = dict(zip(AA_list, bias_list))
+
+    with open(args.output_path, 'w') as f:
+        f.write(json.dumps(my_dict) + '\n')
+
+
+if __name__ == "__main__":
+    argparser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+    argparser.add_argument("--output_path", type=str, help="Path to the output dictionary")
+    argparser.add_argument("--AA_list", type=str, default='', help="List of AAs to be biased")
+    argparser.add_argument("--bias_list", type=str, default='', help="AA bias strengths")
+
+    args = argparser.parse_args()
+    main(args)
+
+#e.g. output
+#{"A": -0.01, "G": 0.02}