AF_cluster_in_colabdesign.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "V100",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/HWaymentSteele/AF_Cluster/blob/main/AF_cluster_in_colabdesign.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#AF-Cluster, implemented with ColabDesign\n",
        "\n",
        "Made possible thanks to the [ColabDesign](https://github.com/sokrypton/ColabDesign) codebase!\n",
        "\n",
        "The DBSCAN clustering step is the same implementation as the code used for Wayment-Steele, Ojoawo, ... Kern (2024) ([ref](https://www.nature.com/articles/s41586-023-06832-9)). This notebook allows for combining that clustering algorithm with all models, recycles, dropout, random seeds.\n",
        "\n",
        "NB: the results in the paper used min. number of sequences in clusters=3, model 1, and 3 recycles.\n",
        "\n",
        "H Wayment-Steele, last updated 26 Jul 2024"
      ],
      "metadata": {
        "id": "-ODkDJyNnBOI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "#@title setup\n",
        "import os, time, gc\n",
        "if not os.path.isdir(\"params\"):\n",
        "  # get code\n",
        "  print(\"installing ColabDesign\")\n",
        "  os.system(\"(mkdir params; apt-get install aria2 -qq; \\\n",
        "  aria2c -q -x 16 https://storage.googleapis.com/alphafold/alphafold_params_2022-12-06.tar; \\\n",
        "  tar -xf alphafold_params_2022-12-06.tar -C params; touch params/done.txt )&\")\n",
        "\n",
        "  os.system(\"pip -q install git+https://github.com/sokrypton/ColabDesign.git@gamma\")\n",
        "  os.system(\"ln -s /usr/local/lib/python3.*/dist-packages/colabdesign colabdesign\")\n",
        "  os.system(\"wget https://raw.githubusercontent.com/sokrypton/ColabFold/beta/colabfold/mmseqs/api.py\")\n",
        "\n",
        "  # install hhsuite\n",
        "  print(\"installing HHsuite\")\n",
        "  os.makedirs(\"hhsuite\", exist_ok=True)\n",
        "  os.system(f\"curl -fsSL https://github.com/soedinglab/hh-suite/releases/download/v3.3.0/hhsuite-3.3.0-SSE2-Linux.tar.gz | tar xz -C hhsuite/\")\n",
        "\n",
        "  # download params\n",
        "  if not os.path.isfile(\"params/done.txt\"):\n",
        "    print(\"downloading AlphaFold params\")\n",
        "    while not os.path.isfile(\"params/done.txt\"):\n",
        "      time.sleep(5)\n",
        "if \"hhsuite\" not in os.environ['PATH']:\n",
        "  os.environ['PATH'] += \":hhsuite/bin:hhsuite/scripts\"\n",
        "\n",
        "import re, tempfile\n",
        "from IPython.display import HTML\n",
        "from google.colab import files\n",
        "import numpy as np\n",
        "from colabdesign import mk_af_model, clear_mem\n",
        "from colabdesign.af.contrib import predict\n",
        "from colabdesign.shared.protein import _np_rmsd\n",
        "\n",
        "from api import run_mmseqs2\n",
        "import matplotlib.pyplot as plt\n",
        "import string\n",
        "import numpy as np\n",
        "from sklearn.cluster import DBSCAN\n",
        "\n",
        "def run_hhalign(query_sequence, target_sequence, query_a3m=None, target_a3m=None):\n",
        "  with tempfile.NamedTemporaryFile() as tmp_query, \\\n",
        "  tempfile.NamedTemporaryFile() as tmp_target, \\\n",
        "  tempfile.NamedTemporaryFile() as tmp_alignment:\n",
        "    if query_a3m is None:\n",
        "      tmp_query.write(f\">Q\\n{query_sequence}\\n\".encode())\n",
        "      tmp_query.flush()\n",
        "      query_a3m = tmp_query.name\n",
        "    if target_a3m is None:\n",
        "      tmp_target.write(f\">T\\n{target_sequence}\\n\".encode())\n",
        "      tmp_target.flush()\n",
        "      target_a3m = tmp_target.name\n",
        "    os.system(f\"hhalign -hide_cons -i {query_a3m} -t {target_a3m} -o {tmp_alignment.name}\")\n",
        "    X, start_indices = predict.parse_hhalign_output(tmp_alignment.name)\n",
        "  return X, start_indices\n",
        "\n",
        "def run_hhfilter(input, output, id=90, qid=10):\n",
        "  os.system(f\"hhfilter -id {id} -qid {qid} -i {input} -o {output}\")\n",
        "\n",
        "def one_hot(x,cat=None):\n",
        "  if cat is None: cat = np.max(x)+1\n",
        "  oh = np.concatenate((np.eye(cat),np.zeros([1,cat])))\n",
        "  return oh[x]\n",
        "\n",
        "def sweep_dbscan(msa, deletion_matrix, verbose=True, min_eps=3, max_eps=20, eps_step=0.5,min_samples=5):\n",
        "  '''Input: MSA with shape N, L (N=num sequences, L=length) where chars are integers\n",
        "  Ouptut: list of MSA clusters. each is shape [M,L] where M is variable, is the length of the new clustered MSA.\n",
        "  Each cluster MSA has query sequence at start\n",
        "  '''\n",
        "\n",
        "  N, L = msa.shape\n",
        "  ohe_msa = one_hot(msa).reshape(N,-1)\n",
        "  eps_test_vals=np.arange(min_eps, max_eps+eps_step, eps_step)\n",
        "  smaller_split = np.random.choice(range(len(msa)), int(len(msa)/4))\n",
        "  test_split = ohe_msa[smaller_split]\n",
        "  n_clusters=[]\n",
        "  if verbose:\n",
        "    print('eps\\tnum clusters\\tn not clustered')\n",
        "  for eps in eps_test_vals:\n",
        "    clustering = DBSCAN(eps=eps, min_samples=min_samples).fit(test_split)\n",
        "    n_clust = len(set(clustering.labels_))\n",
        "    n_not_clustered = len(clustering.labels_[np.where(clustering.labels_==-1)])\n",
        "    if verbose:\n",
        "       print('%.2f\\t%d\\t%d' % (eps, n_clust, n_not_clustered))\n",
        "    n_clusters.append(n_clust)\n",
        "    if eps>10 and n_clust==1:\n",
        "        break\n",
        "\n",
        "  eps_max = eps_test_vals[np.argmax(n_clusters)]\n",
        "  if verbose: print(\"eps max:\", eps_max)\n",
        "  clustering = DBSCAN(eps=eps_max, min_samples=min_samples).fit(ohe_msa)\n",
        "  clusters = [x for x in list(set(clustering.labels_)) if x>=0]\n",
        "  n_not_clustered = len(clustering.labels_[np.where(clustering.labels_==-1)])\n",
        "  if verbose:\n",
        "    print('%d clusters' % len(clusters))\n",
        "    print('%d seqs not clustered' % n_not_clustered)\n",
        "\n",
        "  clustered_msas=[]\n",
        "  clustered_dtxs=[]\n",
        "  query_seq=msa[0]\n",
        "  query_dtx = deletion_matrix[0]\n",
        "\n",
        "  for c in clusters:\n",
        "    cluster_msa = msa[np.where(clustering.labels_==c)]\n",
        "    cluster_dtx = deletion_matrix[np.where(clustering.labels_==c)]\n",
        "    cluster_msa = np.concatenate([[query_seq], cluster_msa])\n",
        "    cluster_dtx = np.concatenate([[query_dtx], cluster_dtx])\n",
        "\n",
        "    clustered_msas.append(cluster_msa)\n",
        "    clustered_dtxs.append(cluster_dtx)\n",
        "\n",
        "  return clustered_msas, clustered_dtxs\n",
        "\n",
        "\n",
        "def make_msa_arr(msa, query_seq):\n",
        "    # get dists between each member of msa and query seq\n",
        "    dist_arr = [1-len(np.where(x- query_seq!=0)[0])/len(query_seq) for x in msa]\n",
        "\n",
        "    sorted_msa = [msa[x] for x in np.argsort(dist_arr)]\n",
        "    sorted_dist_arr = [dist_arr[x] for x in np.argsort(dist_arr)]\n",
        "\n",
        "    plotting_arr = np.zeros([len(msa), len(query_seq)])*np.NaN\n",
        "\n",
        "    ctr=0\n",
        "    for dist, msa_seq in list(zip(sorted_dist_arr, sorted_msa)):\n",
        "        for j, char in enumerate(msa_seq):\n",
        "          if char!=21:\n",
        "            plotting_arr[ctr,j] = dist\n",
        "        ctr+=1\n",
        "\n",
        "    return plotting_arr\n",
        "\n",
        "def plot_cluster_msas(list_of_cluster_msas, pad_size=20, figsize=(8,8),dpi=100,sort_by_dist=False):\n",
        "    query_seq=list_of_cluster_msas[0][0]\n",
        "    arrs = [make_msa_arr(msa[1:], query_seq) for msa in list(reversed(list_of_cluster_msas))] # exclude 0 since that's the query seq\n",
        "    mean_cluster_dists = [np.nanmean(x) for x in arrs]\n",
        "\n",
        "    if sort_by_dist:\n",
        "        arrs = [arrs[x] for x in np.argsort(mean_cluster_dists)]\n",
        "\n",
        "    pad = np.zeros([pad_size, len(query_seq)])*np.NaN\n",
        "    padded_arrs = [np.vstack([x, pad]) for x in arrs]\n",
        "\n",
        "    plotting_arr = np.vstack(padded_arrs)\n",
        "\n",
        "    yaxis_size,  xaxis_size= plotting_arr.shape\n",
        "\n",
        "    plt.figure(figsize=figsize, dpi=dpi)\n",
        "    plt.imshow(\n",
        "        plotting_arr,\n",
        "    interpolation=\"nearest\",\n",
        "    aspect=\"auto\",\n",
        "    cmap=\"rainbow_r\",\n",
        "    vmin=0,\n",
        "    vmax=1,\n",
        "    origin=\"lower\",\n",
        "    extent=(0,xaxis_size, 0, yaxis_size),)\n",
        "\n",
        "    for x in np.cumsum([len(x) for x in padded_arrs]):\n",
        "        plt.axhline(x-0.5*pad_size,color='k',linewidth=0.5)\n",
        "\n",
        "    plt.xlim(0, xaxis_size)\n",
        "    plt.ylim(-0.5*pad_size, yaxis_size)\n",
        "    plt.colorbar(label=\"Sequence identity to query\",aspect=2, fraction=0.05)\n",
        "    plt.xlabel(\"Positions\")\n",
        "    plt.ylabel(\"Sequences\")"
      ],
      "metadata": {
        "cellView": "form",
        "id": "NsRHgcGPs4fL",
        "outputId": "d2cbffd2-438f-48c0-de6d-55565ce88dbc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "installing ColabDesign\n",
            "installing HHsuite\n",
            "downloading AlphaFold params\n",
            "CPU times: user 2.57 s, sys: 419 ms, total: 2.99 s\n",
            "Wall time: 2min 13s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title prep_inputs\n",
        "sequence = \"RKTYVLKLYVAGNTPNSVRALKTLNNILEKEFKGVYALKVIDVLKNPQLAEEDKILATPTLAKVLPPPVRRIIGDLSNREKVLIGLDLLYEEIGDQAEDDLGLE\" #@param {type:\"string\"}\n",
        "jobname = \"KaiBTE\" #@param {type:\"string\"}\n",
        "\n",
        "copies = 1 #@param [\"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"10\", \"11\", \"12\"] {type:\"raw\"}\n",
        "#@markdown ----\n",
        "#@markdown **MSA options**\n",
        "msa_method = \"mmseqs2\" #@param [\"mmseqs2\",\"single_sequence\",\"custom_a3m\"]\n",
        "pair_mode = \"unpaired_paired\" #@param [\"unpaired_paired\",\"paired\",\"unpaired\"] {type:\"string\"}\n",
        "#@markdown filtering options\n",
        "cov = 75 #@param [\"0\", \"25\", \"50\", \"75\", \"90\", \"99\"] {type:\"raw\"}\n",
        "id = 90 #@param [\"80\",\"90\", \"100\"] {type:\"raw\"}\n",
        "qid = 0 #@param [\"0\", \"10\", \"15\", \"20\", \"30\"] {type:\"raw\"}\n",
        "do_not_filter = False #@param {type:\"boolean\"}\n",
        "\n",
        "#@markdown **AF-Cluster options**\n",
        "min_samples_in_cluster = 10 #@param [\"3\",\"5\",\"10\"] {type:\"raw\"}\n",
        "\n",
        "#@markdown ----\n",
        "#@markdown **Templates options**\n",
        "template_mode = \"none\" #@param [\"none\", \"mmseqs2\", \"custom\"] {type:\"string\"}\n",
        "use_templates = template_mode in [\"mmseqs2\",\"custom\"]\n",
        "pdb = \"\" #@param {type:\"string\"}\n",
        "chain = \"\" #@param {type:\"string\"}\n",
        "flexible = False #@param {type:\"boolean\"}\n",
        "propagate_to_copies = True #@param {type:\"boolean\"}\n",
        "rm_interchain = False\n",
        "rm_sidechain = rm_sequence = flexible\n",
        "\n",
        "# filter options\n",
        "sequence = str(sequence).replace(\"/\",\":\")\n",
        "sequence = re.sub(\"[^A-Z:/]\", \"\", sequence.upper())\n",
        "sequence = re.sub(\":+\",\":\",sequence)\n",
        "sequence = re.sub(\"^[:/]+\",\"\",sequence)\n",
        "sequence = re.sub(\"[:/]+$\",\"\",sequence)\n",
        "jobname = re.sub(r'\\W+', '', jobname)\n",
        "\n",
        "# process sequence\n",
        "sequences = sequence.split(\":\")\n",
        "u_sequences = predict.get_unique_sequences(sequences)\n",
        "if len(sequences) > len(u_sequences):\n",
        "  print(\"WARNING: use copies to define homooligomers\")\n",
        "u_lengths = [len(s) for s in u_sequences]\n",
        "\n",
        "sub_seq = \"\".join(u_sequences)\n",
        "seq = sub_seq * copies\n",
        "\n",
        "jobname = f\"{jobname}_{predict.get_hash(seq)[:5]}\"\n",
        "input_opts = {\"sequence\":u_sequences,\n",
        "              \"copies\":copies,\n",
        "              \"msa_method\":msa_method,\n",
        "              \"pair_mode\":pair_mode,\n",
        "              \"do_not_filter\":do_not_filter,\n",
        "              \"cov\":cov,\n",
        "              \"id\":id,\n",
        "              \"template_mode\":template_mode,\n",
        "              \"propagate_to_copies\":propagate_to_copies}\n",
        "\n",
        "##################\n",
        "# GET MSA\n",
        "##################\n",
        "os.makedirs(jobname, exist_ok=True)\n",
        "if msa_method == \"mmseqs2\":\n",
        "  msa, deletion_matrix = predict.get_msa(u_sequences, jobname,\n",
        "    mode=pair_mode,\n",
        "    cov=cov, id=id, qid=qid, max_msa=4096,\n",
        "    do_not_filter=do_not_filter,\n",
        "    mmseqs2_fn=run_mmseqs2,\n",
        "    hhfilter_fn=run_hhfilter)\n",
        "\n",
        "elif msa_method == \"single_sequence\":\n",
        "  with open(f\"{jobname}/msa.a3m\",\"w\") as a3m:\n",
        "    a3m.write(f\">{jobname}\\n{sub_seq}\\n\")\n",
        "  msa, deletion_matrix = predict.parse_a3m(f\"{jobname}/msa.a3m\")\n",
        "\n",
        "elif msa_method == \"custom_a3m\":\n",
        "  print(\"upload custom a3m\")\n",
        "  msa_dict = files.upload()\n",
        "  lines = msa_dict[list(msa_dict.keys())[0]].decode().splitlines()\n",
        "  a3m_lines = []\n",
        "  for line in lines:\n",
        "    line = line.replace(\"\\x00\",\"\")\n",
        "    if len(line) > 0 and not line.startswith('#'):\n",
        "      a3m_lines.append(line)\n",
        "\n",
        "  with open(f\"{jobname}/msa.a3m\",\"w\") as a3m:\n",
        "    a3m.write(\"\\n\".join(a3m_lines))\n",
        "  if do_not_filter:\n",
        "    os.system(f\"hhfilter -M first -qid 0 -id 100 -cov 0 -i {jobname}/msa.a3m -o {jobname}/msa.filt.a3m\")\n",
        "  else:\n",
        "    os.system(f\"hhfilter -M first -qid {qid} -id {id} -cov {cov} -i {jobname}/msa.a3m -o {jobname}/msa.filt.a3m\")\n",
        "  msa, deletion_matrix = predict.parse_a3m(f\"{jobname}/msa.filt.a3m\")\n",
        "\n",
        "if len(msa) > 1:\n",
        "  predict.plot_msa(msa, u_lengths).show()\n",
        "\n",
        "print('performing AF-Cluster')\n",
        "\n",
        "clustered_msas, clustered_dtxs = sweep_dbscan(msa,deletion_matrix, verbose=True,min_samples=min_samples_in_cluster)\n",
        "plot_cluster_msas(clustered_msas)\n",
        "\n",
        "##################\n",
        "if use_templates:\n",
        "  print(\"aligning template\")\n",
        "  template_msa = f\"{jobname}/msa.a3m\"\n",
        "  if template_mode == \"mmseqs2\":\n",
        "    predict.get_msa(u_sequences, jobname,\n",
        "      mode=\"unpaired\",\n",
        "      mmseqs2_fn=run_mmseqs2,\n",
        "      do_not_filter=True,\n",
        "      do_not_return=True,\n",
        "      output_a3m=f\"{jobname}/msa_tmp.a3m\")\n",
        "    template_msa = f\"{jobname}/msa_tmp.a3m\"\n",
        "    if not propagate_to_copies and copies > 1:\n",
        "      new_msa = []\n",
        "      with open(template_msa, \"r\") as handle:\n",
        "        for line in handle:\n",
        "          if not line.startswith(\">\"):\n",
        "            new_msa.append(line.rstrip())\n",
        "      with open(template_msa, \"w\") as handle:\n",
        "        for n,seq in enumerate(new_msa):\n",
        "          handle.write(f\">{n}\\n{seq*copies}\\n\")\n",
        "\n",
        "    templates = {}\n",
        "    print(\"ID\\tpdb\\tcid\\tevalue\")\n",
        "    for line in open(f\"{jobname}/msa/_env/pdb70.m8\",\"r\"):\n",
        "      p = line.rstrip().split()\n",
        "      M,target_id,qid,e_value = p[0],p[1],p[2],p[10]\n",
        "      M = int(M)\n",
        "      if M not in templates:\n",
        "        templates[M] = []\n",
        "      if len(templates[M]) < 4:\n",
        "        print(f\"{int(M)}\\t{target_id}\\t{qid}\\t{e_value}\")\n",
        "        templates[M].append(target_id)\n",
        "    if len(templates) == 0:\n",
        "      use_templates = False\n",
        "      print(\"ERROR: no templates found...\")\n",
        "    else:\n",
        "      Ms = sorted(list(templates.keys()))\n",
        "      pdbs,chains = [],[]\n",
        "      for M in Ms:\n",
        "        for n,target_id in enumerate(templates[M]):\n",
        "          pdb_id,chain_id = target_id.split(\"_\")\n",
        "          if len(pdbs) < n+1:\n",
        "            pdbs.append([])\n",
        "            chains.append([])\n",
        "          pdbs[n].append(pdb_id)\n",
        "          chains[n].append(chain_id)\n",
        "      print(pdbs)\n",
        "  else:\n",
        "    pdbs,chains = [pdb],[chain]\n",
        "\n",
        "if use_templates:\n",
        "  input_opts.update({\"pdbs\":pdbs, \"chains\":chains})\n",
        "  batches = []\n",
        "  for pdb,chain in zip(pdbs,chains):\n",
        "    query_seq = \"\".join(u_sequences)\n",
        "    batch = predict.get_template_feats(pdb, chain,\n",
        "      query_seq=query_seq,\n",
        "      query_a3m=template_msa,\n",
        "      copies=copies,\n",
        "      propagate_to_copies=propagate_to_copies,\n",
        "      use_seq=not rm_sequence,\n",
        "      get_pdb_fn=get_pdb,\n",
        "      align_fn=run_hhalign)\n",
        "    batches.append(batch)\n",
        "\n",
        "  # for display\n",
        "  plt.figure(figsize=(3*len(batches),3))\n",
        "  for n,batch in enumerate(batches):\n",
        "    plt.subplot(1,len(batches),n+1)\n",
        "    plt.title(f\"template features {n+1}\")\n",
        "    dgram = batch[\"dgram\"].argmax(-1).astype(float)\n",
        "    dgram[batch[\"dgram\"].sum(-1) == 0] = np.nan\n",
        "    Ln = dgram.shape[0]\n",
        "    plt.imshow(dgram, extent=(0, Ln, Ln, 0))\n",
        "    predict.plot_ticks(u_lengths * copies)\n",
        "  plt.show()\n",
        "else:\n",
        "  batches = [None]\n",
        "\n",
        "################\n",
        "print(\"GC\",gc.collect())"
      ],
      "metadata": {
        "id": "7pYi5tmHe34I",
        "cellView": "form",
        "outputId": "77c7a34c-ebfc-41d1-d325-4ac48841076e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "getting unpaired MSA\n",
            "parsing msas\n",
            "gathering info\n",
            "filtering sequences\n",
            "selecting final sequences\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "performing AF-Cluster\n",
            "eps\tnum clusters\tn not clustered\n",
            "3.00\t1\t1024\n",
            "3.50\t1\t1024\n",
            "4.00\t1\t1024\n",
            "4.50\t1\t1024\n",
            "5.00\t1\t1024\n",
            "5.50\t1\t1024\n",
            "6.00\t2\t1011\n",
            "6.50\t3\t1000\n",
            "7.00\t3\t949\n",
            "7.50\t8\t779\n",
            "8.00\t6\t552\n",
            "8.50\t7\t315\n",
            "9.00\t3\t154\n",
            "9.50\t2\t57\n",
            "10.00\t2\t28\n",
            "10.50\t2\t2\n",
            "11.00\t1\t0\n",
            "eps max: 7.5\n",
            "19 clusters\n",
            "2117 seqs not clustered\n",
            "GC 6017\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "#@title run_prediction\n",
        "#@markdown Model options\n",
        "model_type = \"auto\" #@param [\"monomer (ptm)\", \"multimer (v3)\", \"auto\"]\n",
        "model = \"all\" #@param [\"1\", \"2\", \"3\", \"4\", \"5\", \"all\"]\n",
        "num_recycles = 3 #@param [\"0\", \"1\", \"2\", \"3\", \"6\", \"12\", \"24\"] {type:\"raw\"}\n",
        "recycle_early_stop_tolerance = 0.5 #@param [\"0.0\", \"0.5\", \"1.0\"] {type:\"raw\"}\n",
        "use_initial_guess = False\n",
        "#@markdown MSA options\n",
        "num_msa = 512 #@param [\"1\",\"2\",\"4\",\"8\",\"16\",\"32\", \"64\", \"128\", \"256\", \"512\"] {type:\"raw\"}\n",
        "num_extra_msa = 1024 #@param [\"1\",\"2\",\"4\",\"8\",\"16\",\"32\", \"64\", \"128\", \"256\", \"512\", \"1024\",\"2048\",\"4096\"] {type:\"raw\"}\n",
        "use_cluster_profile = False #@param {type:\"boolean\"}\n",
        "use_mlm = False #@param {type:\"boolean\"}\n",
        "#@markdown Stochastic options\n",
        "use_dropout = False #@param {type:\"boolean\"}\n",
        "seed = 0 #@param {type:\"raw\"}\n",
        "num_seeds = 1 #@param [\"1\", \"2\", \"4\", \"8\", \"16\", \"32\", \"64\", \"128\"] {type:\"raw\"}\n",
        "\n",
        "if model_type == \"monomer (ptm)\":\n",
        "  use_multimer = False\n",
        "elif model_type == \"multimer (v3)\":\n",
        "  use_multimer = True\n",
        "elif len(u_lengths) > 1 or copies > 1:\n",
        "  use_multimer = True\n",
        "else:\n",
        "  use_multimer = False\n",
        "\n",
        "model_opts = {\"num_msa\":num_msa, # number of sequences to use\n",
        "              \"num_extra_msa\":num_extra_msa,\n",
        "              \"num_templates\":len(batches),\n",
        "              \"use_mlm\":True,\n",
        "              \"use_cluster_profile\":use_cluster_profile,\n",
        "              \"use_multimer\":use_multimer,\n",
        "              \"use_templates\":use_templates,\n",
        "              \"use_batch_as_template\":False,\n",
        "              \"use_dgram\":True,\n",
        "              \"protocol\":\"hallucination\",\n",
        "              \"best_metric\":\"pae\",\n",
        "              \"optimize_seq\":False,\n",
        "              \"debug\":False,\n",
        "              \"clear_prev\":False}\n",
        "\n",
        "if \"af\" in dir():\n",
        "  if model_opts != model_opts_:\n",
        "    if model_opts[\"use_multimer\"] == af._args[\"use_multimer\"] \\\n",
        "    or model_opts[\"use_templates\"] == af._args[\"use_templates\"]:\n",
        "      old_params = dict(zip(af._model_names,\n",
        "                            af._model_params))\n",
        "    else:\n",
        "      print(\"loading alphafold params\")\n",
        "      old_params = {}\n",
        "    af = mk_af_model(old_params=old_params,\n",
        "                     **model_opts)\n",
        "    model_opts_ = predict.copy_dict(model_opts)\n",
        "else:\n",
        "  print(\"loading alphafold params\")\n",
        "  af = mk_af_model(**model_opts)\n",
        "  model_opts_ = predict.copy_dict(model_opts)\n",
        "\n",
        "run_opts = {\"seed\":seed,\n",
        "            \"use_mlm\":use_mlm,\n",
        "            \"use_dropout\":use_dropout,\n",
        "            \"num_recycles\":num_recycles,\n",
        "            \"model\":model,\n",
        "            \"use_initial_guess\":use_initial_guess}\n",
        "\n",
        "af.prep_inputs(u_lengths, copies=copies, seed=seed)\n",
        "if use_templates:\n",
        "  af.set_opt(use_initial_guess=use_initial_guess)\n",
        "  for n,batch in enumerate(batches):\n",
        "    af.set_template(batch=batch, n=n)\n",
        "  af.set_opt(\"template\",\n",
        "             rm_sc=rm_sidechain,\n",
        "             rm_seq=rm_sequence,\n",
        "             rm_ic=rm_interchain)\n",
        "af.set_opt(\"mlm\",\n",
        "           replace_fraction=0.15 if use_mlm else 0.0)\n",
        "\n",
        "if model == \"all\":\n",
        "  models = af._model_names\n",
        "else:\n",
        "  models = [af._model_names[int(model) - 1]]\n",
        "\n",
        "pdb_path = f\"{jobname}/pdb\"\n",
        "os.makedirs(pdb_path, exist_ok=True)\n",
        "\n",
        "seeds = list(range(seed,seed+num_seeds))\n",
        "print(\"running prediction\")\n",
        "\n",
        "outputs=[]\n",
        "\n",
        "for c_ind in range(len(clustered_msas)):\n",
        "  af.set_msa(clustered_msas[c_ind], clustered_dtxs[c_ind])\n",
        "  for seed in seeds:\n",
        "    af.set_seed(seed)\n",
        "    for model in models:\n",
        "      o={}\n",
        "      recycle = 0\n",
        "      af._inputs.pop(\"prev\",None)\n",
        "      stop_recycle = False\n",
        "      while recycle < num_recycles + 1:\n",
        "        af.predict(dropout=use_dropout, models=[model], verbose=False)\n",
        "\n",
        "        print_str = f\"cluster={c_ind} seed={seed} model={model} recycle={recycle}\"\n",
        "        print_key = [\"plddt\",\"ptm\"]\n",
        "        if len(af._lengths) > 1: print_key.append(\"i_ptm\")\n",
        "        for k in print_key:\n",
        "          print_str += f\" {k}={af.aux['log'][k]:.3f}\"\n",
        "\n",
        "        af._inputs[\"prev\"] = af.aux[\"prev\"]\n",
        "        af._save_results(save_best=True, verbose=False)\n",
        "        af._k += 1\n",
        "        output_pdb = f\"{pdb_path}/{model}_cluster{c_ind}_r{recycle}_seed{seed}.pdb\"\n",
        "        af.save_current_pdb(output_pdb)\n",
        "\n",
        "        recycle += 1\n",
        "        if recycle > 1:\n",
        "          rmsd_tol = _np_rmsd(af._tmp[\"traj\"][\"xyz\"][-2],\n",
        "                              af._tmp[\"traj\"][\"xyz\"][-1],\n",
        "                              use_jax=False)\n",
        "          if rmsd_tol < recycle_early_stop_tolerance:\n",
        "            stop_recycle = True\n",
        "          print_str += f\" rmsd_tol={rmsd_tol:.3f}\"\n",
        "        print(print_str)\n",
        "        if stop_recycle: break\n",
        "        for k in print_key:\n",
        "          o.update({k: af.aux['log'][k]})\n",
        "\n",
        "        o.update({'pdb_path': output_pdb})\n",
        "        o.update({'model': model})\n",
        "        o.update({'cluster': c_ind})\n",
        "        o.update({'cluster_size': clustered_msas[c_ind].shape[0]-1})\n",
        "        outputs.append(o)\n",
        "\n",
        "import pandas as pd\n",
        "outputs = pd.DataFrame.from_records(outputs)\n",
        "################\n",
        "print(\"GC\",gc.collect())"
      ],
      "metadata": {
        "cellView": "form",
        "id": "aE7Bs7qQtcP0",
        "outputId": "a3355ae0-3e33-41cf-d79a-e73896d0e36b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "loading alphafold params\n",
            "running prediction\n",
            "cluster=0 seed=0 model=model_1_ptm recycle=0 plddt=0.576 ptm=0.418\n",
            "cluster=0 seed=0 model=model_1_ptm recycle=1 plddt=0.583 ptm=0.416 rmsd_tol=3.402\n",
            "cluster=0 seed=0 model=model_1_ptm recycle=2 plddt=0.570 ptm=0.421 rmsd_tol=1.426\n",
            "cluster=0 seed=0 model=model_1_ptm recycle=3 plddt=0.593 ptm=0.436 rmsd_tol=1.435\n",
            "cluster=0 seed=0 model=model_2_ptm recycle=0 plddt=0.631 ptm=0.507\n",
            "cluster=0 seed=0 model=model_2_ptm recycle=1 plddt=0.611 ptm=0.483 rmsd_tol=2.370\n",
            "cluster=0 seed=0 model=model_2_ptm recycle=2 plddt=0.612 ptm=0.478 rmsd_tol=2.592\n",
            "cluster=0 seed=0 model=model_2_ptm recycle=3 plddt=0.590 ptm=0.454 rmsd_tol=1.358\n",
            "cluster=0 seed=0 model=model_3_ptm recycle=0 plddt=0.621 ptm=0.444\n",
            "cluster=0 seed=0 model=model_3_ptm recycle=1 plddt=0.596 ptm=0.433 rmsd_tol=4.945\n",
            "cluster=0 seed=0 model=model_3_ptm recycle=2 plddt=0.592 ptm=0.442 rmsd_tol=1.390\n",
            "cluster=0 seed=0 model=model_3_ptm recycle=3 plddt=0.609 ptm=0.460 rmsd_tol=2.330\n",
            "cluster=0 seed=0 model=model_4_ptm recycle=0 plddt=0.589 ptm=0.447\n",
            "cluster=0 seed=0 model=model_4_ptm recycle=1 plddt=0.596 ptm=0.442 rmsd_tol=2.068\n",
            "cluster=0 seed=0 model=model_4_ptm recycle=2 plddt=0.621 ptm=0.465 rmsd_tol=1.496\n",
            "cluster=0 seed=0 model=model_4_ptm recycle=3 plddt=0.614 ptm=0.443 rmsd_tol=1.791\n",
            "cluster=0 seed=0 model=model_5_ptm recycle=0 plddt=0.577 ptm=0.438\n",
            "cluster=0 seed=0 model=model_5_ptm recycle=1 plddt=0.563 ptm=0.423 rmsd_tol=3.690\n",
            "cluster=0 seed=0 model=model_5_ptm recycle=2 plddt=0.543 ptm=0.407 rmsd_tol=1.287\n",
            "cluster=0 seed=0 model=model_5_ptm recycle=3 plddt=0.599 ptm=0.459 rmsd_tol=1.503\n",
            "cluster=1 seed=0 model=model_1_ptm recycle=0 plddt=0.658 ptm=0.528\n",
            "cluster=1 seed=0 model=model_1_ptm recycle=1 plddt=0.591 ptm=0.494 rmsd_tol=2.185\n",
            "cluster=1 seed=0 model=model_1_ptm recycle=2 plddt=0.618 ptm=0.471 rmsd_tol=12.085\n",
            "cluster=1 seed=0 model=model_1_ptm recycle=3 plddt=0.621 ptm=0.493 rmsd_tol=12.028\n",
            "cluster=1 seed=0 model=model_2_ptm recycle=0 plddt=0.546 ptm=0.402\n",
            "cluster=1 seed=0 model=model_2_ptm recycle=1 plddt=0.546 ptm=0.416 rmsd_tol=4.676\n",
            "cluster=1 seed=0 model=model_2_ptm recycle=2 plddt=0.584 ptm=0.433 rmsd_tol=9.703\n",
            "cluster=1 seed=0 model=model_2_ptm recycle=3 plddt=0.600 ptm=0.468 rmsd_tol=2.379\n",
            "cluster=1 seed=0 model=model_3_ptm recycle=0 plddt=0.837 ptm=0.722\n",
            "cluster=1 seed=0 model=model_3_ptm recycle=1 plddt=0.835 ptm=0.736 rmsd_tol=1.097\n",
            "cluster=1 seed=0 model=model_3_ptm recycle=2 plddt=0.828 ptm=0.725 rmsd_tol=1.032\n",
            "cluster=1 seed=0 model=model_3_ptm recycle=3 plddt=0.831 ptm=0.736 rmsd_tol=0.681\n",
            "cluster=1 seed=0 model=model_4_ptm recycle=0 plddt=0.839 ptm=0.738\n",
            "cluster=1 seed=0 model=model_4_ptm recycle=1 plddt=0.832 ptm=0.739 rmsd_tol=0.999\n",
            "cluster=1 seed=0 model=model_4_ptm recycle=2 plddt=0.814 ptm=0.713 rmsd_tol=1.745\n",
            "cluster=1 seed=0 model=model_4_ptm recycle=3 plddt=0.811 ptm=0.709 rmsd_tol=1.220\n",
            "cluster=1 seed=0 model=model_5_ptm recycle=0 plddt=0.834 ptm=0.731\n",
            "cluster=1 seed=0 model=model_5_ptm recycle=1 plddt=0.833 ptm=0.742 rmsd_tol=1.781\n",
            "cluster=1 seed=0 model=model_5_ptm recycle=2 plddt=0.826 ptm=0.734 rmsd_tol=1.005\n",
            "cluster=1 seed=0 model=model_5_ptm recycle=3 plddt=0.821 ptm=0.728 rmsd_tol=0.717\n",
            "cluster=2 seed=0 model=model_1_ptm recycle=0 plddt=0.694 ptm=0.595\n",
            "cluster=2 seed=0 model=model_1_ptm recycle=1 plddt=0.798 ptm=0.699 rmsd_tol=1.461\n",
            "cluster=2 seed=0 model=model_1_ptm recycle=2 plddt=0.786 ptm=0.686 rmsd_tol=0.791\n",
            "cluster=2 seed=0 model=model_1_ptm recycle=3 plddt=0.792 ptm=0.685 rmsd_tol=0.681\n",
            "cluster=2 seed=0 model=model_2_ptm recycle=0 plddt=0.565 ptm=0.412\n",
            "cluster=2 seed=0 model=model_2_ptm recycle=1 plddt=0.760 ptm=0.665 rmsd_tol=2.425\n",
            "cluster=2 seed=0 model=model_2_ptm recycle=2 plddt=0.822 ptm=0.736 rmsd_tol=3.320\n",
            "cluster=2 seed=0 model=model_2_ptm recycle=3 plddt=0.817 ptm=0.730 rmsd_tol=1.427\n",
            "cluster=2 seed=0 model=model_3_ptm recycle=0 plddt=0.653 ptm=0.514\n",
            "cluster=2 seed=0 model=model_3_ptm recycle=1 plddt=0.801 ptm=0.692 rmsd_tol=1.178\n",
            "cluster=2 seed=0 model=model_3_ptm recycle=2 plddt=0.788 ptm=0.682 rmsd_tol=0.767\n",
            "cluster=2 seed=0 model=model_3_ptm recycle=3 plddt=0.785 ptm=0.678 rmsd_tol=1.027\n",
            "cluster=2 seed=0 model=model_4_ptm recycle=0 plddt=0.759 ptm=0.640\n",
            "cluster=2 seed=0 model=model_4_ptm recycle=1 plddt=0.840 ptm=0.750 rmsd_tol=2.124\n",
            "cluster=2 seed=0 model=model_4_ptm recycle=2 plddt=0.832 ptm=0.742 rmsd_tol=1.816\n",
            "cluster=2 seed=0 model=model_4_ptm recycle=3 plddt=0.836 ptm=0.741 rmsd_tol=1.738\n",
            "cluster=2 seed=0 model=model_5_ptm recycle=0 plddt=0.718 ptm=0.626\n",
            "cluster=2 seed=0 model=model_5_ptm recycle=1 plddt=0.808 ptm=0.727 rmsd_tol=2.580\n",
            "cluster=2 seed=0 model=model_5_ptm recycle=2 plddt=0.807 ptm=0.724 rmsd_tol=1.485\n",
            "cluster=2 seed=0 model=model_5_ptm recycle=3 plddt=0.807 ptm=0.720 rmsd_tol=0.940\n",
            "cluster=3 seed=0 model=model_1_ptm recycle=0 plddt=0.553 ptm=0.464\n",
            "cluster=3 seed=0 model=model_1_ptm recycle=1 plddt=0.549 ptm=0.449 rmsd_tol=1.491\n",
            "cluster=3 seed=0 model=model_1_ptm recycle=2 plddt=0.541 ptm=0.446 rmsd_tol=1.364\n",
            "cluster=3 seed=0 model=model_1_ptm recycle=3 plddt=0.565 ptm=0.447 rmsd_tol=1.690\n",
            "cluster=3 seed=0 model=model_2_ptm recycle=0 plddt=0.594 ptm=0.491\n",
            "cluster=3 seed=0 model=model_2_ptm recycle=1 plddt=0.578 ptm=0.457 rmsd_tol=0.927\n",
            "cluster=3 seed=0 model=model_2_ptm recycle=2 plddt=0.579 ptm=0.462 rmsd_tol=0.671\n",
            "cluster=3 seed=0 model=model_2_ptm recycle=3 plddt=0.610 ptm=0.492 rmsd_tol=0.779\n",
            "cluster=3 seed=0 model=model_3_ptm recycle=0 plddt=0.588 ptm=0.459\n",
            "cluster=3 seed=0 model=model_3_ptm recycle=1 plddt=0.618 ptm=0.460 rmsd_tol=1.883\n",
            "cluster=3 seed=0 model=model_3_ptm recycle=2 plddt=0.620 ptm=0.464 rmsd_tol=0.930\n",
            "cluster=3 seed=0 model=model_3_ptm recycle=3 plddt=0.610 ptm=0.446 rmsd_tol=0.436\n",
            "cluster=3 seed=0 model=model_4_ptm recycle=0 plddt=0.616 ptm=0.515\n",
            "cluster=3 seed=0 model=model_4_ptm recycle=1 plddt=0.689 ptm=0.587 rmsd_tol=1.800\n",
            "cluster=3 seed=0 model=model_4_ptm recycle=2 plddt=0.696 ptm=0.572 rmsd_tol=1.297\n",
            "cluster=3 seed=0 model=model_4_ptm recycle=3 plddt=0.716 ptm=0.599 rmsd_tol=0.561\n",
            "cluster=3 seed=0 model=model_5_ptm recycle=0 plddt=0.605 ptm=0.502\n",
            "cluster=3 seed=0 model=model_5_ptm recycle=1 plddt=0.641 ptm=0.547 rmsd_tol=1.001\n",
            "cluster=3 seed=0 model=model_5_ptm recycle=2 plddt=0.640 ptm=0.503 rmsd_tol=0.561\n",
            "cluster=3 seed=0 model=model_5_ptm recycle=3 plddt=0.640 ptm=0.526 rmsd_tol=0.435\n",
            "cluster=4 seed=0 model=model_1_ptm recycle=0 plddt=0.636 ptm=0.558\n",
            "cluster=4 seed=0 model=model_1_ptm recycle=1 plddt=0.771 ptm=0.688 rmsd_tol=6.311\n",
            "cluster=4 seed=0 model=model_1_ptm recycle=2 plddt=0.750 ptm=0.668 rmsd_tol=1.489\n",
            "cluster=4 seed=0 model=model_1_ptm recycle=3 plddt=0.751 ptm=0.666 rmsd_tol=1.022\n",
            "cluster=4 seed=0 model=model_2_ptm recycle=0 plddt=0.659 ptm=0.591\n",
            "cluster=4 seed=0 model=model_2_ptm recycle=1 plddt=0.762 ptm=0.691 rmsd_tol=1.910\n",
            "cluster=4 seed=0 model=model_2_ptm recycle=2 plddt=0.803 ptm=0.725 rmsd_tol=1.977\n",
            "cluster=4 seed=0 model=model_2_ptm recycle=3 plddt=0.815 ptm=0.733 rmsd_tol=2.117\n",
            "cluster=4 seed=0 model=model_3_ptm recycle=0 plddt=0.704 ptm=0.589\n",
            "cluster=4 seed=0 model=model_3_ptm recycle=1 plddt=0.758 ptm=0.679 rmsd_tol=8.216\n",
            "cluster=4 seed=0 model=model_3_ptm recycle=2 plddt=0.789 ptm=0.712 rmsd_tol=1.911\n",
            "cluster=4 seed=0 model=model_3_ptm recycle=3 plddt=0.816 ptm=0.737 rmsd_tol=1.718\n",
            "cluster=4 seed=0 model=model_4_ptm recycle=0 plddt=0.680 ptm=0.611\n",
            "cluster=4 seed=0 model=model_4_ptm recycle=1 plddt=0.784 ptm=0.705 rmsd_tol=2.852\n",
            "cluster=4 seed=0 model=model_4_ptm recycle=2 plddt=0.800 ptm=0.723 rmsd_tol=1.478\n",
            "cluster=4 seed=0 model=model_4_ptm recycle=3 plddt=0.805 ptm=0.722 rmsd_tol=0.255\n",
            "cluster=4 seed=0 model=model_5_ptm recycle=0 plddt=0.624 ptm=0.537\n",
            "cluster=4 seed=0 model=model_5_ptm recycle=1 plddt=0.684 ptm=0.619 rmsd_tol=5.393\n",
            "cluster=4 seed=0 model=model_5_ptm recycle=2 plddt=0.808 ptm=0.737 rmsd_tol=2.482\n",
            "cluster=4 seed=0 model=model_5_ptm recycle=3 plddt=0.807 ptm=0.732 rmsd_tol=0.805\n",
            "cluster=5 seed=0 model=model_1_ptm recycle=0 plddt=0.505 ptm=0.405\n",
            "cluster=5 seed=0 model=model_1_ptm recycle=1 plddt=0.504 ptm=0.389 rmsd_tol=1.794\n",
            "cluster=5 seed=0 model=model_1_ptm recycle=2 plddt=0.496 ptm=0.389 rmsd_tol=1.765\n",
            "cluster=5 seed=0 model=model_1_ptm recycle=3 plddt=0.497 ptm=0.381 rmsd_tol=1.565\n",
            "cluster=5 seed=0 model=model_2_ptm recycle=0 plddt=0.576 ptm=0.476\n",
            "cluster=5 seed=0 model=model_2_ptm recycle=1 plddt=0.698 ptm=0.628 rmsd_tol=6.683\n",
            "cluster=5 seed=0 model=model_2_ptm recycle=2 plddt=0.639 ptm=0.557 rmsd_tol=5.288\n",
            "cluster=5 seed=0 model=model_2_ptm recycle=3 plddt=0.623 ptm=0.537 rmsd_tol=1.633\n",
            "cluster=5 seed=0 model=model_3_ptm recycle=0 plddt=0.539 ptm=0.412\n",
            "cluster=5 seed=0 model=model_3_ptm recycle=1 plddt=0.529 ptm=0.388 rmsd_tol=2.505\n",
            "cluster=5 seed=0 model=model_3_ptm recycle=2 plddt=0.529 ptm=0.381 rmsd_tol=1.260\n",
            "cluster=5 seed=0 model=model_3_ptm recycle=3 plddt=0.533 ptm=0.384 rmsd_tol=0.543\n",
            "cluster=5 seed=0 model=model_4_ptm recycle=0 plddt=0.487 ptm=0.384\n",
            "cluster=5 seed=0 model=model_4_ptm recycle=1 plddt=0.506 ptm=0.392 rmsd_tol=6.701\n",
            "cluster=5 seed=0 model=model_4_ptm recycle=2 plddt=0.531 ptm=0.409 rmsd_tol=3.783\n",
            "cluster=5 seed=0 model=model_4_ptm recycle=3 plddt=0.527 ptm=0.400 rmsd_tol=0.630\n",
            "cluster=5 seed=0 model=model_5_ptm recycle=0 plddt=0.505 ptm=0.406\n",
            "cluster=5 seed=0 model=model_5_ptm recycle=1 plddt=0.545 ptm=0.454 rmsd_tol=5.763\n",
            "cluster=5 seed=0 model=model_5_ptm recycle=2 plddt=0.627 ptm=0.532 rmsd_tol=12.227\n",
            "cluster=5 seed=0 model=model_5_ptm recycle=3 plddt=0.629 ptm=0.540 rmsd_tol=2.700\n",
            "cluster=6 seed=0 model=model_1_ptm recycle=0 plddt=0.617 ptm=0.530\n",
            "cluster=6 seed=0 model=model_1_ptm recycle=1 plddt=0.687 ptm=0.614 rmsd_tol=5.686\n",
            "cluster=6 seed=0 model=model_1_ptm recycle=2 plddt=0.700 ptm=0.619 rmsd_tol=4.121\n",
            "cluster=6 seed=0 model=model_1_ptm recycle=3 plddt=0.758 ptm=0.683 rmsd_tol=2.110\n",
            "cluster=6 seed=0 model=model_2_ptm recycle=0 plddt=0.679 ptm=0.587\n",
            "cluster=6 seed=0 model=model_2_ptm recycle=1 plddt=0.767 ptm=0.699 rmsd_tol=3.070\n",
            "cluster=6 seed=0 model=model_2_ptm recycle=2 plddt=0.768 ptm=0.699 rmsd_tol=1.204\n",
            "cluster=6 seed=0 model=model_2_ptm recycle=3 plddt=0.742 ptm=0.676 rmsd_tol=0.717\n",
            "cluster=6 seed=0 model=model_3_ptm recycle=0 plddt=0.739 ptm=0.644\n",
            "cluster=6 seed=0 model=model_3_ptm recycle=1 plddt=0.713 ptm=0.617 rmsd_tol=4.676\n",
            "cluster=6 seed=0 model=model_3_ptm recycle=2 plddt=0.707 ptm=0.597 rmsd_tol=1.110\n",
            "cluster=6 seed=0 model=model_3_ptm recycle=3 plddt=0.697 ptm=0.582 rmsd_tol=0.944\n",
            "cluster=6 seed=0 model=model_4_ptm recycle=0 plddt=0.673 ptm=0.596\n",
            "cluster=6 seed=0 model=model_4_ptm recycle=1 plddt=0.719 ptm=0.636 rmsd_tol=6.420\n",
            "cluster=6 seed=0 model=model_4_ptm recycle=2 plddt=0.775 ptm=0.701 rmsd_tol=0.876\n",
            "cluster=6 seed=0 model=model_4_ptm recycle=3 plddt=0.785 ptm=0.716 rmsd_tol=0.673\n",
            "cluster=6 seed=0 model=model_5_ptm recycle=0 plddt=0.712 ptm=0.644\n",
            "cluster=6 seed=0 model=model_5_ptm recycle=1 plddt=0.735 ptm=0.680 rmsd_tol=1.929\n",
            "cluster=6 seed=0 model=model_5_ptm recycle=2 plddt=0.747 ptm=0.690 rmsd_tol=1.972\n",
            "cluster=6 seed=0 model=model_5_ptm recycle=3 plddt=0.747 ptm=0.687 rmsd_tol=2.103\n",
            "cluster=7 seed=0 model=model_1_ptm recycle=0 plddt=0.575 ptm=0.443\n",
            "cluster=7 seed=0 model=model_1_ptm recycle=1 plddt=0.547 ptm=0.420 rmsd_tol=2.427\n",
            "cluster=7 seed=0 model=model_1_ptm recycle=2 plddt=0.558 ptm=0.430 rmsd_tol=3.678\n",
            "cluster=7 seed=0 model=model_1_ptm recycle=3 plddt=0.551 ptm=0.415 rmsd_tol=2.101\n",
            "cluster=7 seed=0 model=model_2_ptm recycle=0 plddt=0.541 ptm=0.451\n",
            "cluster=7 seed=0 model=model_2_ptm recycle=1 plddt=0.555 ptm=0.437 rmsd_tol=10.183\n",
            "cluster=7 seed=0 model=model_2_ptm recycle=2 plddt=0.555 ptm=0.413 rmsd_tol=1.862\n",
            "cluster=7 seed=0 model=model_2_ptm recycle=3 plddt=0.550 ptm=0.420 rmsd_tol=2.686\n",
            "cluster=7 seed=0 model=model_3_ptm recycle=0 plddt=0.540 ptm=0.403\n",
            "cluster=7 seed=0 model=model_3_ptm recycle=1 plddt=0.570 ptm=0.419 rmsd_tol=4.383\n",
            "cluster=7 seed=0 model=model_3_ptm recycle=2 plddt=0.595 ptm=0.419 rmsd_tol=2.708\n",
            "cluster=7 seed=0 model=model_3_ptm recycle=3 plddt=0.693 ptm=0.570 rmsd_tol=6.703\n",
            "cluster=7 seed=0 model=model_4_ptm recycle=0 plddt=0.589 ptm=0.456\n",
            "cluster=7 seed=0 model=model_4_ptm recycle=1 plddt=0.583 ptm=0.461 rmsd_tol=4.100\n",
            "cluster=7 seed=0 model=model_4_ptm recycle=2 plddt=0.592 ptm=0.475 rmsd_tol=1.182\n",
            "cluster=7 seed=0 model=model_4_ptm recycle=3 plddt=0.672 ptm=0.572 rmsd_tol=2.949\n",
            "cluster=7 seed=0 model=model_5_ptm recycle=0 plddt=0.609 ptm=0.489\n",
            "cluster=7 seed=0 model=model_5_ptm recycle=1 plddt=0.684 ptm=0.611 rmsd_tol=2.830\n",
            "cluster=7 seed=0 model=model_5_ptm recycle=2 plddt=0.768 ptm=0.681 rmsd_tol=2.915\n",
            "cluster=7 seed=0 model=model_5_ptm recycle=3 plddt=0.799 ptm=0.705 rmsd_tol=2.537\n",
            "cluster=8 seed=0 model=model_1_ptm recycle=0 plddt=0.557 ptm=0.431\n",
            "cluster=8 seed=0 model=model_1_ptm recycle=1 plddt=0.590 ptm=0.457 rmsd_tol=7.477\n",
            "cluster=8 seed=0 model=model_1_ptm recycle=2 plddt=0.576 ptm=0.446 rmsd_tol=2.640\n",
            "cluster=8 seed=0 model=model_1_ptm recycle=3 plddt=0.569 ptm=0.431 rmsd_tol=1.144\n",
            "cluster=8 seed=0 model=model_2_ptm recycle=0 plddt=0.741 ptm=0.630\n",
            "cluster=8 seed=0 model=model_2_ptm recycle=1 plddt=0.775 ptm=0.677 rmsd_tol=2.191\n",
            "cluster=8 seed=0 model=model_2_ptm recycle=2 plddt=0.790 ptm=0.695 rmsd_tol=1.357\n",
            "cluster=8 seed=0 model=model_2_ptm recycle=3 plddt=0.799 ptm=0.697 rmsd_tol=1.546\n",
            "cluster=8 seed=0 model=model_3_ptm recycle=0 plddt=0.577 ptm=0.399\n",
            "cluster=8 seed=0 model=model_3_ptm recycle=1 plddt=0.534 ptm=0.357 rmsd_tol=1.219\n",
            "cluster=8 seed=0 model=model_3_ptm recycle=2 plddt=0.526 ptm=0.347 rmsd_tol=0.408\n",
            "cluster=8 seed=0 model=model_4_ptm recycle=0 plddt=0.563 ptm=0.395\n",
            "cluster=8 seed=0 model=model_4_ptm recycle=1 plddt=0.759 ptm=0.631 rmsd_tol=1.909\n",
            "cluster=8 seed=0 model=model_4_ptm recycle=2 plddt=0.767 ptm=0.632 rmsd_tol=1.436\n",
            "cluster=8 seed=0 model=model_4_ptm recycle=3 plddt=0.770 ptm=0.628 rmsd_tol=0.802\n",
            "cluster=8 seed=0 model=model_5_ptm recycle=0 plddt=0.564 ptm=0.432\n",
            "cluster=8 seed=0 model=model_5_ptm recycle=1 plddt=0.745 ptm=0.639 rmsd_tol=2.648\n",
            "cluster=8 seed=0 model=model_5_ptm recycle=2 plddt=0.775 ptm=0.676 rmsd_tol=2.567\n",
            "cluster=8 seed=0 model=model_5_ptm recycle=3 plddt=0.793 ptm=0.692 rmsd_tol=1.232\n",
            "cluster=9 seed=0 model=model_1_ptm recycle=0 plddt=0.578 ptm=0.475\n",
            "cluster=9 seed=0 model=model_1_ptm recycle=1 plddt=0.575 ptm=0.448 rmsd_tol=3.935\n",
            "cluster=9 seed=0 model=model_1_ptm recycle=2 plddt=0.570 ptm=0.436 rmsd_tol=2.428\n",
            "cluster=9 seed=0 model=model_1_ptm recycle=3 plddt=0.556 ptm=0.431 rmsd_tol=1.181\n",
            "cluster=9 seed=0 model=model_2_ptm recycle=0 plddt=0.654 ptm=0.531\n",
            "cluster=9 seed=0 model=model_2_ptm recycle=1 plddt=0.626 ptm=0.494 rmsd_tol=10.960\n",
            "cluster=9 seed=0 model=model_2_ptm recycle=2 plddt=0.545 ptm=0.427 rmsd_tol=2.345\n",
            "cluster=9 seed=0 model=model_2_ptm recycle=3 plddt=0.538 ptm=0.405 rmsd_tol=9.933\n",
            "cluster=9 seed=0 model=model_3_ptm recycle=0 plddt=0.667 ptm=0.542\n",
            "cluster=9 seed=0 model=model_3_ptm recycle=1 plddt=0.628 ptm=0.479 rmsd_tol=7.268\n",
            "cluster=9 seed=0 model=model_3_ptm recycle=2 plddt=0.604 ptm=0.450 rmsd_tol=1.952\n",
            "cluster=9 seed=0 model=model_3_ptm recycle=3 plddt=0.601 ptm=0.438 rmsd_tol=2.223\n",
            "cluster=9 seed=0 model=model_4_ptm recycle=0 plddt=0.620 ptm=0.522\n",
            "cluster=9 seed=0 model=model_4_ptm recycle=1 plddt=0.615 ptm=0.470 rmsd_tol=2.468\n",
            "cluster=9 seed=0 model=model_4_ptm recycle=2 plddt=0.615 ptm=0.476 rmsd_tol=1.466\n",
            "cluster=9 seed=0 model=model_4_ptm recycle=3 plddt=0.617 ptm=0.486 rmsd_tol=3.181\n",
            "cluster=9 seed=0 model=model_5_ptm recycle=0 plddt=0.632 ptm=0.544\n",
            "cluster=9 seed=0 model=model_5_ptm recycle=1 plddt=0.597 ptm=0.489 rmsd_tol=4.093\n",
            "cluster=9 seed=0 model=model_5_ptm recycle=2 plddt=0.608 ptm=0.486 rmsd_tol=2.009\n",
            "cluster=9 seed=0 model=model_5_ptm recycle=3 plddt=0.606 ptm=0.486 rmsd_tol=0.718\n",
            "cluster=10 seed=0 model=model_1_ptm recycle=0 plddt=0.516 ptm=0.429\n",
            "cluster=10 seed=0 model=model_1_ptm recycle=1 plddt=0.505 ptm=0.430 rmsd_tol=3.266\n",
            "cluster=10 seed=0 model=model_1_ptm recycle=2 plddt=0.519 ptm=0.436 rmsd_tol=1.379\n",
            "cluster=10 seed=0 model=model_1_ptm recycle=3 plddt=0.519 ptm=0.433 rmsd_tol=1.912\n",
            "cluster=10 seed=0 model=model_2_ptm recycle=0 plddt=0.549 ptm=0.474\n",
            "cluster=10 seed=0 model=model_2_ptm recycle=1 plddt=0.739 ptm=0.665 rmsd_tol=10.773\n",
            "cluster=10 seed=0 model=model_2_ptm recycle=2 plddt=0.746 ptm=0.671 rmsd_tol=1.469\n",
            "cluster=10 seed=0 model=model_2_ptm recycle=3 plddt=0.742 ptm=0.664 rmsd_tol=0.888\n",
            "cluster=10 seed=0 model=model_3_ptm recycle=0 plddt=0.580 ptm=0.455\n",
            "cluster=10 seed=0 model=model_3_ptm recycle=1 plddt=0.574 ptm=0.448 rmsd_tol=7.828\n",
            "cluster=10 seed=0 model=model_3_ptm recycle=2 plddt=0.701 ptm=0.590 rmsd_tol=5.068\n",
            "cluster=10 seed=0 model=model_3_ptm recycle=3 plddt=0.729 ptm=0.630 rmsd_tol=2.131\n",
            "cluster=10 seed=0 model=model_4_ptm recycle=0 plddt=0.552 ptm=0.460\n",
            "cluster=10 seed=0 model=model_4_ptm recycle=1 plddt=0.568 ptm=0.473 rmsd_tol=2.247\n",
            "cluster=10 seed=0 model=model_4_ptm recycle=2 plddt=0.710 ptm=0.638 rmsd_tol=5.363\n",
            "cluster=10 seed=0 model=model_4_ptm recycle=3 plddt=0.726 ptm=0.651 rmsd_tol=7.827\n",
            "cluster=10 seed=0 model=model_5_ptm recycle=0 plddt=0.479 ptm=0.369\n",
            "cluster=10 seed=0 model=model_5_ptm recycle=1 plddt=0.553 ptm=0.469 rmsd_tol=2.567\n",
            "cluster=10 seed=0 model=model_5_ptm recycle=2 plddt=0.665 ptm=0.595 rmsd_tol=8.166\n",
            "cluster=10 seed=0 model=model_5_ptm recycle=3 plddt=0.703 ptm=0.625 rmsd_tol=6.647\n",
            "cluster=11 seed=0 model=model_1_ptm recycle=0 plddt=0.634 ptm=0.529\n",
            "cluster=11 seed=0 model=model_1_ptm recycle=1 plddt=0.557 ptm=0.465 rmsd_tol=6.042\n",
            "cluster=11 seed=0 model=model_1_ptm recycle=2 plddt=0.571 ptm=0.481 rmsd_tol=3.627\n",
            "cluster=11 seed=0 model=model_1_ptm recycle=3 plddt=0.604 ptm=0.511 rmsd_tol=2.190\n",
            "cluster=11 seed=0 model=model_2_ptm recycle=0 plddt=0.589 ptm=0.502\n",
            "cluster=11 seed=0 model=model_2_ptm recycle=1 plddt=0.583 ptm=0.480 rmsd_tol=5.261\n",
            "cluster=11 seed=0 model=model_2_ptm recycle=2 plddt=0.612 ptm=0.521 rmsd_tol=3.263\n",
            "cluster=11 seed=0 model=model_2_ptm recycle=3 plddt=0.593 ptm=0.506 rmsd_tol=1.469\n",
            "cluster=11 seed=0 model=model_3_ptm recycle=0 plddt=0.616 ptm=0.499\n",
            "cluster=11 seed=0 model=model_3_ptm recycle=1 plddt=0.632 ptm=0.534 rmsd_tol=7.789\n",
            "cluster=11 seed=0 model=model_3_ptm recycle=2 plddt=0.740 ptm=0.649 rmsd_tol=6.158\n",
            "cluster=11 seed=0 model=model_3_ptm recycle=3 plddt=0.773 ptm=0.688 rmsd_tol=1.685\n",
            "cluster=11 seed=0 model=model_4_ptm recycle=0 plddt=0.560 ptm=0.437\n",
            "cluster=11 seed=0 model=model_4_ptm recycle=1 plddt=0.617 ptm=0.542 rmsd_tol=9.086\n",
            "cluster=11 seed=0 model=model_4_ptm recycle=2 plddt=0.699 ptm=0.620 rmsd_tol=3.009\n",
            "cluster=11 seed=0 model=model_4_ptm recycle=3 plddt=0.764 ptm=0.691 rmsd_tol=1.352\n",
            "cluster=11 seed=0 model=model_5_ptm recycle=0 plddt=0.590 ptm=0.506\n",
            "cluster=11 seed=0 model=model_5_ptm recycle=1 plddt=0.764 ptm=0.708 rmsd_tol=8.779\n",
            "cluster=11 seed=0 model=model_5_ptm recycle=2 plddt=0.718 ptm=0.664 rmsd_tol=5.143\n",
            "cluster=11 seed=0 model=model_5_ptm recycle=3 plddt=0.739 ptm=0.681 rmsd_tol=6.014\n",
            "cluster=12 seed=0 model=model_1_ptm recycle=0 plddt=0.464 ptm=0.365\n",
            "cluster=12 seed=0 model=model_1_ptm recycle=1 plddt=0.463 ptm=0.379 rmsd_tol=2.427\n",
            "cluster=12 seed=0 model=model_1_ptm recycle=2 plddt=0.478 ptm=0.390 rmsd_tol=1.229\n",
            "cluster=12 seed=0 model=model_1_ptm recycle=3 plddt=0.472 ptm=0.390 rmsd_tol=0.679\n",
            "cluster=12 seed=0 model=model_2_ptm recycle=0 plddt=0.427 ptm=0.331\n",
            "cluster=12 seed=0 model=model_2_ptm recycle=1 plddt=0.489 ptm=0.413 rmsd_tol=4.944\n",
            "cluster=12 seed=0 model=model_2_ptm recycle=2 plddt=0.470 ptm=0.368 rmsd_tol=2.598\n",
            "cluster=12 seed=0 model=model_2_ptm recycle=3 plddt=0.484 ptm=0.388 rmsd_tol=2.377\n",
            "cluster=12 seed=0 model=model_3_ptm recycle=0 plddt=0.483 ptm=0.347\n",
            "cluster=12 seed=0 model=model_3_ptm recycle=1 plddt=0.499 ptm=0.366 rmsd_tol=3.234\n",
            "cluster=12 seed=0 model=model_3_ptm recycle=2 plddt=0.509 ptm=0.372 rmsd_tol=1.576\n",
            "cluster=12 seed=0 model=model_3_ptm recycle=3 plddt=0.517 ptm=0.390 rmsd_tol=1.596\n",
            "cluster=12 seed=0 model=model_4_ptm recycle=0 plddt=0.472 ptm=0.357\n",
            "cluster=12 seed=0 model=model_4_ptm recycle=1 plddt=0.484 ptm=0.389 rmsd_tol=1.993\n",
            "cluster=12 seed=0 model=model_4_ptm recycle=2 plddt=0.494 ptm=0.386 rmsd_tol=2.156\n",
            "cluster=12 seed=0 model=model_4_ptm recycle=3 plddt=0.485 ptm=0.384 rmsd_tol=0.792\n",
            "cluster=12 seed=0 model=model_5_ptm recycle=0 plddt=0.531 ptm=0.461\n",
            "cluster=12 seed=0 model=model_5_ptm recycle=1 plddt=0.491 ptm=0.386 rmsd_tol=6.751\n",
            "cluster=12 seed=0 model=model_5_ptm recycle=2 plddt=0.498 ptm=0.394 rmsd_tol=2.388\n",
            "cluster=12 seed=0 model=model_5_ptm recycle=3 plddt=0.507 ptm=0.393 rmsd_tol=2.592\n",
            "cluster=13 seed=0 model=model_1_ptm recycle=0 plddt=0.515 ptm=0.401\n",
            "cluster=13 seed=0 model=model_1_ptm recycle=1 plddt=0.512 ptm=0.395 rmsd_tol=1.907\n",
            "cluster=13 seed=0 model=model_1_ptm recycle=2 plddt=0.532 ptm=0.421 rmsd_tol=1.333\n",
            "cluster=13 seed=0 model=model_1_ptm recycle=3 plddt=0.552 ptm=0.438 rmsd_tol=1.582\n",
            "cluster=13 seed=0 model=model_2_ptm recycle=0 plddt=0.553 ptm=0.422\n",
            "cluster=13 seed=0 model=model_2_ptm recycle=1 plddt=0.584 ptm=0.453 rmsd_tol=3.102\n",
            "cluster=13 seed=0 model=model_2_ptm recycle=2 plddt=0.596 ptm=0.476 rmsd_tol=1.304\n",
            "cluster=13 seed=0 model=model_2_ptm recycle=3 plddt=0.608 ptm=0.505 rmsd_tol=1.773\n",
            "cluster=13 seed=0 model=model_3_ptm recycle=0 plddt=0.522 ptm=0.380\n",
            "cluster=13 seed=0 model=model_3_ptm recycle=1 plddt=0.571 ptm=0.442 rmsd_tol=1.770\n",
            "cluster=13 seed=0 model=model_3_ptm recycle=2 plddt=0.565 ptm=0.427 rmsd_tol=1.222\n",
            "cluster=13 seed=0 model=model_3_ptm recycle=3 plddt=0.550 ptm=0.412 rmsd_tol=0.690\n",
            "cluster=13 seed=0 model=model_4_ptm recycle=0 plddt=0.538 ptm=0.418\n",
            "cluster=13 seed=0 model=model_4_ptm recycle=1 plddt=0.553 ptm=0.420 rmsd_tol=3.179\n",
            "cluster=13 seed=0 model=model_4_ptm recycle=2 plddt=0.550 ptm=0.412 rmsd_tol=0.610\n",
            "cluster=13 seed=0 model=model_4_ptm recycle=3 plddt=0.552 ptm=0.419 rmsd_tol=0.532\n",
            "cluster=13 seed=0 model=model_5_ptm recycle=0 plddt=0.587 ptm=0.458\n",
            "cluster=13 seed=0 model=model_5_ptm recycle=1 plddt=0.653 ptm=0.552 rmsd_tol=2.795\n",
            "cluster=13 seed=0 model=model_5_ptm recycle=2 plddt=0.711 ptm=0.610 rmsd_tol=0.904\n",
            "cluster=13 seed=0 model=model_5_ptm recycle=3 plddt=0.716 ptm=0.617 rmsd_tol=2.707\n",
            "cluster=14 seed=0 model=model_1_ptm recycle=0 plddt=0.623 ptm=0.465\n",
            "cluster=14 seed=0 model=model_1_ptm recycle=1 plddt=0.758 ptm=0.605 rmsd_tol=2.429\n",
            "cluster=14 seed=0 model=model_1_ptm recycle=2 plddt=0.751 ptm=0.606 rmsd_tol=2.170\n",
            "cluster=14 seed=0 model=model_1_ptm recycle=3 plddt=0.751 ptm=0.603 rmsd_tol=0.685\n",
            "cluster=14 seed=0 model=model_2_ptm recycle=0 plddt=0.562 ptm=0.454\n",
            "cluster=14 seed=0 model=model_2_ptm recycle=1 plddt=0.667 ptm=0.545 rmsd_tol=1.403\n",
            "cluster=14 seed=0 model=model_2_ptm recycle=2 plddt=0.742 ptm=0.624 rmsd_tol=1.129\n",
            "cluster=14 seed=0 model=model_2_ptm recycle=3 plddt=0.720 ptm=0.597 rmsd_tol=1.321\n",
            "cluster=14 seed=0 model=model_3_ptm recycle=0 plddt=0.668 ptm=0.508\n",
            "cluster=14 seed=0 model=model_3_ptm recycle=1 plddt=0.764 ptm=0.623 rmsd_tol=1.418\n",
            "cluster=14 seed=0 model=model_3_ptm recycle=2 plddt=0.777 ptm=0.634 rmsd_tol=1.095\n",
            "cluster=14 seed=0 model=model_3_ptm recycle=3 plddt=0.779 ptm=0.632 rmsd_tol=0.919\n",
            "cluster=14 seed=0 model=model_4_ptm recycle=0 plddt=0.744 ptm=0.622\n",
            "cluster=14 seed=0 model=model_4_ptm recycle=1 plddt=0.780 ptm=0.663 rmsd_tol=1.295\n",
            "cluster=14 seed=0 model=model_4_ptm recycle=2 plddt=0.765 ptm=0.640 rmsd_tol=1.139\n",
            "cluster=14 seed=0 model=model_4_ptm recycle=3 plddt=0.765 ptm=0.637 rmsd_tol=1.379\n",
            "cluster=14 seed=0 model=model_5_ptm recycle=0 plddt=0.653 ptm=0.562\n",
            "cluster=14 seed=0 model=model_5_ptm recycle=1 plddt=0.791 ptm=0.674 rmsd_tol=1.133\n",
            "cluster=14 seed=0 model=model_5_ptm recycle=2 plddt=0.758 ptm=0.644 rmsd_tol=1.031\n",
            "cluster=14 seed=0 model=model_5_ptm recycle=3 plddt=0.755 ptm=0.635 rmsd_tol=0.450\n",
            "cluster=15 seed=0 model=model_1_ptm recycle=0 plddt=0.666 ptm=0.570\n",
            "cluster=15 seed=0 model=model_1_ptm recycle=1 plddt=0.764 ptm=0.673 rmsd_tol=4.802\n",
            "cluster=15 seed=0 model=model_1_ptm recycle=2 plddt=0.764 ptm=0.669 rmsd_tol=3.054\n",
            "cluster=15 seed=0 model=model_1_ptm recycle=3 plddt=0.767 ptm=0.666 rmsd_tol=1.384\n",
            "cluster=15 seed=0 model=model_2_ptm recycle=0 plddt=0.518 ptm=0.420\n",
            "cluster=15 seed=0 model=model_2_ptm recycle=1 plddt=0.558 ptm=0.442 rmsd_tol=3.911\n",
            "cluster=15 seed=0 model=model_2_ptm recycle=2 plddt=0.556 ptm=0.444 rmsd_tol=1.676\n",
            "cluster=15 seed=0 model=model_2_ptm recycle=3 plddt=0.530 ptm=0.437 rmsd_tol=1.069\n",
            "cluster=15 seed=0 model=model_3_ptm recycle=0 plddt=0.508 ptm=0.378\n",
            "cluster=15 seed=0 model=model_3_ptm recycle=1 plddt=0.560 ptm=0.432 rmsd_tol=7.114\n",
            "cluster=15 seed=0 model=model_3_ptm recycle=2 plddt=0.764 ptm=0.679 rmsd_tol=8.279\n",
            "cluster=15 seed=0 model=model_3_ptm recycle=3 plddt=0.838 ptm=0.749 rmsd_tol=4.696\n",
            "cluster=15 seed=0 model=model_4_ptm recycle=0 plddt=0.545 ptm=0.426\n",
            "cluster=15 seed=0 model=model_4_ptm recycle=1 plddt=0.521 ptm=0.424 rmsd_tol=3.290\n",
            "cluster=15 seed=0 model=model_4_ptm recycle=2 plddt=0.700 ptm=0.629 rmsd_tol=11.274\n",
            "cluster=15 seed=0 model=model_4_ptm recycle=3 plddt=0.780 ptm=0.701 rmsd_tol=5.481\n",
            "cluster=15 seed=0 model=model_5_ptm recycle=0 plddt=0.587 ptm=0.508\n",
            "cluster=15 seed=0 model=model_5_ptm recycle=1 plddt=0.779 ptm=0.710 rmsd_tol=9.565\n",
            "cluster=15 seed=0 model=model_5_ptm recycle=2 plddt=0.811 ptm=0.732 rmsd_tol=2.353\n",
            "cluster=15 seed=0 model=model_5_ptm recycle=3 plddt=0.809 ptm=0.731 rmsd_tol=1.020\n",
            "cluster=16 seed=0 model=model_1_ptm recycle=0 plddt=0.531 ptm=0.445\n",
            "cluster=16 seed=0 model=model_1_ptm recycle=1 plddt=0.544 ptm=0.465 rmsd_tol=7.905\n",
            "cluster=16 seed=0 model=model_1_ptm recycle=2 plddt=0.561 ptm=0.474 rmsd_tol=3.625\n",
            "cluster=16 seed=0 model=model_1_ptm recycle=3 plddt=0.549 ptm=0.456 rmsd_tol=1.831\n",
            "cluster=16 seed=0 model=model_2_ptm recycle=0 plddt=0.520 ptm=0.427\n",
            "cluster=16 seed=0 model=model_2_ptm recycle=1 plddt=0.512 ptm=0.433 rmsd_tol=7.468\n",
            "cluster=16 seed=0 model=model_2_ptm recycle=2 plddt=0.539 ptm=0.439 rmsd_tol=6.822\n",
            "cluster=16 seed=0 model=model_2_ptm recycle=3 plddt=0.557 ptm=0.431 rmsd_tol=2.214\n",
            "cluster=16 seed=0 model=model_3_ptm recycle=0 plddt=0.579 ptm=0.456\n",
            "cluster=16 seed=0 model=model_3_ptm recycle=1 plddt=0.578 ptm=0.447 rmsd_tol=7.208\n",
            "cluster=16 seed=0 model=model_3_ptm recycle=2 plddt=0.633 ptm=0.507 rmsd_tol=2.762\n",
            "cluster=16 seed=0 model=model_3_ptm recycle=3 plddt=0.639 ptm=0.514 rmsd_tol=1.907\n",
            "cluster=16 seed=0 model=model_4_ptm recycle=0 plddt=0.537 ptm=0.430\n",
            "cluster=16 seed=0 model=model_4_ptm recycle=1 plddt=0.522 ptm=0.418 rmsd_tol=3.570\n",
            "cluster=16 seed=0 model=model_4_ptm recycle=2 plddt=0.500 ptm=0.411 rmsd_tol=7.436\n",
            "cluster=16 seed=0 model=model_4_ptm recycle=3 plddt=0.489 ptm=0.375 rmsd_tol=7.024\n",
            "cluster=16 seed=0 model=model_5_ptm recycle=0 plddt=0.501 ptm=0.424\n",
            "cluster=16 seed=0 model=model_5_ptm recycle=1 plddt=0.585 ptm=0.496 rmsd_tol=9.287\n",
            "cluster=16 seed=0 model=model_5_ptm recycle=2 plddt=0.698 ptm=0.598 rmsd_tol=5.924\n",
            "cluster=16 seed=0 model=model_5_ptm recycle=3 plddt=0.802 ptm=0.720 rmsd_tol=4.579\n",
            "cluster=17 seed=0 model=model_1_ptm recycle=0 plddt=0.517 ptm=0.417\n",
            "cluster=17 seed=0 model=model_1_ptm recycle=1 plddt=0.519 ptm=0.432 rmsd_tol=2.815\n",
            "cluster=17 seed=0 model=model_1_ptm recycle=2 plddt=0.530 ptm=0.443 rmsd_tol=1.239\n",
            "cluster=17 seed=0 model=model_1_ptm recycle=3 plddt=0.533 ptm=0.444 rmsd_tol=0.354\n",
            "cluster=17 seed=0 model=model_2_ptm recycle=0 plddt=0.518 ptm=0.435\n",
            "cluster=17 seed=0 model=model_2_ptm recycle=1 plddt=0.531 ptm=0.450 rmsd_tol=3.379\n",
            "cluster=17 seed=0 model=model_2_ptm recycle=2 plddt=0.548 ptm=0.471 rmsd_tol=2.755\n",
            "cluster=17 seed=0 model=model_2_ptm recycle=3 plddt=0.545 ptm=0.460 rmsd_tol=1.094\n",
            "cluster=17 seed=0 model=model_3_ptm recycle=0 plddt=0.536 ptm=0.401\n",
            "cluster=17 seed=0 model=model_3_ptm recycle=1 plddt=0.539 ptm=0.388 rmsd_tol=1.105\n",
            "cluster=17 seed=0 model=model_3_ptm recycle=2 plddt=0.536 ptm=0.378 rmsd_tol=0.699\n",
            "cluster=17 seed=0 model=model_3_ptm recycle=3 plddt=0.523 ptm=0.372 rmsd_tol=0.728\n",
            "cluster=17 seed=0 model=model_4_ptm recycle=0 plddt=0.553 ptm=0.443\n",
            "cluster=17 seed=0 model=model_4_ptm recycle=1 plddt=0.547 ptm=0.433 rmsd_tol=1.472\n",
            "cluster=17 seed=0 model=model_4_ptm recycle=2 plddt=0.528 ptm=0.413 rmsd_tol=0.516\n",
            "cluster=17 seed=0 model=model_4_ptm recycle=3 plddt=0.537 ptm=0.424 rmsd_tol=1.439\n",
            "cluster=17 seed=0 model=model_5_ptm recycle=0 plddt=0.530 ptm=0.416\n",
            "cluster=17 seed=0 model=model_5_ptm recycle=1 plddt=0.513 ptm=0.411 rmsd_tol=3.082\n",
            "cluster=17 seed=0 model=model_5_ptm recycle=2 plddt=0.516 ptm=0.406 rmsd_tol=3.647\n",
            "cluster=17 seed=0 model=model_5_ptm recycle=3 plddt=0.511 ptm=0.400 rmsd_tol=1.360\n",
            "cluster=18 seed=0 model=model_1_ptm recycle=0 plddt=0.564 ptm=0.486\n",
            "cluster=18 seed=0 model=model_1_ptm recycle=1 plddt=0.693 ptm=0.612 rmsd_tol=5.198\n",
            "cluster=18 seed=0 model=model_1_ptm recycle=2 plddt=0.743 ptm=0.661 rmsd_tol=3.597\n",
            "cluster=18 seed=0 model=model_1_ptm recycle=3 plddt=0.776 ptm=0.684 rmsd_tol=1.843\n",
            "cluster=18 seed=0 model=model_2_ptm recycle=0 plddt=0.601 ptm=0.512\n",
            "cluster=18 seed=0 model=model_2_ptm recycle=1 plddt=0.761 ptm=0.680 rmsd_tol=3.766\n",
            "cluster=18 seed=0 model=model_2_ptm recycle=2 plddt=0.774 ptm=0.696 rmsd_tol=3.056\n",
            "cluster=18 seed=0 model=model_2_ptm recycle=3 plddt=0.823 ptm=0.739 rmsd_tol=2.437\n",
            "cluster=18 seed=0 model=model_3_ptm recycle=0 plddt=0.608 ptm=0.494\n",
            "cluster=18 seed=0 model=model_3_ptm recycle=1 plddt=0.719 ptm=0.615 rmsd_tol=6.074\n",
            "cluster=18 seed=0 model=model_3_ptm recycle=2 plddt=0.725 ptm=0.630 rmsd_tol=1.892\n",
            "cluster=18 seed=0 model=model_3_ptm recycle=3 plddt=0.794 ptm=0.695 rmsd_tol=1.247\n",
            "cluster=18 seed=0 model=model_4_ptm recycle=0 plddt=0.566 ptm=0.445\n",
            "cluster=18 seed=0 model=model_4_ptm recycle=1 plddt=0.580 ptm=0.492 rmsd_tol=3.308\n",
            "cluster=18 seed=0 model=model_4_ptm recycle=2 plddt=0.593 ptm=0.515 rmsd_tol=3.509\n",
            "cluster=18 seed=0 model=model_4_ptm recycle=3 plddt=0.657 ptm=0.553 rmsd_tol=2.582\n",
            "cluster=18 seed=0 model=model_5_ptm recycle=0 plddt=0.546 ptm=0.461\n",
            "cluster=18 seed=0 model=model_5_ptm recycle=1 plddt=0.571 ptm=0.460 rmsd_tol=2.619\n",
            "cluster=18 seed=0 model=model_5_ptm recycle=2 plddt=0.590 ptm=0.493 rmsd_tol=5.422\n",
            "cluster=18 seed=0 model=model_5_ptm recycle=3 plddt=0.783 ptm=0.711 rmsd_tol=3.780\n",
            "GC 45\n",
            "CPU times: user 5min 39s, sys: 8.14 s, total: 5min 47s\n",
            "Wall time: 6min 27s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Setup code to compare to experimental\n",
        "#@markdown\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",
        "  elif os.path.isfile(pdb_code):\n",
        "    return pdb_code\n",
        "  elif len(pdb_code) == 4:\n",
        "    os.makedirs(\"tmp\",exist_ok=True)\n",
        "    os.system(f\"wget -qnc https://files.rcsb.org/download/{pdb_code}.pdb -P tmp/\")\n",
        "    return f\"tmp/{pdb_code}.pdb\"\n",
        "  else:\n",
        "    os.makedirs(\"tmp\",exist_ok=True)\n",
        "    os.system(f\"wget -qnc https://alphafold.ebi.ac.uk/files/AF-{pdb_code}-F1-model_v4.pdb -P tmp/\")\n",
        "    return f\"tmp/AF-{pdb_code}-F1-model_v4.pdb\"\n",
        "\n",
        "def get_pdb_and_chain(pdb_code=\"\",chain=\"A\",len=None,offset=None):\n",
        "  get_pdb(pdb_code)\n",
        "  from colabdesign.shared.protein import pdb_to_string, renum_pdb_str\n",
        "  string = pdb_to_string('tmp/{0}.pdb'.format(pdb_code),chains=chain,models=1)\n",
        "  if offset is not None:\n",
        "    string = renum_pdb_str(string,[len],offset=offset)\n",
        "  else:\n",
        "    string = renum_pdb_str(string,[len])\n",
        "\n",
        "  with open('tmp/{0}{1}.pdb'.format(pdb_code,chain),'w') as f:\n",
        "    f.write(string)\n",
        "\n",
        "if not os.path.exists('TMscore_cpp'):\n",
        "  os.system('wget https://zhanggroup.org/TM-score/TMscore_cpp.gz')\n",
        "  os.system('gunzip TMscore_cpp.gz')\n",
        "  os.system('chmod +x TMscore_cpp')\n",
        "\n",
        "\"\"\"\n",
        "Compute TM Scores between two PDBs and parse outputs\n",
        "pdb_pred -- The path to the predicted PDB\n",
        "pdb_native -- The path to the native PDB\n",
        "test_len -- run asserts that the input and output should have the same length\n",
        "\"\"\"\n",
        "import subprocess\n",
        "\n",
        "tm_re = re.compile(r'TM-score[\\s]*=[\\s]*(\\d.\\d+)')\n",
        "ref_len_re = re.compile(r'Length=[\\s]*(\\d+)[\\s]*\\(by which all scores are normalized\\)')\n",
        "common_re = re.compile(r'Number of residues in common=[\\s]*(\\d+)')\n",
        "super_re = re.compile(r'\\(\":\" denotes the residue pairs of distance < 5\\.0 Angstrom\\)\\\\n([A-Z\\-]+)\\\\n[\" \", :]+\\\\n([A-Z\\-]+)\\\\n')\n",
        "\n",
        "def compute_tmscore(pdb_pred, pdb_native, test_len=False):\n",
        "  cmd = (['./TMscore_cpp', pdb_pred, pdb_native])\n",
        "  tmscore_output = str(subprocess.check_output(cmd))\n",
        "  try:\n",
        "    tm_out = float(tm_re.search(tmscore_output).group(1))\n",
        "    reflen = int(ref_len_re.search(tmscore_output).group(1))\n",
        "    common = int(common_re.search(tmscore_output).group(1))\n",
        "\n",
        "    seq1 = super_re.search(tmscore_output).group(1)\n",
        "    seq2 = super_re.search(tmscore_output).group(1)\n",
        "  except Exception as e:\n",
        "    print(\"Failed on: \" + \" \".join(cmd))\n",
        "    raise e\n",
        "\n",
        "  if test_len:\n",
        "    assert reflen == common, cmd\n",
        "    assert seq1 == seq2, cmd\n",
        "    assert len(seq1) == reflen, cmd\n",
        "\n",
        "  return tm_out"
      ],
      "metadata": {
        "cellView": "form",
        "id": "tzZYwYV9VQjr"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "get_pdb_and_chain('2QKE',chain='E',len=104)\n",
        "get_pdb_and_chain('5JYT',chain='A',len=106, offset=-3) # TM-score needs numbering being the same"
      ],
      "metadata": {
        "id": "rOO9n3YDRJFZ"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_tmscore_(row, native_pdb):\n",
        "  return compute_tmscore(row['pdb_path'],native_pdb)\n",
        "\n",
        "outputs['TMscore_2QKEE'] = outputs.apply(lambda row: compute_tmscore_(row, 'tmp/2QKEE.pdb'), axis=1)\n",
        "outputs['TMscore_5JYTA'] = outputs.apply(lambda row: compute_tmscore_(row, 'tmp/5JYTA.pdb'), axis=1)"
      ],
      "metadata": {
        "id": "DN5Cmq3bbfni"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "outputs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "LOuk3I0kkrD5",
        "outputId": "8f878f1f-d868-47b2-d7c8-6389cd023d2c"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        plddt       ptm                                           pdb_path  \\\n",
              "0    0.592678  0.435655  KaiBTE_13036/pdb/model_1_ptm_cluster0_r3_seed0...   \n",
              "1    0.592678  0.435655  KaiBTE_13036/pdb/model_1_ptm_cluster0_r3_seed0...   \n",
              "2    0.592678  0.435655  KaiBTE_13036/pdb/model_1_ptm_cluster0_r3_seed0...   \n",
              "3    0.592678  0.435655  KaiBTE_13036/pdb/model_1_ptm_cluster0_r3_seed0...   \n",
              "4    0.589853  0.453781  KaiBTE_13036/pdb/model_2_ptm_cluster0_r3_seed0...   \n",
              "..        ...       ...                                                ...   \n",
              "368  0.656659  0.552536  KaiBTE_13036/pdb/model_4_ptm_cluster18_r3_seed...   \n",
              "369  0.782608  0.711065  KaiBTE_13036/pdb/model_5_ptm_cluster18_r3_seed...   \n",
              "370  0.782608  0.711065  KaiBTE_13036/pdb/model_5_ptm_cluster18_r3_seed...   \n",
              "371  0.782608  0.711065  KaiBTE_13036/pdb/model_5_ptm_cluster18_r3_seed...   \n",
              "372  0.782608  0.711065  KaiBTE_13036/pdb/model_5_ptm_cluster18_r3_seed...   \n",
              "\n",
              "           model  cluster  cluster_size  TMscore_2QKEE  TMscore_5JYTA  \n",
              "0    model_1_ptm        0            52         0.6499         0.4407  \n",
              "1    model_1_ptm        0            52         0.6499         0.4407  \n",
              "2    model_1_ptm        0            52         0.6499         0.4407  \n",
              "3    model_1_ptm        0            52         0.6499         0.4407  \n",
              "4    model_2_ptm        0            52         0.7554         0.4610  \n",
              "..           ...      ...           ...            ...            ...  \n",
              "368  model_4_ptm       18            14         0.4728         0.6835  \n",
              "369  model_5_ptm       18            14         0.4694         0.7256  \n",
              "370  model_5_ptm       18            14         0.4694         0.7256  \n",
              "371  model_5_ptm       18            14         0.4694         0.7256  \n",
              "372  model_5_ptm       18            14         0.4694         0.7256  \n",
              "\n",
              "[373 rows x 8 columns]"
            ],
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              "\n",
              "  <div id=\"df-a22d360a-86f0-4bf9-b404-e58114f03a63\" class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>plddt</th>\n",
              "      <th>ptm</th>\n",
              "      <th>pdb_path</th>\n",
              "      <th>model</th>\n",
              "      <th>cluster</th>\n",
              "      <th>cluster_size</th>\n",
              "      <th>TMscore_2QKEE</th>\n",
              "      <th>TMscore_5JYTA</th>\n",
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              "      <th>1</th>\n",
              "      <td>0.592678</td>\n",
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              "      <th>2</th>\n",
              "      <td>0.592678</td>\n",
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              "      <td>KaiBTE_13036/pdb/model_1_ptm_cluster0_r3_seed0...</td>\n",
              "      <td>model_1_ptm</td>\n",
              "      <td>0</td>\n",
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              "      <td>0.6499</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.592678</td>\n",
              "      <td>0.435655</td>\n",
              "      <td>KaiBTE_13036/pdb/model_1_ptm_cluster0_r3_seed0...</td>\n",
              "      <td>model_1_ptm</td>\n",
              "      <td>0</td>\n",
              "      <td>52</td>\n",
              "      <td>0.6499</td>\n",
              "      <td>0.4407</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.589853</td>\n",
              "      <td>0.453781</td>\n",
              "      <td>KaiBTE_13036/pdb/model_2_ptm_cluster0_r3_seed0...</td>\n",
              "      <td>model_2_ptm</td>\n",
              "      <td>0</td>\n",
              "      <td>52</td>\n",
              "      <td>0.7554</td>\n",
              "      <td>0.4610</td>\n",
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              "    <tr>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>368</th>\n",
              "      <td>0.656659</td>\n",
              "      <td>0.552536</td>\n",
              "      <td>KaiBTE_13036/pdb/model_4_ptm_cluster18_r3_seed...</td>\n",
              "      <td>model_4_ptm</td>\n",
              "      <td>18</td>\n",
              "      <td>14</td>\n",
              "      <td>0.4728</td>\n",
              "      <td>0.6835</td>\n",
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              "      <td>model_5_ptm</td>\n",
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              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a22d360a-86f0-4bf9-b404-e58114f03a63 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-a22d360a-86f0-4bf9-b404-e58114f03a63');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
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              "    width: 32px;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "    box-shadow: none;\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-92b9f43d-7b74-4d32-950b-bb0edcae8d3c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
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              "\n",
              "  <div id=\"id_1c5b8bea-f492-4e6f-a949-56ea02dc5150\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('outputs')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "        document.querySelector('#id_1c5b8bea-f492-4e6f-a949-56ea02dc5150 button.colab-df-generate');\n",
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              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('outputs');\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "outputs",
              "summary": "{\n  \"name\": \"outputs\",\n  \"rows\": 373,\n  \"fields\": [\n    {\n      \"column\": \"plddt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.113030250044557,\n        \"min\": 0.4718754291534424,\n        \"max\": 0.8382444679737091,\n        \"num_unique_values\": 95,\n        \"samples\": [\n          0.5519037842750549,\n          0.8163257837295532,\n          0.7791501134634018\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ptm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1271122693389404,\n        \"min\": 0.35698172450065613,\n        \"max\": 0.7490108609199524,\n        \"num_unique_values\": 95,\n        \"samples\": [\n          0.4190443158149719,\n          0.7365415096282959,\n          0.6324594020843506\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pdb_path\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 95,\n        \"samples\": [\n          \"KaiBTE_13036/pdb/model_4_ptm_cluster13_r3_seed0.pdb\",\n          \"KaiBTE_13036/pdb/model_3_ptm_cluster4_r3_seed0.pdb\",\n          \"KaiBTE_13036/pdb/model_3_ptm_cluster14_r3_seed0.pdb\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"model_2_ptm\",\n          \"model_5_ptm\",\n          \"model_3_ptm\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cluster\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 0,\n        \"max\": 18,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          0,\n          5,\n          11\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cluster_size\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 340,\n        \"min\": 9,\n        \"max\": 1533,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          16,\n          9,\n          52\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TMscore_2QKEE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.12845311295654913,\n        \"min\": 0.3858,\n        \"max\": 0.8623,\n        \"num_unique_values\": 92,\n        \"samples\": [\n          0.4767,\n          0.4432,\n          0.4521\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TMscore_5JYTA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.11226816379123288,\n        \"min\": 0.4184,\n        \"max\": 0.8284,\n        \"num_unique_values\": 93,\n        \"samples\": [\n          0.6591,\n          0.7662,\n          0.6594\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px\n",
        "\n",
        "# Create a scatter plot with hover labels\n",
        "fig = px.scatter(outputs, x='TMscore_2QKEE', y='TMscore_5JYTA', color='plddt',\n",
        "                 color_continuous_scale='rainbow_r',range_color=[0.5,0.9],\n",
        "                 width=400,height=400, template=\"simple_white\",hover_data=[\"pdb_path\",\"cluster_size\"])\n",
        "\n",
        "# Show the plot\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "EJz43t9zN28Q",
        "outputId": "e1121c73-e8a6-4502-b30a-19b4626bf242"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "                            \n",
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              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Download prediction\n",
        "\n",
        "#@markdown Once this cell has been executed, a zip-archive with\n",
        "#@markdown the obtained prediction will be automatically downloaded\n",
        "#@markdown to your computer.\n",
        "\n",
        "# add settings file\n",
        "settings_path = f\"{jobname}/settings.txt\"\n",
        "with open(settings_path, \"w\") as text_file:\n",
        "  if \"input_opts\" in dir():\n",
        "    for k,v in input_opts.items():\n",
        "      text_file.write(f\"{k}={v}\\n\")\n",
        "  if \"model_opts\" in dir():\n",
        "    for k,v in model_opts.items():\n",
        "      text_file.write(f\"{k}={v}\\n\")\n",
        "  if \"run_opts\" in dir():\n",
        "    for k,v in run_opts.items():\n",
        "      text_file.write(f\"{k}={v}\\n\")\n",
        "# --- Download the predictions ---\n",
        "os.system(f\"zip -r {jobname}.zip {jobname}\")\n",
        "files.download(f'{jobname}.zip')"
      ],
      "metadata": {
        "id": "GzFUdn9gFZgo",
        "outputId": "1f543cf2-17c6-49bc-e5a0-e1e3c5e52bcf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "cellView": "form"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_ec0ebe63-eb0d-44fe-b864-5f75fe618fad\", \"KaiBTE_13036.zip\", 7358596)"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}