├── .gitignore ├── LICENSE ├── README.md ├── environment.yml ├── img ├── install.png ├── recording_v0.1.0.gif ├── recording_v0.1.0.mov └── screenshot_v0.1.0.png ├── pymolfold ├── __init__.py └── widget.ui ├── pymolfold_v0.2.0.zip ├── pymolfold_v0.3.0.zip ├── pymolfoldv0.4.0.zip └── tests ├── boltz_tests.ipynb ├── chai_tests.ipynb ├── esm3_tests.ipynb ├── example.fasta └── protenix_tests.ipynb /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | share/python-wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | MANIFEST 28 | 29 | # PyInstaller 30 | # Usually these files are written by a python script from a template 31 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 32 | *.manifest 33 | *.spec 34 | 35 | # Installer logs 36 | pip-log.txt 37 | pip-delete-this-directory.txt 38 | 39 | # Unit test / coverage reports 40 | htmlcov/ 41 | .tox/ 42 | .nox/ 43 | .coverage 44 | .coverage.* 45 | .cache 46 | nosetests.xml 47 | coverage.xml 48 | *.cover 49 | *.py,cover 50 | .hypothesis/ 51 | .pytest_cache/ 52 | cover/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | .pybuilder/ 76 | target/ 77 | 78 | # Jupyter Notebook 79 | .ipynb_checkpoints 80 | 81 | # IPython 82 | profile_default/ 83 | ipython_config.py 84 | 85 | # pyenv 86 | # For a library or package, you might want to ignore these files since the code is 87 | # intended to run in multiple environments; otherwise, check them in: 88 | # .python-version 89 | 90 | # pipenv 91 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 92 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 93 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 94 | # install all needed dependencies. 95 | #Pipfile.lock 96 | 97 | # poetry 98 | # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. 99 | # This is especially recommended for binary packages to ensure reproducibility, and is more 100 | # commonly ignored for libraries. 101 | # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control 102 | #poetry.lock 103 | 104 | # pdm 105 | # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. 106 | #pdm.lock 107 | # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it 108 | # in version control. 109 | # https://pdm.fming.dev/latest/usage/project/#working-with-version-control 110 | .pdm.toml 111 | .pdm-python 112 | .pdm-build/ 113 | 114 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm 115 | __pypackages__/ 116 | 117 | # Celery stuff 118 | celerybeat-schedule 119 | celerybeat.pid 120 | 121 | # SageMath parsed files 122 | *.sage.py 123 | 124 | # Environments 125 | .env 126 | .venv 127 | env/ 128 | venv/ 129 | ENV/ 130 | env.bak/ 131 | venv.bak/ 132 | 133 | # Spyder project settings 134 | .spyderproject 135 | .spyproject 136 | 137 | # Rope project settings 138 | .ropeproject 139 | 140 | # mkdocs documentation 141 | /site 142 | 143 | # mypy 144 | .mypy_cache/ 145 | .dmypy.json 146 | dmypy.json 147 | 148 | # Pyre type checker 149 | .pyre/ 150 | 151 | # pytype static type analyzer 152 | .pytype/ 153 | 154 | # Cython debug symbols 155 | cython_debug/ 156 | 157 | # PyCharm 158 | # JetBrains specific template is maintained in a separate JetBrains.gitignore that can 159 | # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore 160 | # and can be added to the global gitignore or merged into this file. 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If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # PyMOLfold 2 | Plugin for folding sequences directly in PyMOL using various models (ESM3, Boltz-1, and Chai-1). 3 | 4 |

Colby T. Ford, Ph.D.

5 | 6 | ![Plugin Demo](img/recording_v0.1.0.gif) 7 | 8 | ## Plugin Installation 9 | 10 | ### From Local Download 11 | 1. Download the *pymolfold.zip* file from this repository. 12 | - Latest package: [pymolfold_v0.3.0.zip](https://github.com/colbyford/PyMOLfold/raw/refs/heads/main/pymolfold_v0.3.0.zip) 13 | - Stable release: [pymolfold_v0.3.0.zip](https://github.com/colbyford/PyMOLfold/releases/download/v0.3.0/pymolfold_v0.3.0.zip) 14 | 2. Open PyMOL and in the menu bar, go to *Plugin* > *Plugin Manager*. 15 | 3. On the *Install New Plugin* tab, click the *Choose file...* button under "Install from local file". 16 | 4. Locate the .zip file and open it. 17 | 18 | ### From the Web 19 | 20 | 1. Open PyMOL and in the menu bar, go to *Plugin* > *Plugin Manager*. 21 | 22 | 2. On the *Install New Plugin* tab, under the *Install from PyuMOLWiki or any URL* section, paste in the URL of the release .zip file from GitHub. 23 | - `https://github.com/colbyford/PyMOLfold/releases/download/v0.2.0/pymolfold_v0.2.0.zip` 24 | ![Installation](img/install.png) 25 | 26 | 3. Click the *Fetch* button and follow the prompts to install the package. 27 | 28 | 29 | Once installed, navigate to *Plugin* > *PyMOLfold* in the menu bar. 30 | Then, in the dialog box, simply paste in your API key and amino acid sequence you wish to fold. 31 | 32 | > [!NOTE] 33 | > Note: This plugin does not include any of the model package(s) or weights. You'll need to install them into the PyMOL Conda environment. 34 | 35 | 36 | ## Package Installation 37 | 38 | You can install PyPI libraries from the PyMOL command line. You can also run the `pip` install commands from your normal terminal if you know the path to PyMOL's Python executable. Running from the terminal is a bit easier to troubleshoot any installation issues. To find the PyMOL Python executable, use the `sys` library as follows. 39 | Simply copy and paste these commands in the pymol command line interface terminal: 40 | 41 | ```python 42 | import sys 43 | print(sys.executable) 44 | ``` 45 | The output should be something like `/home//biotools/pymol/bin/python` on Linux or `/Applications/PyMOL.app/Contents/bin/python` on macOS. 46 | Then, to run a command in your normal terminal, simply point to the PyMOL Python executable. For example: 47 | 48 | ```bash 49 | /home//biotools/pymol/bin/python -m pip install 50 | ``` 51 | 52 | Here are the example install commands for the supported models: 53 | 54 | - ESM3: `pip install esm` 55 | - Boltz-1: `pip install boltz` 56 | - Chai-1: `pip install chai_lab==0.5.0` 57 | - Protenix: `pip install --extra-index-url=https://pypi.nvidia.com --trusted-host pypi.nvidia.com nvidia-cublas-cu12 deepspeed==0.14.4 protenix` 58 | 59 | > [!NOTE] 60 | > Note: To use an ESM3 model, you'll need an API key from [Evolutionary Scale Forge](https://forge.evolutionaryscale.ai/). Otherwise, the plugin can't download the folding model. 61 | 62 | ## Using Conda 63 | The version of PyMOL that can be downloaded from the [Schrödinger website](https://pymol.org/) is currently compiled with Python 3.10. On some operating systems, you may have some difficulties installing the various model packages with this Python version. 64 | 65 | To get around this, we can use [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html) to get PyMOL with a different version of Python that will *hopefully* work with your desired folding library. You can modify the provided [environment.yml](environment.yml) file to install the desired Python version + model package. (Note: You likely cannot install all of the model libraries at the same time as they have dependency conflicts.) 66 | 67 | ```bash 68 | conda env create --file environment.yml 69 | ## Go get a ☕️ as this will take a while. 70 | 71 | conda activate pymolfold 72 | pymol ## This will open the GUI 73 | 74 | # conda deactivate 75 | # conda env update --file environment.yml 76 | # conda remove -n pymolfold --all 77 | ``` 78 | 79 | ## Feature Roadmap 80 | 81 | - [ ] UI updates for model-specific controls 82 | - [ ] Support for multiple protein chains. 83 | - [X] Model support for Protenix model (in beta) 84 | - In v0.3.0 release (Credit @colbyford) 85 | - [X] Model support for Chai-1 (proteins and protein-ligand complexes) 86 | - In v0.2.5 release (Credit: @ullahsamee) 87 | - [X] UI controls for temperature, steps, etc. 88 | - In v0.2.0 release (Credit @colbyford) 89 | - [X] Model support for Boltz-1 (+ ligands) 90 | - In v0.2.0 release (Credit: @ullahsamee) 91 | - [X] UI to select different models. 92 | - In v0.1.0 release (Credit: @colbyford) 93 | - [X] Support for the latest ESM3 models. 94 | - In v0.1.0 release. (Credit: @colbyford) 95 | - [X] Basic folding with `esm3-small-2024-08`. 96 | - In initial release. (Credit: @colbyford) 97 | 98 | -------------------------------------------------------------------------------- /environment.yml: -------------------------------------------------------------------------------- 1 | # run: conda env create --file environment.yml 2 | name: pymolfold 3 | channels: 4 | - conda-forge 5 | - schrodinger 6 | dependencies: 7 | - python>=3.12 8 | - pip 9 | - pymol-bundle 10 | - pip: 11 | ## Uncomment the model you'd like to install and use 12 | ## Note: It's unlikely that you can install all models at once due to conflicting dependencies 13 | - esm 14 | # - boltz 15 | # - chai_lab 16 | # - git+https://github.com/chaidiscovery/chai-lab.git 17 | # - protenix -------------------------------------------------------------------------------- /img/install.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ullahsamee/PyMOLfold/4372d4c05a1315841390dca8460a2f2ea38b2030/img/install.png -------------------------------------------------------------------------------- /img/recording_v0.1.0.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ullahsamee/PyMOLfold/4372d4c05a1315841390dca8460a2f2ea38b2030/img/recording_v0.1.0.gif -------------------------------------------------------------------------------- /img/recording_v0.1.0.mov: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ullahsamee/PyMOLfold/4372d4c05a1315841390dca8460a2f2ea38b2030/img/recording_v0.1.0.mov -------------------------------------------------------------------------------- /img/screenshot_v0.1.0.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ullahsamee/PyMOLfold/4372d4c05a1315841390dca8460a2f2ea38b2030/img/screenshot_v0.1.0.png -------------------------------------------------------------------------------- /pymolfold/__init__.py: -------------------------------------------------------------------------------- 1 | ''' 2 | PyMOL Protein Folding Plugin 3 | 4 | By Colby T. Ford, Ph.D. 5 | License: GPLv3 6 | ''' 7 | 8 | from __future__ import absolute_import 9 | from __future__ import print_function 10 | import os, tempfile, random, string, sys, subprocess, json 11 | 12 | def __init_plugin__(app=None): 13 | ''' 14 | Add an entry to the PyMOL "Plugin" menu 15 | ''' 16 | from pymol.plugins import addmenuitemqt 17 | addmenuitemqt('PyMOLfold', run_plugin_gui) 18 | 19 | 20 | ## Global reference to avoid garbage collection of our dialog 21 | dialog = None 22 | 23 | 24 | def run_plugin_gui(): 25 | ''' 26 | Open the custom dialog 27 | ''' 28 | global dialog 29 | if dialog is None: 30 | dialog = make_dialog() 31 | dialog.show() 32 | 33 | 34 | ## Folding Functions 35 | 36 | ## ESM Folding 37 | def fold_esm(model_name:str, aa_sequence:str, temperature:float=0.7, num_steps:int=8, token:str=""): 38 | """ 39 | Protein folding using ESM models 40 | """ 41 | try: 42 | from esm.sdk import client 43 | from esm.sdk.api import ESMProtein, GenerationConfig 44 | except ModuleNotFoundError as e: 45 | raise Exception(f"esm module not found: {str(e)}") 46 | 47 | try: 48 | model = client(model=model_name, url="https://forge.evolutionaryscale.ai", token=token) 49 | except Exception as e: 50 | raise Exception(f"Error getting ESM model with token: {str(e)}") 51 | 52 | ## Generate the protein structure 53 | structure_prediction_config = GenerationConfig( 54 | track="structure", 55 | num_steps=num_steps, 56 | temperature=temperature, 57 | ) 58 | 59 | structure_prediction_prompt = ESMProtein(sequence=aa_sequence) 60 | 61 | structure_prediction = model.generate( 62 | structure_prediction_prompt, 63 | structure_prediction_config 64 | ) 65 | 66 | structure_prediction_chain = structure_prediction.to_protein_chain() 67 | 68 | pdb_string = structure_prediction_chain.to_pdb_string() 69 | 70 | ## Save the output PDB file temporarily 71 | with tempfile.NamedTemporaryFile(delete=False, suffix=".pdb") as temp_pdb: 72 | temp_pdb.write(pdb_string.encode()) 73 | temp_pdb_path = temp_pdb.name 74 | 75 | return temp_pdb_path 76 | 77 | ## Chai Folding 78 | def fold_chai(aa_sequence:str, ligand:str=None, ligand_type:str="smiles", num_trunk_recycles:int=3, num_diffn_timesteps:int=200, seed:int=1337): 79 | """ 80 | Protein folding using Chai models 81 | """ 82 | try: 83 | from chai_lab.chai1 import run_inference 84 | import torch 85 | except ModuleNotFoundError as e: 86 | raise Exception(f"chai_lab module not found: {str(e)}") 87 | 88 | ## Start building FASTA content 89 | fasta_content = f">A|protein|name=chain_A\n{aa_sequence}\n" 90 | 91 | ## Add ligand if provided 92 | if ligand and ligand_type: 93 | fasta_content += f">ligand|name=chain_B\n{ligand}\n" 94 | 95 | ## Create temp fasta file 96 | with tempfile.NamedTemporaryFile(delete=False, suffix=".fasta") as temp_fasta: 97 | temp_fasta.write(fasta_content.encode()) 98 | temp_fasta_path = temp_fasta.name 99 | 100 | ## Create temp output directory 101 | output_dir = tempfile.mkdtemp() 102 | 103 | ## Detect devices 104 | device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") 105 | 106 | ## Run inferencing 107 | candidates = run_inference( 108 | fasta_file=temp_fasta_path, 109 | output_dir=output_dir, 110 | num_trunk_recycles=num_trunk_recycles, 111 | num_diffn_timesteps=num_diffn_timesteps, 112 | seed=seed, 113 | device=device, 114 | use_esm_embeddings=True 115 | ) 116 | 117 | if not candidates.cif_paths: 118 | raise ValueError("No structure files were generated") 119 | 120 | return candidates.cif_paths[0] 121 | 122 | ## Boltz Folding 123 | def fold_boltz(aa_sequence:str, ligand:str=None, ligand_type:str=None, use_msa_server:bool=False, recycling_steps:int=3, sampling_steps:int=200): 124 | """ 125 | Protein folding using Boltz-1 model 126 | """ 127 | try: 128 | import boltz 129 | import torch 130 | except ModuleNotFoundError as e: 131 | raise Exception(f"Could not import required module: {str(e)}") 132 | 133 | ## Start building FASTA content 134 | fasta_content = f">A|protein|empty\n{aa_sequence}\n" 135 | 136 | ## Add ligand if provided 137 | if ligand and ligand_type: 138 | fasta_content += f">B|{ligand_type}|\n{ligand}\n" 139 | 140 | ## Create temp fasta file 141 | with tempfile.NamedTemporaryFile(delete=False, suffix=".fasta") as temp_fasta: 142 | temp_fasta.write(fasta_content.encode()) 143 | temp_fasta_path = temp_fasta.name 144 | temp_fasta_filename = os.path.basename(temp_fasta_path).replace(".fasta", "") 145 | 146 | ## Create temp output directory 147 | output_dir = tempfile.mkdtemp() 148 | 149 | ## Set device 150 | device = "gpu" if torch.cuda.is_available() else "cpu" 151 | 152 | try: 153 | ## Run boltz command 154 | cmd = [ 155 | "boltz", 156 | "predict", 157 | temp_fasta_path, 158 | "--out_dir", output_dir, 159 | "--accelerator", device, 160 | "--output_format", "pdb", 161 | "--recycling_steps", str(recycling_steps), 162 | "--sampling_steps", str(sampling_steps) 163 | ] 164 | 165 | print("Running Boltz with command:", " ".join(cmd)) 166 | 167 | if use_msa_server: 168 | cmd.append("--use_msa_server") 169 | 170 | result = subprocess.run(cmd, capture_output=True, text=True, check=True) 171 | 172 | except Exception as e: 173 | raise Exception(f"Error during structure prediction: {str(e)}") 174 | 175 | ## Get the path to the folded PDB file 176 | folded_pdb_path = os.path.join(output_dir, 177 | f"bolts_results_{temp_fasta_filename}", 178 | "predictions", 179 | temp_fasta_filename, 180 | f"{temp_fasta_filename}_model_0.pdb") 181 | 182 | if not os.path.exists(folded_pdb_path): 183 | raise Exception(f"Expected output file not found: {folded_pdb_path}") 184 | 185 | return folded_pdb_path 186 | 187 | def fold_protenix(aa_sequence:str, ligand:str=None, use_msa_server:bool=False, seed:int=1337): 188 | """ 189 | Protein folding using Protenix model 190 | """ 191 | try: 192 | import protenix 193 | import torch 194 | except ModuleNotFoundError as e: 195 | raise Exception(f"Could not import required module: {str(e)}") 196 | 197 | ## Build the JSON body 198 | json_content = [{"sequences": [ 199 | {"proteinChain": { 200 | "sequence": aa_sequence, 201 | "count": 1 202 | } 203 | } 204 | ], 205 | "name": "pymolfold" 206 | }] 207 | 208 | ## If ligand provided, add it to the JSON 209 | if ligand: 210 | ligand_dict = { 211 | "ligand": { 212 | "ligand": ligand, 213 | "count": 1 214 | } 215 | } 216 | json_content[0]["sequences"].append(ligand_dict) 217 | 218 | ## Create temp json file 219 | with tempfile.NamedTemporaryFile(delete=False, suffix=".json") as temp_json: 220 | temp_json.write(json.dumps(json_content).encode()) 221 | temp_json_path = temp_json.name 222 | 223 | ## Create temp output directory 224 | output_dir = tempfile.mkdtemp() 225 | 226 | try: 227 | ## Run protenix command 228 | cmd = [ 229 | "protenix", 230 | "predict", 231 | "--input", temp_json_path, 232 | "--out_dir", output_dir, 233 | "--seeds", str(seed) 234 | ] 235 | if use_msa_server: 236 | cmd.append("--use_msa_server") 237 | 238 | result = subprocess.run(cmd, capture_output=True, text=True, check=True) 239 | 240 | except Exception as e: 241 | raise Exception(f"Error during structure prediction: {str(e)}") 242 | 243 | ## Get the path to the folded CIF file 244 | folded_cif_path = os.path.join(output_dir, 245 | "pymolfold", 246 | str(seed), 247 | "pymolfold_1337_sample_0.cif") 248 | 249 | if not os.path.exists(folded_cif_path): 250 | raise Exception(f"Expected output file not found: {folded_cif_path}") 251 | 252 | return folded_cif_path 253 | 254 | def apply_alphafold_colors(object_name): 255 | """ 256 | Apply AlphaFold-style coloring to the structure 257 | Credit: Konstantin Korotkov 258 | """ 259 | from pymol import cmd 260 | 261 | ## Define AlphaFold colors 262 | cmd.set_color("n0", [0.051, 0.341, 0.827]) # High confidence (dark blue) 263 | cmd.set_color("n1", [0.416, 0.796, 0.945]) # Good confidence (light blue) 264 | cmd.set_color("n2", [0.996, 0.851, 0.212]) # Medium confidence (yellow) 265 | cmd.set_color("n3", [0.992, 0.490, 0.302]) # Low confidence (orange) 266 | 267 | ## Apply coloring based on B-factor ranges 268 | cmd.color("n0", f"{object_name} and b < 100") 269 | cmd.color("n1", f"{object_name} and b < 90") 270 | cmd.color("n2", f"{object_name} and b < 70") 271 | cmd.color("n3", f"{object_name} and b < 50") 272 | 273 | 274 | ## Main Dialog 275 | def make_dialog(): 276 | ## Entrypoint to the PyMOL API 277 | from pymol import cmd 278 | 279 | ## Pymol.Qt provides the PyQt5 interface, but may support PyQt4 280 | ## and/or PySide as well 281 | from pymol.Qt import QtWidgets 282 | from pymol.Qt.utils import loadUi 283 | 284 | ## Create a new Window 285 | dialog = QtWidgets.QDialog() 286 | 287 | # populate the Window from our *.ui file which was created with the Qt Designer 288 | uifile = os.path.join(os.path.dirname(__file__), 'widget.ui') 289 | form = loadUi(uifile, dialog) 290 | 291 | ## Callback for the "Fold" button 292 | def run(): 293 | 294 | ## Get basic form data 295 | model_name = form.input_list_models.currentText() 296 | aa_sequence = form.input_aa_seq.toPlainText() 297 | ligand_sequence = form.input_ligand.toPlainText().strip() 298 | ligand_type = form.input_ligand_type.currentText() if ligand_sequence else None 299 | seed = int(form.input_seed.text()) 300 | af_coloring = form.input_af_coloring.isChecked() 301 | 302 | if not aa_sequence: 303 | QtWidgets.QMessageBox.warning(form, "Error", "Please enter a valid amino acid sequence.") 304 | return 305 | 306 | try: 307 | if model_name.startswith("esm3"): 308 | ## ESM Parameters 309 | esm_token = form.input_esm_token.text() 310 | esm_temp = float(form.input_esm_temp.text()) 311 | esm_nsteps = int(form.input_esm_nsteps.text()) 312 | 313 | folded_pdb_path = fold_esm(model_name, 314 | aa_sequence, 315 | temperature=esm_temp, 316 | num_steps=esm_nsteps, 317 | token=esm_token) 318 | 319 | elif model_name == "boltz-1": 320 | ## Boltz Parameters 321 | boltz_recycling_steps = int(form.input_boltz_recycling_steps.text()) 322 | boltz_sampling_steps = int(form.input_boltz_sampling_steps.text()) 323 | boltz_use_msa_server = form.input_boltz_use_msa_server.isChecked() 324 | 325 | folded_pdb_path = fold_boltz(aa_sequence, 326 | ligand=ligand_sequence, 327 | ligand_type=ligand_type, 328 | use_msa_server=boltz_use_msa_server, 329 | recycling_steps=boltz_recycling_steps, 330 | sampling_steps=boltz_sampling_steps) 331 | 332 | elif model_name == "chai-1": 333 | ## Chai Parameters 334 | chai_recycling_steps = int(form.input_chai_recycling_steps.text()) 335 | chai_diffusion_steps = int(form.input_chai_diffusion_steps.text()) 336 | 337 | folded_pdb_path = fold_chai(aa_sequence, 338 | ligand=ligand_sequence, 339 | ligand_type=ligand_type, 340 | num_trunk_recycles=chai_recycling_steps, 341 | num_diffn_timesteps=chai_diffusion_steps, 342 | seed=seed) 343 | 344 | elif model_name == "protenix": 345 | ## Protenix Parameters 346 | protenix_use_msa = form.input_protenix_use_msa.isChecked() 347 | 348 | folded_pdb_path = fold_protenix(aa_sequence, 349 | ligand=ligand_sequence, 350 | ligand_type=ligand_type, 351 | use_msa_server=protenix_use_msa, 352 | seed=seed) 353 | 354 | else: 355 | QtWidgets.QMessageBox.critical(form, "Error", f"Not a supported model name: {str(model_name)}") 356 | return 357 | 358 | ## Load the folded structure into PyMOL 359 | if not folded_pdb_path: 360 | QtWidgets.QMessageBox.critical(form, "Error", "No folded structure was returned.") 361 | return 362 | 363 | ## Generate a unique object name 364 | object_name = f"folded_structure_{''.join(random.choices(string.ascii_lowercase + string.digits, k=3))}" 365 | cmd.load(folded_pdb_path, object_name) 366 | 367 | ## Apply AlphaFold-style coloring 368 | if af_coloring: 369 | apply_alphafold_colors(object_name) 370 | 371 | QtWidgets.QMessageBox.information(form, "Success", f"Sequence folded with {model_name} and structure loaded into PyMOL!") 372 | 373 | except Exception as e: 374 | QtWidgets.QMessageBox.critical(form, "Error", f"An error occurred: {str(e)}") 375 | 376 | ## Button callbacks 377 | form.button_fold.clicked.connect(run) 378 | form.button_close.clicked.connect(dialog.close) 379 | 380 | return dialog -------------------------------------------------------------------------------- /pymolfold/widget.ui: -------------------------------------------------------------------------------- 1 | 2 | 3 | Form 4 | 5 | 6 | Qt::NonModal 7 | 8 | 9 | true 10 | 11 | 12 | 13 | 0 14 | 0 15 | 490 16 | 733 17 | 18 | 19 | 20 | 21 | 0 22 | 0 23 | 24 | 25 | 26 | PyMOLfold Plugin 27 | 28 | 29 | 30 | 31 | 32 | background-color: #000;color:white; padding: 10px 33 | 34 | 35 | <html><head/><body><p align="center"><span style=" font-size:9pt; font-weight:600; color:#ffffff;">PyMOLfold Plugin</span></p><p align="center"><span style=" font-size:9pt; color:#ffffff;">Contribute: </span><a href="https://github.com/colbyford"><span style=" text-decoration: underline; color:#ffffff;">https://github.com/colbyford</span></a></p></body></html> 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 100 45 | 46 | 47 | 48 | esm3-small-2024-08 49 | 50 | 51 | 52 | 53 | esm3-open-2024-03 54 | 55 | 56 | 57 | 58 | esm3-medium-2024-08 59 | 60 | 61 | 62 | 63 | esm3-large-2024-03 64 | 65 | 66 | 67 | 68 | esm3-medium-multimer-2024-09 69 | 70 | 71 | 72 | 73 | boltz-1 74 | 75 | 76 | 77 | 78 | chai-1 79 | 80 | 81 | 82 | 83 | protenix 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 75 93 | true 94 | 95 | 96 | 97 | Settings 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | 75 106 | true 107 | 108 | 109 | 110 | Model Name 111 | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 75 119 | true 120 | 121 | 122 | 123 | Amino Acid Sequence 124 | 125 | 126 | 127 | 128 | 129 | 130 | QAbstractScrollArea::AdjustToContents 131 | 132 | 133 | FVNQHLCGSHLVEALYLVCGERGFFYTPKTRREAEDLQGSLQPLALEGSLQKRGIVEQCCTSICSLYQLENYCN 134 | 135 | 136 | 137 | 138 | 139 | 140 | 141 | ccd 142 | 143 | 144 | 145 | 146 | smiles 147 | 148 | 149 | 150 | 151 | 152 | 153 | 154 | QAbstractScrollArea::AdjustToContentsOnFirstShow 155 | 156 | 157 | Enter CCD code or SMILES string (optional) 158 | 159 | 160 | 161 | 162 | 163 | 164 | <html><head/><body><p><span style=" font-weight:600;">Ligand Type</span></p></body></html> 165 | 166 | 167 | 168 | 169 | 170 | 171 | <html><head/><body><p><span style=" font-weight:600;">Ligand (CCD/SMILES)</span></p></body></html> 172 | 173 | 174 | 175 | 176 | 177 | 178 | 179 | 180 | 181 | 75 182 | true 183 | 184 | 185 | 186 | ESM3 Settings 187 | 188 | 189 | 190 | 191 | 192 | 193 | Forge Token 194 | 195 | 196 | 197 | 198 | 199 | 200 | 201 | 0 202 | 0 203 | 204 | 205 | 206 | QLineEdit::PasswordEchoOnEdit 207 | 208 | 209 | 210 | 211 | 212 | 213 | Temperature 214 | 215 | 216 | 217 | 218 | 219 | 220 | Decimal value bettween [0,1] 221 | 222 | 223 | 9.99 224 | 225 | 226 | 0.70 227 | 228 | 229 | Decimal value bettween [0,1] 230 | 231 | 232 | 233 | 234 | 235 | 236 | Number of Steps 237 | 238 | 239 | 240 | 241 | 242 | 243 | false 244 | 245 | 246 | 999 247 | 248 | 249 | 8 250 | 251 | 252 | 253 | 254 | 255 | 256 | Qt::Horizontal 257 | 258 | 259 | 260 | 261 | 262 | 263 | 264 | 75 265 | true 266 | 267 | 268 | 269 | Boltz Settings 270 | 271 | 272 | Qt::AlignLeading|Qt::AlignLeft|Qt::AlignVCenter 273 | 274 | 275 | false 276 | 277 | 278 | false 279 | 280 | 281 | 282 | 283 | 284 | 285 | Recycling Steps 286 | 287 | 288 | 289 | 290 | 291 | 292 | 999 293 | 294 | 295 | 3 296 | 297 | 298 | 299 | 300 | 301 | 302 | Sampling Steps 303 | 304 | 305 | 306 | 307 | 308 | 309 | 99999 310 | 311 | 312 | 200 313 | 314 | 315 | 316 | 317 | 318 | 319 | Use MSA Server? 320 | 321 | 322 | 323 | 324 | 325 | 326 | https://api.colabfold.com 327 | 328 | 329 | 330 | 331 | 332 | 333 | Qt::Horizontal 334 | 335 | 336 | 337 | 338 | 339 | 340 | 341 | 75 342 | true 343 | 344 | 345 | 346 | Chai Settings 347 | 348 | 349 | 350 | 351 | 352 | 353 | Recycling Steps 354 | 355 | 356 | 357 | 358 | 359 | 360 | 999 361 | 362 | 363 | 3 364 | 365 | 366 | 367 | 368 | 369 | 370 | Diffusion Steps 371 | 372 | 373 | 374 | 375 | 376 | 377 | 9999 378 | 379 | 380 | 200 381 | 382 | 383 | 384 | 385 | 386 | 387 | Qt::Horizontal 388 | 389 | 390 | 391 | 392 | 393 | 394 | 395 | 75 396 | true 397 | 398 | 399 | 400 | Protenix Settings 401 | 402 | 403 | 404 | 405 | 406 | 407 | Perform MSA? 408 | 409 | 410 | 411 | 412 | 413 | 414 | Local with mmseqs 415 | 416 | 417 | 418 | 419 | 420 | 421 | true 422 | 423 | 424 | Qt::Horizontal 425 | 426 | 427 | 428 | 429 | 430 | 431 | 432 | 75 433 | true 434 | 435 | 436 | 437 | General 438 | 439 | 440 | 441 | 442 | 443 | 444 | Seed 445 | 446 | 447 | 448 | 449 | 450 | 451 | 99999999 452 | 453 | 454 | 1337 455 | 456 | 457 | 458 | 459 | 460 | 461 | Coloring 462 | 463 | 464 | 465 | 466 | 467 | 468 | AlphaFold Confidence Colors 469 | 470 | 471 | 472 | 473 | 474 | 475 | 476 | 477 | 478 | 479 | 480 | 481 | Fold 482 | 483 | 484 | 485 | 486 | 487 | 488 | Close 489 | 490 | 491 | 492 | 493 | 494 | 495 | 496 | 497 | 498 | 499 | -------------------------------------------------------------------------------- /pymolfold_v0.2.0.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ullahsamee/PyMOLfold/4372d4c05a1315841390dca8460a2f2ea38b2030/pymolfold_v0.2.0.zip -------------------------------------------------------------------------------- /pymolfold_v0.3.0.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ullahsamee/PyMOLfold/4372d4c05a1315841390dca8460a2f2ea38b2030/pymolfold_v0.3.0.zip -------------------------------------------------------------------------------- /pymolfoldv0.4.0.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ullahsamee/PyMOLfold/4372d4c05a1315841390dca8460a2f2ea38b2030/pymolfoldv0.4.0.zip -------------------------------------------------------------------------------- /tests/boltz_tests.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Boltz-1 Tests" 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 15, 13 | "metadata": {}, 14 | "outputs": [], 15 | "source": [ 16 | "import os, tempfile, subprocess" 17 | ] 18 | }, 19 | { 20 | "cell_type": "code", 21 | "execution_count": 2, 22 | "metadata": {}, 23 | "outputs": [], 24 | "source": [ 25 | "aa_sequence = \"FVNQHLCGSHLVEALYLVCGERGFFYTPKTRREAEDLQGSLQPLALEGSLQKRGIVEQCCTSICSLYQLENYCN\"" 26 | ] 27 | }, 28 | { 29 | "cell_type": "code", 30 | "execution_count": 3, 31 | "metadata": {}, 32 | "outputs": [], 33 | "source": [ 34 | "ligand = \"C([C@@H]1[C@H]([C@@H]([C@H]([C@H](O1)O)O)O)O)O\"\n", 35 | "ligand_type = \"smiles\"" 36 | ] 37 | }, 38 | { 39 | "cell_type": "code", 40 | "execution_count": 29, 41 | "metadata": {}, 42 | "outputs": [], 43 | "source": [ 44 | "## Start building FASTA content\n", 45 | "fasta_content = f\">A|protein|empty\\n{aa_sequence}\\n\"\n", 46 | "\n", 47 | "## Add ligand if provided\n", 48 | "if ligand and ligand_type:\n", 49 | " fasta_content += f\">B|{ligand_type}|\\n{ligand}\\n\"" 50 | ] 51 | }, 52 | { 53 | "cell_type": "code", 54 | "execution_count": 30, 55 | "metadata": {}, 56 | "outputs": [], 57 | "source": [ 58 | "# fasta_line = f\">A|protein|empty\\n{aa_sequence}\"" 59 | ] 60 | }, 61 | { 62 | "cell_type": "code", 63 | "execution_count": 43, 64 | "metadata": {}, 65 | "outputs": [ 66 | { 67 | "name": "stdout", 68 | "output_type": "stream", 69 | "text": [ 70 | ">A|protein|empty\n", 71 | "FVNQHLCGSHLVEALYLVCGERGFFYTPKTRREAEDLQGSLQPLALEGSLQKRGIVEQCCTSICSLYQLENYCN\n", 72 | ">B|smiles|\n", 73 | "C([C@@H]1[C@H]([C@@H]([C@H]([C@H](O1)O)O)O)O)O\n", 74 | "\n" 75 | ] 76 | } 77 | ], 78 | "source": [ 79 | "print(fasta_content)" 80 | ] 81 | }, 82 | { 83 | "cell_type": "code", 84 | "execution_count": 44, 85 | "metadata": {}, 86 | "outputs": [ 87 | { 88 | "name": "stdout", 89 | "output_type": "stream", 90 | "text": [ 91 | "Temp fasta file created: C:\\Users\\Colby\\AppData\\Local\\Temp\\tmp0fqkelxc.fasta\n" 92 | ] 93 | } 94 | ], 95 | "source": [ 96 | "## Create temp fasta file\n", 97 | "with tempfile.NamedTemporaryFile(delete=False, suffix=\".fasta\") as temp_fasta:\n", 98 | " temp_fasta.write(fasta_content.encode())\n", 99 | " temp_fasta_path = temp_fasta.name\n", 100 | " temp_fasta_filename = os.path.basename(temp_fasta_path).replace(\".fasta\", \"\")\n", 101 | "\n", 102 | "print(f\"Temp fasta file created: {temp_fasta_path}\")" 103 | ] 104 | }, 105 | { 106 | "cell_type": "code", 107 | "execution_count": 45, 108 | "metadata": {}, 109 | "outputs": [], 110 | "source": [ 111 | "## Create temp output directory\n", 112 | "output_dir = tempfile.mkdtemp()" 113 | ] 114 | }, 115 | { 116 | "cell_type": "code", 117 | "execution_count": 46, 118 | "metadata": {}, 119 | "outputs": [], 120 | "source": [ 121 | "output_path = os.path.join(output_dir,\n", 122 | " f\"boltz_results_{temp_fasta_filename.replace(\".fasta\",\"\")}\",\n", 123 | " \"predictions\",\n", 124 | " temp_fasta_filename.replace(\".fasta\",\"\"),\n", 125 | " f\"{temp_fasta_filename}_model_0.pdb\")" 126 | ] 127 | }, 128 | { 129 | "cell_type": "code", 130 | "execution_count": 47, 131 | "metadata": {}, 132 | "outputs": [ 133 | { 134 | "name": "stdout", 135 | "output_type": "stream", 136 | "text": [ 137 | "Temp output path: C:\\Users\\Colby\\AppData\\Local\\Temp\\tmpbgtemwg8\\boltz_results_tmp0fqkelxc\\predictions\\tmp0fqkelxc\\tmp0fqkelxc_model_0.pdb\n" 138 | ] 139 | } 140 | ], 141 | "source": [ 142 | "print(f\"Temp output path: {output_path}\")" 143 | ] 144 | }, 145 | { 146 | "cell_type": "code", 147 | "execution_count": 48, 148 | "metadata": {}, 149 | "outputs": [], 150 | "source": [ 151 | "cmd = [\n", 152 | " \"boltz\",\n", 153 | " \"predict\",\n", 154 | " temp_fasta_path,\n", 155 | " \"--out_dir\", output_dir,\n", 156 | " \"--accelerator\", \"cpu\",\n", 157 | " \"--output_format\", \"pdb\",\n", 158 | " \"--recycling_steps\", str(3),\n", 159 | " \"--sampling_steps\", str(200)\n", 160 | "]" 161 | ] 162 | }, 163 | { 164 | "cell_type": "code", 165 | "execution_count": 49, 166 | "metadata": {}, 167 | "outputs": [ 168 | { 169 | "data": { 170 | "text/plain": [ 171 | "'boltz predict C:\\\\Users\\\\Colby\\\\AppData\\\\Local\\\\Temp\\\\tmp0fqkelxc.fasta --out_dir C:\\\\Users\\\\Colby\\\\AppData\\\\Local\\\\Temp\\\\tmpbgtemwg8 --accelerator cpu --output_format pdb --recycling_steps 3 --sampling_steps 200'" 172 | ] 173 | }, 174 | "execution_count": 49, 175 | "metadata": {}, 176 | "output_type": "execute_result" 177 | } 178 | ], 179 | "source": [ 180 | "\" \".join(cmd)" 181 | ] 182 | }, 183 | { 184 | "cell_type": "code", 185 | "execution_count": 37, 186 | "metadata": {}, 187 | "outputs": [ 188 | { 189 | "name": "stdout", 190 | "output_type": "stream", 191 | "text": [ 192 | "Running on CPU, this will be slow. Consider using a GPU.\n", 193 | "Checking input data.\n", 194 | "Running predictions for 1 structure\n", 195 | "Processing input data.\n", 196 | "Found explicit empty MSA for some proteins, will run these in single sequence mode. Keep in mind that the model predictions will be suboptimal without an MSA.\n", 197 | "\n", 198 | "Predicting: | | 0/? [00:00A|protein|name=chain_A\\n{aa_sequence}\\n\"\n", 48 | "\n", 49 | "## Add ligand if provided\n", 50 | "if ligand and ligand_type:\n", 51 | " fasta_content += f\">ligand|name=chain_B\\n{ligand}\\n\"" 52 | ] 53 | }, 54 | { 55 | "cell_type": "code", 56 | "execution_count": null, 57 | "metadata": {}, 58 | "outputs": [], 59 | "source": [ 60 | "## Create temp fasta file\n", 61 | "with tempfile.NamedTemporaryFile(delete=False, suffix=\".fasta\") as temp_fasta:\n", 62 | " temp_fasta.write(fasta_content.encode())\n", 63 | " temp_fasta_path = temp_fasta.name" 64 | ] 65 | }, 66 | { 67 | "cell_type": "code", 68 | "execution_count": null, 69 | "metadata": {}, 70 | "outputs": [], 71 | "source": [ 72 | "## Create temp output directory\n", 73 | "output_dir = tempfile.mkdtemp()" 74 | ] 75 | }, 76 | { 77 | "cell_type": "code", 78 | "execution_count": null, 79 | "metadata": {}, 80 | "outputs": [], 81 | "source": [ 82 | "## Detect devices\n", 83 | "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")" 84 | ] 85 | }, 86 | { 87 | "cell_type": "code", 88 | "execution_count": null, 89 | "metadata": {}, 90 | "outputs": [], 91 | "source": [ 92 | "## Run inferencing\n", 93 | "candidates = run_inference(\n", 94 | " fasta_file=temp_fasta_path,\n", 95 | " output_dir=output_dir,\n", 96 | " num_trunk_recycles=3,\n", 97 | " num_diffn_timesteps=200,\n", 98 | " seed=1337,\n", 99 | " device=device,\n", 100 | " use_esm_embeddings=True\n", 101 | ")" 102 | ] 103 | }, 104 | { 105 | "cell_type": "code", 106 | "execution_count": null, 107 | "metadata": {}, 108 | "outputs": [], 109 | "source": [ 110 | "cif_paths = candidates.cif_paths" 111 | ] 112 | }, 113 | { 114 | "cell_type": "code", 115 | "execution_count": null, 116 | "metadata": {}, 117 | "outputs": [], 118 | "source": [ 119 | "cif_paths[0]" 120 | ] 121 | } 122 | ], 123 | "metadata": { 124 | "kernelspec": { 125 | "display_name": "pymolfold-chai", 126 | "language": "python", 127 | "name": "python3" 128 | }, 129 | "language_info": { 130 | "codemirror_mode": { 131 | "name": "ipython", 132 | "version": 3 133 | }, 134 | "file_extension": ".py", 135 | "mimetype": "text/x-python", 136 | "name": "python", 137 | "nbconvert_exporter": "python", 138 | "pygments_lexer": "ipython3", 139 | "version": "3.12.8" 140 | } 141 | }, 142 | "nbformat": 4, 143 | "nbformat_minor": 2 144 | } 145 | -------------------------------------------------------------------------------- /tests/example.fasta: -------------------------------------------------------------------------------- 1 | >aa_sequence 2 | FVNQHLCGSHLVEALYLVCGERGFFYTPKTRREAEDLQGSLQPLALEGSLQKRGIVEQCCTSICSLYQLENYCN 3 | >ligand_sequence 4 | C([C@@H]1[C@H]([C@@H]([C@H]([C@H](O1)O)O)O)O)O -------------------------------------------------------------------------------- /tests/protenix_tests.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Protenix Tests" 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 10, 13 | "metadata": {}, 14 | "outputs": [], 15 | "source": [ 16 | "import os, tempfile, subprocess, json" 17 | ] 18 | }, 19 | { 20 | "cell_type": "code", 21 | "execution_count": 2, 22 | "metadata": {}, 23 | "outputs": [], 24 | "source": [ 25 | "aa_sequence = \"FVNQHLCGSHLVEALYLVCGERGFFYTPKTRREAEDLQGSLQPLALEGSLQKRGIVEQCCTSICSLYQLENYCN\"" 26 | ] 27 | }, 28 | { 29 | "cell_type": "code", 30 | "execution_count": 3, 31 | "metadata": {}, 32 | "outputs": [], 33 | "source": [ 34 | "ligand = \"C([C@@H]1[C@H]([C@@H]([C@H]([C@H](O1)O)O)O)O)O\"\n", 35 | "ligand_type = \"smiles\"" 36 | ] 37 | }, 38 | { 39 | "cell_type": "code", 40 | "execution_count": 7, 41 | "metadata": {}, 42 | "outputs": [], 43 | "source": [ 44 | "json_content = [\n", 45 | " {\"sequences\": [\n", 46 | " {\n", 47 | " \"proteinChain\": {\n", 48 | " \"sequence\": aa_sequence,\n", 49 | " \"count\": 1\n", 50 | " }\n", 51 | " },\n", 52 | " # {\n", 53 | " # \"ligand\": {\n", 54 | " # \"ligand\": ligand,\n", 55 | " # \"count\": 1\n", 56 | " # }\n", 57 | " # }\n", 58 | " ],\n", 59 | " \"name\": \"pymolfold\"\n", 60 | " }\n", 61 | " ]\n", 62 | "\n", 63 | "## If ligand provided, add it to the JSON\n", 64 | "if ligand:\n", 65 | " ligand_dict = {\n", 66 | " \"ligand\": {\n", 67 | " \"ligand\": ligand,\n", 68 | " \"count\": 1\n", 69 | " }\n", 70 | " }\n", 71 | " json_content[0][\"sequences\"].append(ligand_dict)" 72 | ] 73 | }, 74 | { 75 | "cell_type": "code", 76 | "execution_count": 8, 77 | "metadata": {}, 78 | "outputs": [ 79 | { 80 | "name": "stdout", 81 | "output_type": "stream", 82 | "text": [ 83 | "[{'sequences': [{'proteinChain': {'sequence': 'FVNQHLCGSHLVEALYLVCGERGFFYTPKTRREAEDLQGSLQPLALEGSLQKRGIVEQCCTSICSLYQLENYCN', 'count': 1}}, {'ligand': {'ligand': 'C([C@@H]1[C@H]([C@@H]([C@H]([C@H](O1)O)O)O)O)O', 'count': 1}}], 'name': 'pymolfold'}]\n" 84 | ] 85 | } 86 | ], 87 | "source": [ 88 | "print(json_content)" 89 | ] 90 | }, 91 | { 92 | "cell_type": "code", 93 | "execution_count": 11, 94 | "metadata": {}, 95 | "outputs": [ 96 | { 97 | "name": "stdout", 98 | "output_type": "stream", 99 | "text": [ 100 | "Temp JSON file created: C:\\Users\\Colby\\AppData\\Local\\Temp\\tmpqxafqpt3.json\n" 101 | ] 102 | } 103 | ], 104 | "source": [ 105 | "## Create temp json file\n", 106 | "with tempfile.NamedTemporaryFile(delete=False, suffix=\".json\") as temp_json:\n", 107 | " temp_json.write(json.dumps(json_content).encode())\n", 108 | " temp_json_path = temp_json.name\n", 109 | " temp_json_filename = os.path.basename(temp_json_path).replace(\".json\", \"\")\n", 110 | "\n", 111 | "print(f\"Temp JSON file created: {temp_json_path}\")" 112 | ] 113 | }, 114 | { 115 | "cell_type": "code", 116 | "execution_count": 13, 117 | "metadata": {}, 118 | "outputs": [], 119 | "source": [ 120 | "## Create temp output directory\n", 121 | "output_dir = tempfile.mkdtemp()" 122 | ] 123 | }, 124 | { 125 | "cell_type": "code", 126 | "execution_count": 14, 127 | "metadata": {}, 128 | "outputs": [ 129 | { 130 | "name": "stdout", 131 | "output_type": "stream", 132 | "text": [ 133 | "Temp output path: C:\\Users\\Colby\\AppData\\Local\\Temp\\tmpbj3l70xu\n" 134 | ] 135 | } 136 | ], 137 | "source": [ 138 | "print(f\"Temp output path: {output_dir}\")" 139 | ] 140 | }, 141 | { 142 | "cell_type": "code", 143 | "execution_count": 17, 144 | "metadata": {}, 145 | "outputs": [], 146 | "source": [ 147 | "cmd = [\n", 148 | " \"protenix\",\n", 149 | " \"predict\",\n", 150 | " \"--input\", temp_json_path,\n", 151 | " \"--out_dir\", output_dir,\n", 152 | " \"--seeds\", str(1337)\n", 153 | " ]\n", 154 | "\n", 155 | "cmd.append(\"--use_msa_server\")" 156 | ] 157 | }, 158 | { 159 | "cell_type": "code", 160 | "execution_count": 18, 161 | "metadata": {}, 162 | "outputs": [ 163 | { 164 | "data": { 165 | "text/plain": [ 166 | "'protenix predict --input C:\\\\Users\\\\Colby\\\\AppData\\\\Local\\\\Temp\\\\tmpqxafqpt3.json --out_dir C:\\\\Users\\\\Colby\\\\AppData\\\\Local\\\\Temp\\\\tmpbj3l70xu --seeds 1337 --use_msa_server'" 167 | ] 168 | }, 169 | "execution_count": 18, 170 | "metadata": {}, 171 | "output_type": "execute_result" 172 | } 173 | ], 174 | "source": [ 175 | "\" \".join(cmd)" 176 | ] 177 | }, 178 | { 179 | "cell_type": "code", 180 | "execution_count": 50, 181 | "metadata": {}, 182 | "outputs": [], 183 | "source": [ 184 | "result = subprocess.run(cmd, capture_output=True, text=True, check=True)" 185 | ] 186 | } 187 | ], 188 | "metadata": { 189 | "kernelspec": { 190 | "display_name": "pymolfold", 191 | "language": "python", 192 | "name": "python3" 193 | }, 194 | "language_info": { 195 | "codemirror_mode": { 196 | "name": "ipython", 197 | "version": 3 198 | }, 199 | "file_extension": ".py", 200 | "mimetype": "text/x-python", 201 | "name": "python", 202 | "nbconvert_exporter": "python", 203 | "pygments_lexer": "ipython3", 204 | "version": "3.12.8" 205 | } 206 | }, 207 | "nbformat": 4, 208 | "nbformat_minor": 2 209 | } 210 | --------------------------------------------------------------------------------