{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "provenance": [], "gpuType": "T4" }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" }, "accelerator": "GPU", "gpuClass": "standard" }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "74RP4ByMFXHR" }, "source": [ "#**Protein-protein complex structure prediction**\n", "This notebook provides a general protein-protein complex structure prediction service utilizing RoseTTAFold2 and MiniWorld. It also offers specialized epitope-guided prediction for antigen-antibody complexes.\n", "\n", "\n", "#### **Tips and Instructions**\n", "- click the little ▶ play icon to the left of each cell to execute it.\n", "- use \":\" to specify multimeric inputs (e.g. sequence=\"AAA:BBB\" for a two chain complex)\n", "- Refer to the detailed instructions provided in each cell for guidance.\n" ] }, { "cell_type": "code", "source": [ "%%time\n", "#@title ##**Setup the system** (1~2m)##\n", "#@markdown ####**Choose model parameters for prediction.**##\n", "#@markdown - **RF2_apr23**: Use this model for general protein-protein complex structure prediction\n", "#@markdown - **RF2_abag**: Use this model for epitope-guided **antibody-antigen** complex structure prediction\n", "\n", "params = \"RF2_abag\" # @param [\"RF2_apr23\",\"RF2_abag\"]\n", "\n", "import os, time, sys\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"max_split_size_mb:512\"\n", "\n", "if params == \"RF2_apr23\" and not os.path.isfile(f\"{params}.tgz\"):\n", " # send param download into background\n", " print (\"Download model weights\")\n", " os.system(\"(apt-get install aria2; aria2c -q -x 16 https://files.ipd.uw.edu/dimaio/RF2_apr23.tgz) &\")\n", "\n", "if params == \"RF2_abag\" and not os.path.isfile(f\"{params}.tgz\"):\n", " # send param download into background\n", " print (\"Download model weights\")\n", " os.system(\"pip install gdown; gdown --id 18ZNdC9hHEGdSpJYfWKaAeqEMd9P3vTzl\")\n", "\n", "\n", "if not os.path.isdir(\"iitp-RF2\"):\n", " print(\"Install RoseTTAFold2 & MiniWorld packages\")\n", " os.system(\"git clone https://github.com/SNU-CSSB/iitp-RF2.git\")\n", " # add codes to overwrite embedding module.\n", " os.system(\"pip install py3Dmol\")\n", "\n", " # 17Mar2024: adding --no-dependencies to avoid installing nvidia-cuda-* dependencies\n", " os.system(\"pip install --no-dependencies dgl==2.0.0 -f https://data.dgl.ai/wheels/cu121/repo.html\")\n", " os.system(\"pip install --no-dependencies e3nn==0.3.3 opt_einsum_fx\")\n", " os.system(\"cd iitp-RF2/SE3Transformer; pip install .\")\n", "\n", " os.system(\"wget https://raw.githubusercontent.com/sokrypton/ColabFold/main/colabfold/colabfold.py -O colabfold_utils.py\")\n", "\n", " #os.system(\"pip install dgl -f https://data.dgl.ai/wheels/cu121/repo.html\")\n", " #os.system(\"cd RoseTTAFold2/SE3Transformer; pip -q install --no-cache-dir -r requirements.txt; pip -q install .\")\n", " #os.system(\"wget https://raw.githubusercontent.com/sokrypton/ColabFold/beta/colabfold/mmseqs/api.py\")\n", "\n", " # install hhsuite\n", " print(\"Install 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", "if not os.path.isfile(f\"{params}.pt\"):\n", " time.sleep(5)\n", "\n", "if os.path.isfile(f\"{params}.tgz.aria2\"):\n", " print(\"Still downloading model weights\")\n", " while os.path.isfile(f\"{params}.tgz.aria2\"):\n", " time.sleep(5)\n", "\n", "\n", "if not os.path.isfile(f\"{params}.pt\"):\n", " os.system(f\"tar -zxvf {params}.tgz\")\n", " if params == \"RF2_apr23\":\n", " os.system(f\"mv weights/{params}.pt .\")\n", "\n", "if not \"IMPORTED\" in dir():\n", " if 'iitp-RF2/network' not in sys.path:\n", " os.environ[\"DGLBACKEND\"] = \"pytorch\"\n", " sys.path.append('iitp-RF2/network')\n", " if \"hhsuite\" not in os.environ['PATH']:\n", " os.environ['PATH'] += \":hhsuite/bin:hhsuite/scripts\"\n", "\n", " import matplotlib.pyplot as plt\n", " from google.colab import files\n", " import numpy as np\n", " from parsers import parse_a3m\n", " #from api import run_mmseqs2\n", " from colabfold_utils import run_mmseqs2\n", " import py3Dmol\n", " import torch\n", " from string import ascii_uppercase, ascii_lowercase\n", " import hashlib, re, os\n", " import random\n", "\n", " def get_hash(x): return hashlib.sha1(x.encode()).hexdigest()\n", " alphabet_list = list(ascii_uppercase+ascii_lowercase)\n", " from collections import OrderedDict, Counter\n", "\n", " IMPORTED = True\n", "\n", "if not \"pred\" in dir() or params_sele != params:\n", " from predict import Predictor\n", " print(\"Compile the network\")\n", "\n", " if (torch.cuda.is_available()):\n", " pred = Predictor(f\"{params}.pt\", torch.device(\"cuda:0\"))\n", " else:\n", " print (\"WARNING: using CPU\")\n", " pred = Predictor(f\"{params}.pt\", torch.device(\"cpu\"))\n", " params_sele = params\n", "\n", "def get_unique_sequences(seq_list):\n", " unique_seqs = list(OrderedDict.fromkeys(seq_list))\n", " return unique_seqs\n", "\n", "def run_mmseqs2_wrapper(*args, **kwargs):\n", " kwargs['user_agent'] = \"colabfold/rosettafold2\"\n", " return run_mmseqs2(*args, **kwargs)\n", "\n", "def get_msa(seq, jobname, cov=50, id=90, max_msa=2048,\n", " mode=\"unpaired_paired\"):\n", "\n", " assert mode in [\"unpaired\",\"paired\",\"unpaired_paired\"]\n", " seqs = [seq] if isinstance(seq,str) else seq\n", "\n", " # collapse homooligomeric sequences\n", " counts = Counter(seqs)\n", " u_seqs = list(counts.keys())\n", " u_nums = list(counts.values())\n", "\n", " # expand homooligomeric sequences\n", " first_seq = \"/\".join(sum([[x]*n for x,n in zip(u_seqs,u_nums)],[]))\n", " msa = [first_seq]\n", "\n", " path = os.path.join(jobname,\"msa\")\n", " os.makedirs(path, exist_ok=True)\n", " if mode in [\"paired\",\"unpaired_paired\"] and len(u_seqs) > 1:\n", " print(\"getting paired MSA\")\n", " out_paired = run_mmseqs2_wrapper(u_seqs, f\"{path}/\", use_pairing=True)\n", " headers, sequences = [],[]\n", " for a3m_lines in out_paired:\n", " n = -1\n", " for line in a3m_lines.split(\"\\n\"):\n", " if len(line) > 0:\n", " if line.startswith(\">\"):\n", " n += 1\n", " if len(headers) < (n + 1):\n", " headers.append([])\n", " sequences.append([])\n", " headers[n].append(line)\n", " else:\n", " sequences[n].append(line)\n", " # filter MSA\n", " with open(f\"{path}/paired_in.a3m\",\"w\") as handle:\n", " for n,sequence in enumerate(sequences):\n", " handle.write(f\">n{n}\\n{''.join(sequence)}\\n\")\n", " os.system(f\"hhfilter -i {path}/paired_in.a3m -id {id} -cov {cov} -o {path}/paired_out.a3m\")\n", " with open(f\"{path}/paired_out.a3m\",\"r\") as handle:\n", " for line in handle:\n", " if line.startswith(\">\"):\n", " n = int(line[2:])\n", " xs = sequences[n]\n", " # expand homooligomeric sequences\n", " xs = ['/'.join([x]*num) for x,num in zip(xs,u_nums)]\n", " msa.append('/'.join(xs))\n", "\n", " if len(msa) < max_msa and (mode in [\"unpaired\",\"unpaired_paired\"] or len(u_seqs) == 1):\n", " print(\"getting unpaired MSA\")\n", " out = run_mmseqs2_wrapper(u_seqs,f\"{path}/\")\n", " Ls = [len(seq) for seq in u_seqs]\n", " sub_idx = []\n", " sub_msa = []\n", " sub_msa_num = 0\n", " for n,a3m_lines in enumerate(out):\n", " sub_msa.append([])\n", " with open(f\"{path}/in_{n}.a3m\",\"w\") as handle:\n", " handle.write(a3m_lines)\n", " # filter\n", " os.system(f\"hhfilter -i {path}/in_{n}.a3m -id {id} -cov {cov} -o {path}/out_{n}.a3m\")\n", " with open(f\"{path}/out_{n}.a3m\",\"r\") as handle:\n", " for line in handle:\n", " if not line.startswith(\">\"):\n", " xs = ['-'*l for l in Ls]\n", " xs[n] = line.rstrip()\n", " # expand homooligomeric sequences\n", " xs = ['/'.join([x]*num) for x,num in zip(xs,u_nums)]\n", " sub_msa[-1].append('/'.join(xs))\n", " sub_msa_num += 1\n", " sub_idx.append(list(range(len(sub_msa[-1]))))\n", "\n", " while len(msa) < max_msa and sub_msa_num > 0:\n", " for n in range(len(sub_idx)):\n", " if len(sub_idx[n]) > 0:\n", " msa.append(sub_msa[n][sub_idx[n].pop(0)])\n", " sub_msa_num -= 1\n", " if len(msa) == max_msa:\n", " break\n", "\n", " with open(f\"{jobname}/msa.a3m\",\"w\") as handle:\n", " for n,sequence in enumerate(msa):\n", " handle.write(f\">n{n}\\n{sequence}\\n\")\n" ], "metadata": { "id": "ymCHu14w17wF", "cellView": "form", "outputId": "cfc1954a-02c7-4368-c56a-afcb4f434ed4", "colab": { "base_uri": "https://localhost:8080/" } }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Download model weights\n", "Install RoseTTAFold2 & MiniWorld packages\n", "Install hhsuite\n", "Compile the network\n", "CPU times: user 14.2 s, sys: 1.88 s, total: 16 s\n", "Wall time: 1min 55s\n" ] } ] }, { "cell_type": "code", "source": [ "#@title ##**Input protein sequences**\n", "\n", "#@markdown ####**Job name for the prediction run**\n", "#@markdown - Provide a unique name for your prediction job. This name will help you identify and organize your results. Use a descriptive label, such as `ProteinA_ProteinB_Prediction` or `Antibody_Antigen_Run1`, for easy reference.\n", "jobname = \"EGFR_pantitumumab\" #@param {type:\"string\"}\n", "\n", "#@markdown ####**Sequence input**\n", "# @markdown - Use `:` to indicate **inter-protein chain breaks** when modeling complexes. This supports both **homo-oligomers** and **hetero-oligomers**.\n", "# @markdown\n", "# @markdown *Example* : `sequence=\"AAA:BBB\"` for a two-chain complex.\n", "# @markdown - For **antibody-antigen complexes**, provide the sequences in the following order:\n", "# @markdown 1. Antibody heavy chain\n", "# @markdown 2. Antibody light chain\n", "# @markdown 3. Antigen chain(s)\n", "\n", "sequence = \"QVQLQESGPGLVKPSETLSLTCTVSGGSVSSGDYYWTWIRQSPGKGLEWIGHIYYSGNTNYNPSLKSRLTISIDTSKTQFSLKLSSVTAADTAIYYCVRDRVTGAFDIWGQGTMVTVSS:DIQMTQSPSSLSASVGDRVTITCQASQDISNYLNWYQQKPGKAPKLLIYDASNLETGVPSRFSGSGSGTDFTFTISSLQPEDIATYFCQHFDHLPLAFGGGTKVEIK:LEEKKVCNGIGIGEFKDSLSIDATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLDITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSHHHHHH\" #@param {type:\"string\"}\n", "\n", "msa_method = \"mmseqs2\"\n", "collapse_identical = True" ], "metadata": { "cellView": "form", "id": "Eh48KV70rQ03" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "#@title ##**Additional inputs for antibody-antigen complex structure prediction (only for RF2_abag)**\n", "from google.colab import files\n", "\n", "# @markdown #### **Individual Structural Models for Antibody and Antigen**\n", "# @markdown - File upload will be activated when you run this cell.\n", "\n", "# ab_pdb 업로드\n", "print(\"Please upload the PDB file for antibody structure:\")\n", "ab_pdb = files.upload()\n", "ab_pdb = os.path.abspath(list(ab_pdb.keys())[0])\n", "\n", "# ag_pdb 업로드\n", "print(\"\\nPlease upload the PDB file for antigen structure:\")\n", "ag_pdb = files.upload()\n", "ag_pdb = os.path.abspath(list(ag_pdb.keys())[0])\n", "\n", "\n", "# 업로드된 파일 확인\n", "print(\"\\nUploaded ab_pdb file:\", ab_pdb)\n", "print(\"Uploaded ag_pdb file:\", ag_pdb)\n", "\n", "#@markdown #### **Epitope Residue Information (on Antigen)**\n", "#@markdown - Specify the epitope residues in the format `A123,A152,A178`,\n", "#@markdown where `A` represents the chain identifier, and the number represents the residue number in the provided antigen PDB file.\n", "#@markdown - Ensure the chain and residue numbers match the antigen PDB structure you uploaded.\n", "#@markdown - Accurate epitope information is crucial for precise antibody-antigen complex predictions.\n", "epitope_residue = \"A132\" #@param {type:\"string\"}\n", "epitope_residue = epitope_residue.split(\",\")" ], "metadata": { "id": "-_fLVLjXIpaw", "cellView": "form", "colab": { "base_uri": "https://localhost:8080/", "height": 231 }, "outputId": "77ae8941-a016-42f0-b3ea-736d5ae4ece1" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Please upload the PDB file for antibody structure:\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " \n", " Upload widget is only available when the cell has been executed in the\n", " current browser session. Please rerun this cell to enable.\n", " \n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Saving ab_block.pdb to ab_block.pdb\n", "\n", "Please upload the PDB file for antigen structure:\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " \n", " Upload widget is only available when the cell has been executed in the\n", " current browser session. Please rerun this cell to enable.\n", " \n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Saving ag_block.pdb to ag_block.pdb\n", "\n", "Uploaded ab_pdb file: /content/ab_block.pdb\n", "Uploaded ag_pdb file: /content/ag_block.pdb\n" ] } ] }, { "cell_type": "code", "source": [ "#@title ##**Run predictions**\n", "\n", "# @markdown #### **Hidden Parameters**\n", "# @markdown - This cell contains hidden parameters that you can modify for advanced customization.\n", "# @markdown - Click \"Show Code\" to view and adjust parameters such as symmetry options, sequence pairing modes, model recycling settings, and stochasticity controls.\n", "# @markdown - Modify these settings before running the cell by clicking the ▶ **play** icon.\n", "\n", "# hidden parameters you may want to change\n", "# Symmetry options for homo-oligomer complex structure prediction\n", "sym = \"X\" #please select among [\"X\",\"C\", \"D\", \"T\", \"I\", \"O\"]\n", "order = 1 #number of identical copies. e.g. C2 symmetry [\"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"10\", \"11\", \"12\"] {type:\"raw\"}\n", "msa_concat_mode = \"diag\" #Please select among [\"diag\", \"repeat\", \"default\"]\n", "\n", "# Sequence pairing modes.\n", "if \"abag\" in params:\n", " pair_mode = \"unpaired\" # You MUST use \"unpaired\" model for antibody-antigen complex sturcture prediction\n", "else:\n", " pair_mode = \"unpaired_paired\" #choose one from [\"unpaired_paired\",\"paired\",\"unpaired\"]\n", "\n", "# Recycling options. More recycles may improve the model quality, but it will become slower.\n", "num_recycles = 10 # Integer > 0\n", "\n", "# If you want to introduce some randomness, please modify below.\n", "use_mlm = False\n", "use_dropout = False\n", "max_msa = 256 #[16, 32, 64, 128, 256, 512] {type:\"raw\"}\n", "random_seed = 0 # {type:\"integer\"}\n", "num_models = 1 # [\"1\", \"5\", \"10\", \"15\", \"20\", \"25\"] {type:\"raw\"}\n", "max_extra_msa = max_msa * 8\n", "\n", "\n", "# Below is to run structure prediction. Please don't touch.\n", "sequence = re.sub(\"[^A-Z:]\", \"\", sequence.replace(\"/\",\":\").upper())\n", "sequence = re.sub(\":+\",\":\",sequence)\n", "sequence = re.sub(\"^[:]+\",\"\",sequence)\n", "sequence = re.sub(\"[:]+$\",\"\",sequence)\n", "\n", "if sym in [\"X\",\"C\"]:\n", " copies = order\n", "elif sym in [\"D\"]:\n", " copies = order * 2\n", "else:\n", " copies = {\"T\":12,\"O\":24,\"I\":60}[sym]\n", " order = \"\"\n", "symm = sym + str(order)\n", "\n", "sequences = sequence.replace(\":\",\"/\").split(\"/\")\n", "if collapse_identical:\n", " u_sequences = get_unique_sequences(sequences)\n", "else:\n", " u_sequences = sequences\n", "sequences = sum([u_sequences] * copies,[])\n", "lengths = [len(s) for s in sequences]\n", "\n", "subcrop = -1\n", "topk = 1536\n", "\n", "sequence = \"/\".join(sequences)\n", "jobname = jobname+\"_\"+symm+\"_\"+get_hash(sequence)[:5]\n", "\n", "print(f\"jobname: {jobname}\")\n", "print(f\"lengths: {lengths}\")\n", "\n", "os.makedirs(jobname, exist_ok=True)\n", "if msa_method == \"mmseqs2\":\n", " get_msa(u_sequences, jobname, mode=pair_mode, max_msa=max_extra_msa)\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", "\n", "best_plddt = None\n", "best_seed = None\n", "for seed in range(random_seed,random_seed+num_models):\n", " torch.manual_seed(seed)\n", " random.seed(seed)\n", " np.random.seed(seed)\n", " npz = f\"{jobname}/rf2_seed{seed}_00.npz\"\n", " if \"abag\" in params_sele:\n", " print (\"Input epitopes\", epitope_residue)\n", " pred.predict_abag(msa=f\"{jobname}/msa.a3m\",\n", " ab_pdb=ab_pdb, ag_pdb=ag_pdb,\n", " epi_s=epitope_residue,\n", " out_prefix=f\"{jobname}/rf2_seed{seed}\",\n", " n_recycles=num_recycles,\n", " msa_mask=0.15 if use_mlm else 0.0,\n", " msa_concat_mode=msa_concat_mode,\n", " nseqs=max_msa,\n", " nseqs_full=max_extra_msa,\n", " subcrop=subcrop,\n", " topk=topk,\n", " is_training=use_dropout)\n", " else:\n", " pred.predict(inputs=[f\"{jobname}/msa.a3m\"],\n", " out_prefix=f\"{jobname}/rf2_seed{seed}\",\n", " symm=symm,\n", " ffdb=None, #TODO (templates),\n", " n_recycles=num_recycles,\n", " msa_mask=0.15 if use_mlm else 0.0,\n", " msa_concat_mode=msa_concat_mode,\n", " nseqs=max_msa,\n", " nseqs_full=max_extra_msa,\n", " subcrop=subcrop,\n", " topk=topk,\n", " is_training=use_dropout)\n", " plddt = np.load(npz)[\"lddt\"].mean()\n", " if best_plddt is None or plddt > best_plddt:\n", " best_plddt = plddt\n", " best_seed = seed" ], "metadata": { "id": "L236-CN-HACN", "cellView": "form", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "9f54fa2a-b971-4694-c430-518dc662f2e8" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "jobname: EGFR_pantitumumab_X1_902ca_X1_902ca_X1_902ca\n", "lengths: [119, 107, 201]\n", "getting unpaired MSA\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "COMPLETE: 100%|██████████| 450/450 [elapsed: 00:02 remaining: 00:00]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Input epitopes ['A132']\n", "N=2048 L=427\n", "recycle 0 plddt 0.804 pae 12.445 rmsd 16.619\n", "recycle 1 plddt 0.847 pae 8.391 rmsd 1.914\n", "Updated epitope: 78,101,102,103,104,105,106,109,111,112,130,131,132,133,134,135,154,155,157,158,159,160,161,162\n", "recycle 2 plddt 0.859 pae 6.273 rmsd 0.563\n", "Updated epitope: 78,101,102,103,104,105,106,109,111,112,130,131,132,133,134,135,137,154,155,157,158,159,160,161,162\n", "recycle 3 plddt 0.868 pae 5.281 rmsd 0.982\n", "Updated epitope: 76,78,102,103,104,105,106,109,111,112,130,132,133,134,135,137,155,157,158,159,160,161,162\n", "recycle 4 plddt 0.866 pae 5.621 rmsd 0.288\n", "Updated epitope: 76,78,102,103,104,105,106,109,111,112,130,131,132,133,134,135,137,155,157,158,159,160,161,162\n", "recycle 5 plddt 0.870 pae 5.184 rmsd 0.210\n", "Updated epitope: 76,78,102,103,104,105,106,109,111,112,130,131,132,133,134,135,137,155,157,158,159,160,161,162\n", "recycle 6 plddt 0.863 pae 5.562 rmsd 0.248\n", "Updated epitope: 76,78,102,103,104,105,106,109,111,112,130,132,133,134,135,137,155,157,158,159,160,161,162\n", "recycle 7 plddt 0.865 pae 5.441 rmsd 0.112\n", "Updated epitope: 78,102,103,104,105,106,109,111,112,130,132,133,134,135,137,155,157,158,159,160,161,162\n", "recycle 8 plddt 0.863 pae 5.516 rmsd 0.230\n", "Updated epitope: 78,102,103,104,105,106,109,111,112,130,132,133,134,135,137,155,157,158,159,160,161,162\n", "recycle 9 plddt 0.861 pae 5.656 rmsd 0.210\n", "Updated epitope: 78,102,103,104,105,106,109,111,112,130,132,133,134,135,137,155,157,158,159,160,161,162\n", "recycle 10 plddt 0.862 pae 5.742 rmsd 0.215\n", "Updated epitope: 78,102,103,104,105,106,109,111,112,130,132,133,134,135,137,155,157,158,159,160,161,162\n", "runtime=150.95 vram=3.03\n" ] } ] }, { "cell_type": "code", "source": [ "#@title ##**Display 3D structure** {run: \"auto\"}\n", "os.system(\"pip install py3Dmol\")\n", "\n", "color = \"plddt\" #@param [\"plddt\",\"chain\",\"rainbow\"]\n", "import py3Dmol\n", "import matplotlib\n", "from string import ascii_uppercase,ascii_lowercase\n", "alphabet_list = list(ascii_uppercase+ascii_lowercase)\n", "pymol_color_list = [\"#33ff33\",\"#00ffff\",\"#ff33cc\",\"#ffff00\",\"#ff9999\",\"#e5e5e5\",\"#7f7fff\",\"#ff7f00\",\n", " \"#7fff7f\",\"#199999\",\"#ff007f\",\"#ffdd5e\",\"#8c3f99\",\"#b2b2b2\",\"#007fff\",\"#c4b200\",\n", " \"#8cb266\",\"#00bfbf\",\"#b27f7f\",\"#fcd1a5\",\"#ff7f7f\",\"#ffbfdd\",\"#7fffff\",\"#ffff7f\",\n", " \"#00ff7f\",\"#337fcc\",\"#d8337f\",\"#bfff3f\",\"#ff7fff\",\"#d8d8ff\",\"#3fffbf\",\"#b78c4c\",\n", " \"#339933\",\"#66b2b2\",\"#ba8c84\",\"#84bf00\",\"#b24c66\",\"#7f7f7f\",\"#3f3fa5\",\"#a5512b\"]\n", "pymol_cmap = matplotlib.colors.ListedColormap(pymol_color_list)\n", "\n", "def plot_pdb(pdb, color=\"plddt\"):\n", " hbondCutoff = 4.0\n", " view = py3Dmol.view(js='https://3dmol.org/build/3Dmol.js')\n", " pdb_str = open(pdb,'r').read()\n", " view.addModel(pdb_str,'pdb',{'hbondCutoff':hbondCutoff})\n", " if color == \"rainbow\":\n", " view.setStyle({'cartoon': {'color':'spectrum'}})\n", " elif color == \"chain\":\n", " for n,chain,c in zip(range(len(lengths)),alphabet_list,pymol_color_list):\n", " view.setStyle({'chain':chain},{'cartoon': {'color':c}})\n", " else:\n", " view.setStyle({'cartoon': {'colorscheme': {'prop':'b','gradient': 'roygb','min':50,'max':90}}})\n", " view.zoomTo()\n", " view.show()\n", "\n", "plot_pdb(f\"{jobname}/rf2_seed{best_seed}_00_pred.pdb\", color=color)\n", "output = dict(np.load(f\"{jobname}/rf2_seed{best_seed}_00.npz\"))\n", "plt.figure(figsize=(10,5))\n", "plt.subplot(1,2,1)\n", "plt.title(\"predicted LDDT\")\n", "plt.plot(output[\"lddt\"])\n", "plt.ylim(0,1.0)\n", "plt.subplot(1,2,2)\n", "plt.title(\"predicted alignment error\")\n", "plt.imshow(output[\"pae\"],vmin=0,vmax=30,cmap=\"bwr\")\n", "plt.show()" ], "metadata": { "id": "3m0H-yCIrpc4", "cellView": "form", "colab": { "base_uri": "https://localhost:8080/", "height": 948 }, "outputId": "4b347e06-666e-4c96-d401-2069f19e5fc3" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/3dmoljs_load.v0": "
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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "#@title ##**Evaluate prediction based on DockQ**\n", "\n", "os.system(\"pip install DockQ\")\n", "pdb_code = \"5sx4\" #@param {type:\"string\"}\n", "#@markdown - PDB code for the ground truth structure deposited in PDB database.\n", "\n", "#@markdown #### **Metrics used for evaluation**\n", "#@markdown - **`DockQ`**: Docking quality score ranging from 0 to 1 (higher is better).\n", "#@markdown - **`iRMSD`**: Interface Root Mean Square Deviation (Å) for interface residues.\n", "#@markdown - **`LRMSD`**: Ligand Root Mean Square Deviation (Å) for the entire ligand.\n", "#@markdown - **`fnat`**: Fraction of native contacts (range 0-1, higher indicates more correct contacts).\n", "\n", "if not os.path.isfile(f\"{pdb_code}.pdb1\"):\n", " os.system(f\"wget -qnc https://files.rcsb.org/download/{pdb_code}.pdb1.gz\")\n", " os.system(f\"gunzip {pdb_code}.pdb1.gz\")\n", "nat_fn = f\"{pdb_code}.pdb1\"\n", "\n", "pdb_fn = f\"{jobname}/rf2_seed{best_seed}_00_pred.pdb\"\n", "lines = os.popen(f\"DockQ {pdb_fn} {nat_fn} --short --allowed_mismatches 10\").readlines()[-1]\n", "\n", "import pandas as pd\n", "\n", "# Parsing the string into a dictionary\n", "data_list = lines.split()[:8]\n", "metrics = data_list[0::2] # Extract metric names (even indices)\n", "values = data_list[1::2] # Extract corresponding values (odd indices)\n", "\n", "# Convert values to numeric types where possible\n", "values = [float(value) if '.' in value else int(value) for value in values]\n", "\n", "# Create a DataFrame\n", "data = {\"Metric\": metrics, \"Value\": values}\n", "df = pd.DataFrame(data)\n", "\n", "# Display the table in Google Colab\n", "df.style.set_properties(**{'text-align': 'center'})\n" ], "metadata": { "cellView": "form", "id": "R2N9pK1hU53x", "colab": { "base_uri": "https://localhost:8080/", "height": 175 }, "outputId": "06168522-1bbc-4411-a2e0-1a5003f7b025" }, "execution_count": null, "outputs": [ { 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 MetricValue
0DockQ0.387000
1iRMSD3.357000
2LRMSD11.013000
3fnat0.622000
\n" ] }, "metadata": {}, "execution_count": 11 } ] }, { "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", " text_file.write(f\"method=RoseTTAFold2\\n\")\n", " text_file.write(f\"params={params}\\n\")\n", " text_file.write(f\"sequence={sequence}\\n\")\n", " text_file.write(f\"sym={sym}\\n\")\n", " text_file.write(f\"order={order}\\n\")\n", " text_file.write(f\"msa_concat_mode={msa_concat_mode}\\n\")\n", " text_file.write(f\"collapse_identical={collapse_identical}\")\n", " text_file.write(f\"random_seed={random_seed}\\n\")\n", " text_file.write(f\"msa_method={msa_method}\\n\")\n", " text_file.write(f\"num_recycles={num_recycles}\\n\")\n", " text_file.write(f\"use_mlm={use_mlm}\\n\")\n", " text_file.write(f\"use_dropout={use_dropout}\\n\")\n", " text_file.write(f\"num_models={num_models}\\n\")\n", "\n", "# --- Download the predictions ---\n", "os.system(f\"zip -r {jobname}.zip {jobname}\")\n", "files.download(f'{jobname}.zip')" ], "metadata": { "cellView": "form", "id": "M21J9hQTLVhz" }, "execution_count": null, "outputs": [] } ] }