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  1. .gitattributes +1 -0
  2. LICENSE +21 -0
  3. ProteinMPNN/.gitignore +129 -0
  4. ProteinMPNN/LICENSE +21 -0
  5. ProteinMPNN/README.md +17 -0
  6. ProteinMPNN/colab_notebooks/README.md +1 -0
  7. ProteinMPNN/colab_notebooks/quickdemo.ipynb +322 -0
  8. ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_2_outputs/assigned_pdbs.jsonl +1 -0
  9. ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_2_outputs/fixed_pdbs.jsonl +1 -0
  10. ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_2_outputs/parsed_pdbs.jsonl +0 -0
  11. ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_2_outputs/seqs/3HTN.fa +6 -0
  12. ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_2_outputs/seqs/4YOW.fa +6 -0
  13. ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_3_outputs/seqs/3HTN.fa +6 -0
  14. ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_4_outputs/assigned_pdbs.jsonl +1 -0
  15. ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_4_outputs/fixed_pdbs.jsonl +1 -0
  16. ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_4_outputs/parsed_pdbs.jsonl +0 -0
  17. ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_4_outputs/seqs/3HTN.fa +6 -0
  18. ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_4_outputs/seqs/4YOW.fa +6 -0
  19. ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_5_outputs/assigned_pdbs.jsonl +1 -0
  20. ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_5_outputs/fixed_pdbs.jsonl +1 -0
  21. ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_5_outputs/parsed_pdbs.jsonl +0 -0
  22. ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_5_outputs/seqs/3HTN.fa +6 -0
  23. ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_5_outputs/seqs/4YOW.fa +6 -0
  24. ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_5_outputs/tied_pdbs.jsonl +1 -0
  25. ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_7_outputs/bias_pdbs.jsonl +1 -0
  26. ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_7_outputs/parsed_pdbs.jsonl +0 -0
  27. ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_7_outputs/seqs/3HTN.fa +6 -0
  28. ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_7_outputs/seqs/4YOW.fa +6 -0
  29. ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/pdbs/3HTN.pdb +0 -0
  30. ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/pdbs/4YOW.pdb +0 -0
  31. ProteinMPNN/vanilla_proteinmpnn/PDB_homooligomers/example_6_outputs/parsed_pdbs.jsonl +0 -0
  32. ProteinMPNN/vanilla_proteinmpnn/PDB_homooligomers/example_6_outputs/seqs/4GYT.fa +6 -0
  33. ProteinMPNN/vanilla_proteinmpnn/PDB_homooligomers/example_6_outputs/seqs/6EHB.fa +6 -0
  34. ProteinMPNN/vanilla_proteinmpnn/PDB_homooligomers/example_6_outputs/tied_pdbs.jsonl +1 -0
  35. ProteinMPNN/vanilla_proteinmpnn/PDB_homooligomers/pdbs/4GYT.pdb +0 -0
  36. ProteinMPNN/vanilla_proteinmpnn/PDB_homooligomers/pdbs/6EHB.pdb +0 -0
  37. ProteinMPNN/vanilla_proteinmpnn/PDB_monomers/example_1_outputs/parsed_pdbs.jsonl +2 -0
  38. ProteinMPNN/vanilla_proteinmpnn/PDB_monomers/example_1_outputs/seqs/5L33.fa +6 -0
  39. ProteinMPNN/vanilla_proteinmpnn/PDB_monomers/example_1_outputs/seqs/6MRR.fa +6 -0
  40. ProteinMPNN/vanilla_proteinmpnn/PDB_monomers/pdbs/5L33.pdb +0 -0
  41. ProteinMPNN/vanilla_proteinmpnn/PDB_monomers/pdbs/6MRR.pdb +0 -0
  42. ProteinMPNN/vanilla_proteinmpnn/examples/submit_example_1.sh +27 -0
  43. ProteinMPNN/vanilla_proteinmpnn/examples/submit_example_2.sh +32 -0
  44. ProteinMPNN/vanilla_proteinmpnn/examples/submit_example_3.sh +26 -0
  45. ProteinMPNN/vanilla_proteinmpnn/examples/submit_example_4.sh +39 -0
  46. ProteinMPNN/vanilla_proteinmpnn/examples/submit_example_5.sh +43 -0
  47. ProteinMPNN/vanilla_proteinmpnn/examples/submit_example_6.sh +33 -0
  48. ProteinMPNN/vanilla_proteinmpnn/examples/submit_example_7.sh +40 -0
  49. ProteinMPNN/vanilla_proteinmpnn/helper_scripts/assign_fixed_chains.py +39 -0
  50. ProteinMPNN/vanilla_proteinmpnn/helper_scripts/make_bias_AA.py +27 -0
.gitattributes ADDED
@@ -0,0 +1 @@
 
 
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+ *.pt filter=lfs diff=lfs merge=lfs -text
LICENSE ADDED
@@ -0,0 +1,21 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ MIT License
2
+
3
+ Copyright (c) 2022 Justas Dauparas,Sergey Ovichinnikov, Simon Duerr
4
+
5
+ Permission is hereby granted, free of charge, to any person obtaining a copy
6
+ of this software and associated documentation files (the "Software"), to deal
7
+ in the Software without restriction, including without limitation the rights
8
+ to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
9
+ copies of the Software, and to permit persons to whom the Software is
10
+ furnished to do so, subject to the following conditions:
11
+
12
+ The above copyright notice and this permission notice shall be included in all
13
+ copies or substantial portions of the Software.
14
+
15
+ THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16
+ IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17
+ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18
+ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19
+ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20
+ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21
+ SOFTWARE.
ProteinMPNN/.gitignore ADDED
@@ -0,0 +1,129 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Byte-compiled / optimized / DLL files
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+ __pycache__/
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+ *.py[cod]
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+ *$py.class
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+
6
+ # C extensions
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+ *.so
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+
9
+ # Distribution / packaging
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+ .Python
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+ build/
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+ develop-eggs/
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+ dist/
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+ downloads/
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+ eggs/
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+ .eggs/
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+ lib/
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+ lib64/
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+ parts/
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+ sdist/
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+ var/
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+ wheels/
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+ pip-wheel-metadata/
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+ share/python-wheels/
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+ *.egg-info/
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+ .installed.cfg
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+ *.egg
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+ MANIFEST
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+
30
+ # PyInstaller
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+ # Usually these files are written by a python script from a template
32
+ # before PyInstaller builds the exe, so as to inject date/other infos into it.
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+ *.manifest
34
+ *.spec
35
+
36
+ # Installer logs
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+ pip-log.txt
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+ pip-delete-this-directory.txt
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+
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+ # Unit test / coverage reports
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+ htmlcov/
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+ .tox/
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+ .nox/
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+ .coverage
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+ .coverage.*
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+ .cache
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+ nosetests.xml
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+ coverage.xml
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+ *.cover
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+ *.py,cover
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+ .hypothesis/
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+ .pytest_cache/
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+
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+ # Translations
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+ *.mo
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+ *.pot
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+
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+ # Django stuff:
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+ *.log
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+ local_settings.py
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+ db.sqlite3
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+ db.sqlite3-journal
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+
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+ # Flask stuff:
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+ instance/
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+ .webassets-cache
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+
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+ # Scrapy stuff:
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+ .scrapy
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+
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+ # Sphinx documentation
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+ docs/_build/
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+
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+ # PyBuilder
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+ target/
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+
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+ # Jupyter Notebook
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+ .ipynb_checkpoints
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+
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+ # IPython
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+ profile_default/
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+ ipython_config.py
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+
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+ # pyenv
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+ .python-version
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+
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+ # pipenv
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+ # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
89
+ # However, in case of collaboration, if having platform-specific dependencies or dependencies
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+ # having no cross-platform support, pipenv may install dependencies that don't work, or not
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+ # install all needed dependencies.
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+ #Pipfile.lock
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+
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+ # PEP 582; used by e.g. github.com/David-OConnor/pyflow
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+ __pypackages__/
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+
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+ # Celery stuff
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+ celerybeat-schedule
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+ celerybeat.pid
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+
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+ # SageMath parsed files
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+ *.sage.py
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+
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+ # Environments
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+ .env
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+ .venv
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+ env/
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+ venv/
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+ ENV/
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+ env.bak/
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+ venv.bak/
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+
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+ # Spyder project settings
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+ .spyderproject
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+ .spyproject
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+
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+ # Rope project settings
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+ .ropeproject
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+
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+ # mkdocs documentation
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+ /site
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+
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+ # mypy
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+ .mypy_cache/
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+ .dmypy.json
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+ dmypy.json
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+
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+ # Pyre type checker
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+ .pyre/
ProteinMPNN/LICENSE ADDED
@@ -0,0 +1,21 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ MIT License
2
+
3
+ Copyright (c) 2022 Justas Dauparas
4
+
5
+ Permission is hereby granted, free of charge, to any person obtaining a copy
6
+ of this software and associated documentation files (the "Software"), to deal
7
+ in the Software without restriction, including without limitation the rights
8
+ to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
9
+ copies of the Software, and to permit persons to whom the Software is
10
+ furnished to do so, subject to the following conditions:
11
+
12
+ The above copyright notice and this permission notice shall be included in all
13
+ copies or substantial portions of the Software.
14
+
15
+ THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16
+ IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17
+ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18
+ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19
+ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20
+ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21
+ SOFTWARE.
ProteinMPNN/README.md ADDED
@@ -0,0 +1,17 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ # ProteinMPNN + ESM
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+
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+ You can run this repo locally with the following easy steps.
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+
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+ In a new python environment, do:
6
+
7
+ ```
8
+ pip install -r requirements.txt
9
+
10
+ pip install --upgrade transformers accelerate
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+
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+ python app.py
13
+ ```
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+
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+ This will launch a local webserver and a share URL (`xxx.gradio.app`, helpful if running this on a cluster), open the url in your webbrowser and start designing proteins.
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+
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+
ProteinMPNN/colab_notebooks/README.md ADDED
@@ -0,0 +1 @@
 
 
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+ <a href="https://colab.research.google.com/github/dauparas/ProteinMPNN/blob/main/colab_notebooks/quickdemo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
ProteinMPNN/colab_notebooks/quickdemo.ipynb ADDED
@@ -0,0 +1,322 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "nbformat": 4,
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+ "nbformat_minor": 0,
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+ "metadata": {
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+ "colab": {
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+ "name": "quickdemo.ipynb",
7
+ "provenance": [],
8
+ "include_colab_link": true
9
+ },
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+ "kernelspec": {
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+ "name": "python3",
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+ "display_name": "Python 3"
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+ },
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+ "language_info": {
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+ "name": "python"
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+ }
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+ },
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+ "cells": [
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+ {
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+ "cell_type": "markdown",
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+ "metadata": {
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+ "id": "view-in-github",
23
+ "colab_type": "text"
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+ },
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+ "source": [
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+ "<a href=\"https://colab.research.google.com/github/dauparas/ProteinMPNN/blob/main/colab_notebooks/quickdemo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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+ ]
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+ },
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+ {
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+ "cell_type": "markdown",
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+ "source": [
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+ "#ProteinMPNN\n",
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+ "This notebook is intended as a quick demo, more features to come!"
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+ ],
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+ "metadata": {
36
+ "id": "AYZebfKn8gef"
37
+ }
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+ },
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+ {
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+ "cell_type": "code",
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+ "source": [
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+ "#@title Setup Model\n",
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+ "import json, time, os, sys, glob\n",
44
+ "\n",
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+ "if not os.path.isdir(\"ProteinMPNN\"):\n",
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+ " os.system(\"git clone -q https://github.com/dauparas/ProteinMPNN.git\")\n",
47
+ "sys.path.append('/content/ProteinMPNN/vanilla_proteinmpnn')\n",
48
+ "\n",
49
+ "import matplotlib.pyplot as plt\n",
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+ "import shutil\n",
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+ "import warnings\n",
52
+ "import numpy as np\n",
53
+ "import torch\n",
54
+ "from torch import optim\n",
55
+ "from torch.utils.data import DataLoader\n",
56
+ "from torch.utils.data.dataset import random_split, Subset\n",
57
+ "import copy\n",
58
+ "import torch.nn as nn\n",
59
+ "import torch.nn.functional as F\n",
60
+ "import random\n",
61
+ "import os.path\n",
62
+ "from protein_mpnn_utils import loss_nll, loss_smoothed, gather_edges, gather_nodes, gather_nodes_t, cat_neighbors_nodes, _scores, _S_to_seq, tied_featurize, parse_PDB\n",
63
+ "from protein_mpnn_utils import StructureDataset, StructureDatasetPDB, ProteinMPNN\n",
64
+ "\n",
65
+ "device = torch.device(\"cuda:0\" if (torch.cuda.is_available()) else \"cpu\")\n",
66
+ "model_name=\"v_48_020\" # ProteinMPNN model name: v_48_002, v_48_010, v_48_020, v_48_030, v_32_002, v_32_010; v_32_020, v_32_030; v_48_010=version with 48 edges 0.10A noise\n",
67
+ "backbone_noise=0.00 # Standard deviation of Gaussian noise to add to backbone atoms\n",
68
+ "\n",
69
+ "path_to_model_weights='/content/ProteinMPNN/vanilla_proteinmpnn/vanilla_model_weights' \n",
70
+ "hidden_dim = 128\n",
71
+ "num_layers = 3 \n",
72
+ "model_folder_path = path_to_model_weights\n",
73
+ "if model_folder_path[-1] != '/':\n",
74
+ " model_folder_path = model_folder_path + '/'\n",
75
+ "checkpoint_path = model_folder_path + f'{model_name}.pt'\n",
76
+ "\n",
77
+ "checkpoint = torch.load(checkpoint_path, map_location=device) \n",
78
+ "print('Number of edges:', checkpoint['num_edges'])\n",
79
+ "noise_level_print = checkpoint['noise_level']\n",
80
+ "print(f'Training noise level: {noise_level_print}A')\n",
81
+ "model = ProteinMPNN(num_letters=21, node_features=hidden_dim, edge_features=hidden_dim, hidden_dim=hidden_dim, num_encoder_layers=num_layers, num_decoder_layers=num_layers, augment_eps=backbone_noise, k_neighbors=checkpoint['num_edges'])\n",
82
+ "model.to(device)\n",
83
+ "model.load_state_dict(checkpoint['model_state_dict'])\n",
84
+ "model.eval()\n",
85
+ "print(\"Model loaded\")"
86
+ ],
87
+ "metadata": {
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+ "id": "iYDU3ftml2k5",
89
+ "cellView": "form"
90
+ },
91
+ "execution_count": null,
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+ "outputs": []
93
+ },
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+ {
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+ "cell_type": "code",
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+ "source": [
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+ "import re\n",
98
+ "from google.colab import files\n",
99
+ "import numpy as np\n",
100
+ "\n",
101
+ "#########################\n",
102
+ "def get_pdb(pdb_code=\"\"):\n",
103
+ " if pdb_code is None or pdb_code == \"\":\n",
104
+ " upload_dict = files.upload()\n",
105
+ " pdb_string = upload_dict[list(upload_dict.keys())[0]]\n",
106
+ " with open(\"tmp.pdb\",\"wb\") as out: out.write(pdb_string)\n",
107
+ " return \"tmp.pdb\"\n",
108
+ " else:\n",
109
+ " os.system(f\"wget -qnc https://files.rcsb.org/view/{pdb_code}.pdb\")\n",
110
+ " return f\"{pdb_code}.pdb\"\n",
111
+ "\n",
112
+ "#@markdown ### Input Options\n",
113
+ "pdb='6MRR' #@param {type:\"string\"}\n",
114
+ "pdb_path = get_pdb(pdb)\n",
115
+ "#@markdown - pdb code (leave blank to get an upload prompt)\n",
116
+ "\n",
117
+ "designed_chain = \"A\" #@param {type:\"string\"}\n",
118
+ "fixed_chain = \"\" #@param {type:\"string\"}\n",
119
+ "\n",
120
+ "if designed_chain == \"\":\n",
121
+ " designed_chain_list = []\n",
122
+ "else:\n",
123
+ " designed_chain_list = re.sub(\"[^A-Za-z]+\",\",\", designed_chain).split(\",\")\n",
124
+ "\n",
125
+ "if fixed_chain == \"\":\n",
126
+ " fixed_chain_list = []\n",
127
+ "else:\n",
128
+ " fixed_chain_list = re.sub(\"[^A-Za-z]+\",\",\", fixed_chain).split(\",\")\n",
129
+ "\n",
130
+ "chain_list = list(set(designed_chain_list + fixed_chain_list))\n",
131
+ "\n",
132
+ "#@markdown - specified which chain(s) to design and which chain(s) to keep fixed. \n",
133
+ "#@markdown Use comma:`A,B` to specifiy more than one chain\n",
134
+ "\n",
135
+ "#chain = \"A\" #@param {type:\"string\"}\n",
136
+ "#pdb_path_chains = chain\n",
137
+ "##@markdown - Define which chain to redesign\n",
138
+ "\n",
139
+ "#@markdown ### Design Options\n",
140
+ "num_seqs = 1 #@param [\"1\", \"2\", \"4\", \"8\", \"16\", \"32\", \"64\"] {type:\"raw\"}\n",
141
+ "num_seq_per_target = num_seqs\n",
142
+ "sampling_temp = \"0.1\" #@param [\"0.1\", \"0.15\", \"0.2\", \"0.25\", \"0.3\"]\n",
143
+ "#@markdown - Sampling temperature for amino acids, T=0.0 means taking \n",
144
+ "#@markdown argmax, T>>1.0 means sample randomly. Suggested values \n",
145
+ "#@markdown 0.1, 0.15, 0.2, 0.25, 0.3. Higher values will lead to more diversity.\n",
146
+ "\n",
147
+ "\n",
148
+ "save_score=0 # 0 for False, 1 for True; save score=-log_prob to npy files\n",
149
+ "save_probs=0 # 0 for False, 1 for True; save MPNN predicted probabilites per position\n",
150
+ "score_only=0 # 0 for False, 1 for True; score input backbone-sequence pairs\n",
151
+ "conditional_probs_only=0 # 0 for False, 1 for True; output conditional probabilities p(s_i given the rest of the sequence and backbone)\n",
152
+ "conditional_probs_only_backbone=0 # 0 for False, 1 for True; if true output conditional probabilities p(s_i given backbone)\n",
153
+ " \n",
154
+ "batch_size=1 # Batch size; can set higher for titan, quadro GPUs, reduce this if running out of GPU memory\n",
155
+ "max_length=20000 # Max sequence length\n",
156
+ " \n",
157
+ "out_folder='.' # Path to a folder to output sequences, e.g. /home/out/\n",
158
+ "jsonl_path='' # Path to a folder with parsed pdb into jsonl\n",
159
+ "omit_AAs='X' # Specify which amino acids should be omitted in the generated sequence, e.g. 'AC' would omit alanine and cystine.\n",
160
+ " \n",
161
+ "pssm_multi=0.0 # A value between [0.0, 1.0], 0.0 means do not use pssm, 1.0 ignore MPNN predictions\n",
162
+ "pssm_threshold=0.0 # A value between -inf + inf to restric per position AAs\n",
163
+ "pssm_log_odds_flag=0 # 0 for False, 1 for True\n",
164
+ "pssm_bias_flag=0 # 0 for False, 1 for True\n",
165
+ "\n",
166
+ "\n",
167
+ "##############################################################\n",
168
+ "\n",
169
+ "folder_for_outputs = out_folder\n",
170
+ "\n",
171
+ "NUM_BATCHES = num_seq_per_target//batch_size\n",
172
+ "BATCH_COPIES = batch_size\n",
173
+ "temperatures = [float(item) for item in sampling_temp.split()]\n",
174
+ "omit_AAs_list = omit_AAs\n",
175
+ "alphabet = 'ACDEFGHIKLMNPQRSTVWYX'\n",
176
+ "\n",
177
+ "omit_AAs_np = np.array([AA in omit_AAs_list for AA in alphabet]).astype(np.float32)\n",
178
+ "\n",
179
+ "chain_id_dict = None\n",
180
+ "fixed_positions_dict = None\n",
181
+ "pssm_dict = None\n",
182
+ "omit_AA_dict = None\n",
183
+ "bias_AA_dict = None\n",
184
+ "tied_positions_dict = None\n",
185
+ "bias_by_res_dict = None\n",
186
+ "bias_AAs_np = np.zeros(len(alphabet))\n",
187
+ "\n",
188
+ "\n",
189
+ "###############################################################\n",
190
+ "pdb_dict_list = parse_PDB(pdb_path, input_chain_list=chain_list)\n",
191
+ "dataset_valid = StructureDatasetPDB(pdb_dict_list, truncate=None, max_length=max_length)\n",
192
+ "\n",
193
+ "chain_id_dict = {}\n",
194
+ "chain_id_dict[pdb_dict_list[0]['name']]= (designed_chain_list, fixed_chain_list)\n",
195
+ "\n",
196
+ "print(chain_id_dict)"
197
+ ],
198
+ "metadata": {
199
+ "cellView": "form",
200
+ "id": "k4o6w2Y23wxO"
201
+ },
202
+ "execution_count": null,
203
+ "outputs": []
204
+ },
205
+ {
206
+ "cell_type": "code",
207
+ "source": [
208
+ "#@title RUN\n",
209
+ "with torch.no_grad():\n",
210
+ " print('Generating sequences...')\n",
211
+ " for ix, protein in enumerate(dataset_valid):\n",
212
+ " score_list = []\n",
213
+ " all_probs_list = []\n",
214
+ " all_log_probs_list = []\n",
215
+ " S_sample_list = []\n",
216
+ " batch_clones = [copy.deepcopy(protein) for i in range(BATCH_COPIES)]\n",
217
+ " X, S, mask, lengths, chain_M, chain_encoding_all, chain_list_list, visible_list_list, masked_list_list, masked_chain_length_list_list, chain_M_pos, omit_AA_mask, residue_idx, dihedral_mask, tied_pos_list_of_lists_list, pssm_coef, pssm_bias, pssm_log_odds_all, bias_by_res_all, tied_beta = tied_featurize(batch_clones, device, chain_id_dict, fixed_positions_dict, omit_AA_dict, tied_positions_dict, pssm_dict, bias_by_res_dict)\n",
218
+ " pssm_log_odds_mask = (pssm_log_odds_all > pssm_threshold).float() #1.0 for true, 0.0 for false\n",
219
+ " name_ = batch_clones[0]['name']\n",
220
+ "\n",
221
+ " randn_1 = torch.randn(chain_M.shape, device=X.device)\n",
222
+ " log_probs = model(X, S, mask, chain_M*chain_M_pos, residue_idx, chain_encoding_all, randn_1)\n",
223
+ " mask_for_loss = mask*chain_M*chain_M_pos\n",
224
+ " scores = _scores(S, log_probs, mask_for_loss)\n",
225
+ " native_score = scores.cpu().data.numpy()\n",
226
+ "\n",
227
+ " for temp in temperatures:\n",
228
+ " for j in range(NUM_BATCHES):\n",
229
+ " randn_2 = torch.randn(chain_M.shape, device=X.device)\n",
230
+ " if tied_positions_dict == None:\n",
231
+ " sample_dict = model.sample(X, randn_2, S, chain_M, chain_encoding_all, residue_idx, mask=mask, temperature=temp, omit_AAs_np=omit_AAs_np, bias_AAs_np=bias_AAs_np, chain_M_pos=chain_M_pos, omit_AA_mask=omit_AA_mask, pssm_coef=pssm_coef, pssm_bias=pssm_bias, pssm_multi=pssm_multi, pssm_log_odds_flag=bool(pssm_log_odds_flag), pssm_log_odds_mask=pssm_log_odds_mask, pssm_bias_flag=bool(pssm_bias_flag), bias_by_res=bias_by_res_all)\n",
232
+ " S_sample = sample_dict[\"S\"] \n",
233
+ " else:\n",
234
+ " sample_dict = model.tied_sample(X, randn_2, S, chain_M, chain_encoding_all, residue_idx, mask=mask, temperature=temp, omit_AAs_np=omit_AAs_np, bias_AAs_np=bias_AAs_np, chain_M_pos=chain_M_pos, omit_AA_mask=omit_AA_mask, pssm_coef=pssm_coef, pssm_bias=pssm_bias, pssm_multi=pssm_multi, pssm_log_odds_flag=bool(pssm_log_odds_flag), pssm_log_odds_mask=pssm_log_odds_mask, pssm_bias_flag=bool(pssm_bias_flag), tied_pos=tied_pos_list_of_lists_list[0], tied_beta=tied_beta, bias_by_res=bias_by_res_all)\n",
235
+ " # Compute scores\n",
236
+ " S_sample = sample_dict[\"S\"]\n",
237
+ " log_probs = model(X, S_sample, mask, chain_M*chain_M_pos, residue_idx, chain_encoding_all, randn_2, use_input_decoding_order=True, decoding_order=sample_dict[\"decoding_order\"])\n",
238
+ " mask_for_loss = mask*chain_M*chain_M_pos\n",
239
+ " scores = _scores(S_sample, log_probs, mask_for_loss)\n",
240
+ " scores = scores.cpu().data.numpy()\n",
241
+ " all_probs_list.append(sample_dict[\"probs\"].cpu().data.numpy())\n",
242
+ " all_log_probs_list.append(log_probs.cpu().data.numpy())\n",
243
+ " S_sample_list.append(S_sample.cpu().data.numpy())\n",
244
+ " for b_ix in range(BATCH_COPIES):\n",
245
+ " masked_chain_length_list = masked_chain_length_list_list[b_ix]\n",
246
+ " masked_list = masked_list_list[b_ix]\n",
247
+ " seq_recovery_rate = torch.sum(torch.sum(torch.nn.functional.one_hot(S[b_ix], 21)*torch.nn.functional.one_hot(S_sample[b_ix], 21),axis=-1)*mask_for_loss[b_ix])/torch.sum(mask_for_loss[b_ix])\n",
248
+ " seq = _S_to_seq(S_sample[b_ix], chain_M[b_ix])\n",
249
+ " score = scores[b_ix]\n",
250
+ " score_list.append(score)\n",
251
+ " native_seq = _S_to_seq(S[b_ix], chain_M[b_ix])\n",
252
+ " if b_ix == 0 and j==0 and temp==temperatures[0]:\n",
253
+ " start = 0\n",
254
+ " end = 0\n",
255
+ " list_of_AAs = []\n",
256
+ " for mask_l in masked_chain_length_list:\n",
257
+ " end += mask_l\n",
258
+ " list_of_AAs.append(native_seq[start:end])\n",
259
+ " start = end\n",
260
+ " native_seq = \"\".join(list(np.array(list_of_AAs)[np.argsort(masked_list)]))\n",
261
+ " l0 = 0\n",
262
+ " for mc_length in list(np.array(masked_chain_length_list)[np.argsort(masked_list)])[:-1]:\n",
263
+ " l0 += mc_length\n",
264
+ " native_seq = native_seq[:l0] + '/' + native_seq[l0:]\n",
265
+ " l0 += 1\n",
266
+ " sorted_masked_chain_letters = np.argsort(masked_list_list[0])\n",
267
+ " print_masked_chains = [masked_list_list[0][i] for i in sorted_masked_chain_letters]\n",
268
+ " sorted_visible_chain_letters = np.argsort(visible_list_list[0])\n",
269
+ " print_visible_chains = [visible_list_list[0][i] for i in sorted_visible_chain_letters]\n",
270
+ " native_score_print = np.format_float_positional(np.float32(native_score.mean()), unique=False, precision=4)\n",
271
+ " line = '>{}, score={}, fixed_chains={}, designed_chains={}, model_name={}\\n{}\\n'.format(name_, native_score_print, print_visible_chains, print_masked_chains, model_name, native_seq)\n",
272
+ " print(line.rstrip())\n",
273
+ " start = 0\n",
274
+ " end = 0\n",
275
+ " list_of_AAs = []\n",
276
+ " for mask_l in masked_chain_length_list:\n",
277
+ " end += mask_l\n",
278
+ " list_of_AAs.append(seq[start:end])\n",
279
+ " start = end\n",
280
+ "\n",
281
+ " seq = \"\".join(list(np.array(list_of_AAs)[np.argsort(masked_list)]))\n",
282
+ " l0 = 0\n",
283
+ " for mc_length in list(np.array(masked_chain_length_list)[np.argsort(masked_list)])[:-1]:\n",
284
+ " l0 += mc_length\n",
285
+ " seq = seq[:l0] + '/' + seq[l0:]\n",
286
+ " l0 += 1\n",
287
+ " score_print = np.format_float_positional(np.float32(score), unique=False, precision=4)\n",
288
+ " seq_rec_print = np.format_float_positional(np.float32(seq_recovery_rate.detach().cpu().numpy()), unique=False, precision=4)\n",
289
+ " line = '>T={}, sample={}, score={}, seq_recovery={}\\n{}\\n'.format(temp,b_ix,score_print,seq_rec_print,seq)\n",
290
+ " print(line.rstrip())\n",
291
+ "\n",
292
+ "\n",
293
+ "all_probs_concat = np.concatenate(all_probs_list)\n",
294
+ "all_log_probs_concat = np.concatenate(all_log_probs_list)\n",
295
+ "S_sample_concat = np.concatenate(S_sample_list)"
296
+ ],
297
+ "metadata": {
298
+ "id": "xMVlYh8Fv2of",
299
+ "cellView": "form"
300
+ },
301
+ "execution_count": null,
302
+ "outputs": []
303
+ },
304
+ {
305
+ "cell_type": "code",
306
+ "source": [
307
+ "# experimental output\n",
308
+ "plt.figure(figsize=(20,5), dpi=100)\n",
309
+ "plt.imshow(all_probs_concat.mean(0).T,vmin=0,vmax=1)\n",
310
+ "plt.xlabel(\"positions\")\n",
311
+ "plt.ylabel(\"amino acids\")\n",
312
+ "plt.yticks(range(21),list(alphabet))\n",
313
+ "plt.show()"
314
+ ],
315
+ "metadata": {
316
+ "id": "4jSKLU3L17Sf"
317
+ },
318
+ "execution_count": null,
319
+ "outputs": []
320
+ }
321
+ ]
322
+ }
ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_2_outputs/assigned_pdbs.jsonl ADDED
@@ -0,0 +1 @@
 
 
1
+ {"4YOW": [["A", "B"], ["C", "D", "E", "F"]], "3HTN": [["A", "B"], ["C"]]}
ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_2_outputs/fixed_pdbs.jsonl ADDED
@@ -0,0 +1 @@
 
 
1
+ {"4YOW": {"A": [1, 2, 3, 4, 5, 6, 7, 8, 23, 25], "C": [10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 40], "B": [], "D": [], "E": [], "F": []}, "3HTN": {"A": [1, 2, 3, 4, 5, 6, 7, 8, 23, 25], "C": [10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 40], "B": []}}
ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_2_outputs/parsed_pdbs.jsonl ADDED
The diff for this file is too large to render. See raw diff
 
ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_2_outputs/seqs/3HTN.fa ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ >3HTN, score=1.1538, fixed_chains=['C'], designed_chains=['A', 'B'], model_name=v_48_020
2
+ NMYSYKKIGNKYIVSINNHTEIVKALNAFCKEKGILSGSINGIGAIGELTLRFFNPKTKAYDDKTFREQMEISNLTGNISSMNEQVYLHLHITVGRSDYSALAGHLLSAIQNGAGEFVVEDYSERISRTYNPDLGLNIYDFER/NMYSYKKIGNKYIVSINNHTEIVKALNAFCKEKGILSGSINGIGAIGELTLRFFNPKXXXXDDKTFREQMEISNLTGNISSMNEQVYLHLHITVGRSDYSALAGHLLSAIQNGAGEFVVEDYSERISRTYNPDLGLNIYDFER
3
+ >T=0.1, sample=0, score=0.7465, seq_recovery=0.5426
4
+ KMYEYKKIGNDYIVSIKNNTDLVTAIKEFCKEKKIKSGTINGIGQVKQVTLRFYNFETKEYEEKTFNENLDISNLTGIISTHNNEIFLHLHGTFGKENFSALAGHLISAIVNGKGILKVEDFKEEISTKYDEKLGLYLLDFNK/SMYKYKKIGNDYIVKINNGKNLVESLLEFVKDKNIKSGTINGTGSVSKVTLEFFDPEXXXXKTKTFNDNFDISNLTGFISTKDGKPLIDLHITIGDSDFSALAGHLIDAIVNGEANIKVEDYNVEINVRYDEELGLWLLDFNL
5
+ >T=0.1, sample=0, score=0.7500, seq_recovery=0.5851
6
+ NMYTYEKIGNKYIVSINNNTELITAIKNFCKEKKIKSGTINGIGQVSSVTLRFYNYETKTYENKTFNAQFTISNLTGIISTYNNEIFLHLHITIGDSNFSALAGHLLSAVVNGTCILIVEDFKEKISRKYNEELGLYLLDFNK/NMYKYKKIGNKYIVSINNGKELYEALLDFVKDKNIKSGSVNGTGMISKLTLSFFDPNXXXXTTKTFNMNMDISNLTGFISTKNGEPLLDLHVTVGDSDFSALAGHLVSAVVNGEADVIIENFNKEINVKYNEELGLWLLDFNL
ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_2_outputs/seqs/4YOW.fa ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ >4YOW, score=1.1024, fixed_chains=['C', 'D', 'E', 'F'], designed_chains=['A', 'B'], model_name=v_48_020
2
+ MRIVAADTGGAVLDESFQPVGLIATVAVLVEKPYKTSKRFLVKYADPYNYDLSGRQAIRDEIELAIELAREVSPDVIHLNSTLGGIEVRKLDESTIDALQISDRGKEIWKELSKDLQPLAKKFWEETGIEIIAIGKSSVPVRIAEIYAGIFSVKWALDNVKEKGGLLVGLPRYMEVEIKKDKIIGKSLDPREGGLYGEVKTEVPQGIKWELYPNPLVRRFMVFEITS/XXXX
3
+ >T=0.1, sample=0, score=0.7444, seq_recovery=0.5903
4
+ MKIVAADTGGALADENYNPIGKIATVAVLVTKPYRTSDTFLVEYLDPTKYDLSSHEGIKRELELAIELAEQVKPDLIHLDINLGGVPVAELNPEVIDKLQISEETKKILKELAKTLTPLAQAYLAKTGIPILATGDDSVPVHIAHIYASGAAVKWALENVKELKGLRVLLEEATSVEIKEDSIVVRSLDPRDGGLYGEIKTEIPEGITTELYPNPLRSNHMIFEVKT/XXXX
5
+ >T=0.1, sample=0, score=0.7336, seq_recovery=0.5551
6
+ MKIVAADTGGYLLDENYRPIGRIATVAVLVEKPYRTSDVFLVEYLDPTNYDLSSHEGILREFRLAVELAERVKPDLIHLDIDLGGVPVAELTPEVIEALQISEETKATLKELAKTLTPAAQAFLARTGIPVLAMGSRSVPVRIADIAASVAAVKYALENVKKRKGLRVGLEEAVSVEIEEKSIVGRSLDPRDGGLYFRIETEIPEGVTYELYPNPLRTNHLVFEVKV/XXXX
ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_3_outputs/seqs/3HTN.fa ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ >3HTN, score=1.1501, fixed_chains=['C'], designed_chains=['A', 'B'], model_name=v_48_020
2
+ NMYSYKKIGNKYIVSINNHTEIVKALNAFCKEKGILSGSINGIGAIGELTLRFFNPKTKAYDDKTFREQMEISNLTGNISSMNEQVYLHLHITVGRSDYSALAGHLLSAIQNGAGEFVVEDYSERISRTYNPDLGLNIYDFER/NMYSYKKIGNKYIVSINNHTEIVKALNAFCKEKGILSGSINGIGAIGELTLRFFNPKXXXXDDKTFREQMEISNLTGNISSMNEQVYLHLHITVGRSDYSALAGHLLSAIQNGAGEFVVEDYSERISRTYNPDLGLNIYDFER
3
+ >T=0.1, sample=0, score=0.7254, seq_recovery=0.5355
4
+ HMYEYKEIGNKYIVSINNNTDIVEAIKKFCEEKNIKSGTINGIGQVKSVTLRFYNFETKESKEVTINDNLTISNLTGIISTYNNEIFLDLHITIGDSNFSALAGHLLSAIVNGDCILIIEDYKENISKKYDKELGLWLLDFNK/KMYSYKKIGNKYIVNINNGKDLVTSILKFCEDKKIKSGTINGTGMISKLTLEFFDPEXXXXTTKTFNDILDISNLTGFISTKDGKVFVKLYITAGKKDFSALAGKLVSAIVNGEMNLTIEDFNVEINVEYNEELGLYLLNFNK
5
+ >T=0.1, sample=0, score=0.7494, seq_recovery=0.5745
6
+ SMYEYKKIGNSYIVSVNNNTELVEALTAFCTEKGIKSGTVNGIGQVKSVTLRFYNFETKEYKEKTFEENLEISNLTGIISTYNNKVFLDIHGTFGKSDFSALAGHLVSAIVNGKAILKVEDYKEEISRTYNEETGLWLLDFNK/KMYKYKKIGNDYIVSIKNGKNLVEAIKKFCEDKNIKSGSVNGTGQISKVTLRFFDPEXXXXTTKTFNENMDISNLTGFISTKNGEVLVDLHITVGKSNFSALAGHLVDAIVNGEADLKIEDYNVEINVEYDEKTGLWLLNFNK
ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_4_outputs/assigned_pdbs.jsonl ADDED
@@ -0,0 +1 @@
 
 
1
+ {"4YOW": [["A", "C"], ["B", "D", "E", "F"]], "3HTN": [["A", "C"], ["B"]]}
ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_4_outputs/fixed_pdbs.jsonl ADDED
@@ -0,0 +1 @@
 
 
1
+ {"4YOW": {"A": [1, 2, 3, 4, 5, 6, 7, 8, 23, 25], "C": [10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 40], "B": [], "D": [], "E": [], "F": []}, "3HTN": {"A": [1, 2, 3, 4, 5, 6, 7, 8, 23, 25], "C": [10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 40], "B": []}}
ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_4_outputs/parsed_pdbs.jsonl ADDED
The diff for this file is too large to render. See raw diff
 
ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_4_outputs/seqs/3HTN.fa ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ >3HTN, score=1.1682, fixed_chains=['B'], designed_chains=['A', 'C'], model_name=v_48_020
2
+ NMYSYKKIGNKYIVSINNHTEIVKALNAFCKEKGILSGSINGIGAIGELTLRFFNPKTKAYDDKTFREQMEISNLTGNISSMNEQVYLHLHITVGRSDYSALAGHLLSAIQNGAGEFVVEDYSERISRTYNPDLGLNIYDFER/NMYSYKKIGNKYIVSINNHTEIVKALNAFCKEKGILSGSINGIGAIGELTLRFFNPKTKAYDDKTFREQMEISNLTGNISSMNEQVYLHLHITVGRSDYSALAGHLLSAIQNGAGEFVVEDYSERISRTYNPDLGLNIYDFER
3
+ >T=0.1, sample=0, score=0.7425, seq_recovery=0.5606
4
+ NMYSYKKIGNKYIVNINNNTELVEAIKKFCKDENIKSGSINGTGQVSKVTLRFYNPETKEYKETTFNDNFDISNLTGFISTYNNEVFLDLHITIGKSNFSALAGHLLSAVVNGEMTLVVEDYNELLSMKYNEELGLYLLDFNK/NLYSYKKIGNKYIVSINNHTDIVTALKTFCEDKNIKSGTINGIGQVSSVTLRFFNIETKEVKEVTFNENLEISNLTGIISEKDGKVFLHLHGTFGKSDFSALAGHLLSAVVNGKALFEIEDFKEKVNVEYDEELGLWLLNFNK
5
+ >T=0.1, sample=0, score=0.7474, seq_recovery=0.5644
6
+ NMYSYKKIGNKYIVSINNNTNLVTAIKKFCEDKNIKSGTINGTGQVSKVTLRFYNPETKTYTDKTFNDNFDISNLTGFISTYNGKIFLHLHITIGDSNFSALAGHLIDAIVNGTADLVIEDYNENISMKYDEELGLYLLDFNK/NLYSYKKIGNKYIVSINNHTDIVEALKTFCEEKNIKSGTINGIGTVSSLTLAFRNIETGEVDRKTFNQNLEISNLTGIISTKNGKVFLDLHVTFGDSNFSALAGHLESAIVNGTALVVVEDYNEEVNVEYDEKLGLNLLNFNK
ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_4_outputs/seqs/4YOW.fa ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ >4YOW, score=1.1328, fixed_chains=['B', 'D', 'E', 'F'], designed_chains=['A', 'C'], model_name=v_48_020
2
+ MRIVAADTGGAVLDESFQPVGLIATVAVLVEKPYKTSKRFLVKYADPYNYDLSGRQAIRDEIELAIELAREVSPDVIHLNSTLGGIEVRKLDESTIDALQISDRGKEIWKELSKDLQPLAKKFWEETGIEIIAIGKSSVPVRIAEIYAGIFSVKWALDNVKEKGGLLVGLPRYMEVEIKKDKIIGKSLDPREGGLYGEVKTEVPQGIKWELYPNPLVRRFMVFEITS/MRIVAADTGGAVLDESFQPVGLIATVAVLVEKPYKTSKRFLVKYADPYNYDLSGRQAIRDEIELAIELAREVSPDVIHLNSTLGGIEVRKLDESTIDALQISDRGKEIWKELSKDLQPLAKKFWEETGIEIIAIGKSSVPVRIAEIYAGIFSVKWALDNVKEKGGLLVGLPRYMEVEIKKDKIIGKSLDPREGGLYGEVKTEVPQGIKWELYPNPLVRRFMVFEITS
3
+ >T=0.1, sample=0, score=0.7592, seq_recovery=0.6088
4
+ MRIVAADTGGALLNENYEPIGKIATVAVLVEKPYRTSKEFLVKYHDPLNYDLSSNQGIRDEVLLAIELARKVRPDMIHLDIDLGGVPLAELTPEVIEALQISEETKATLKELAKELTPLAQAFLAETGIPILCIGSRSVPVHIADIYASAEAVRWALENVKKLKGLLVGLEYATRVEIGKDSIKATSLDPRDGGLYAEVKTKIPEGITYELYPDPLRTGHMVFKITT/MKIVAADTGGAVLDESFQPVGRIATVAVVVEEPYRTSKEFLVKYLDPFKYDLSSHEGILEELELAIELAEKVKPDLIHLDLDLGGVELGELDPEVIDALQISPETRATLKELAKTLAPKARAFKEKTGIPILLTGEASVPVRIAEIYASIASVAWALEHVKELKGLRVLLEEAVSVEIEADKIVGRSLDPRDGGLYQELPTAVPEGITWELFPNPLRANHLVFEVTV
5
+ >T=0.1, sample=0, score=0.7365, seq_recovery=0.5972
6
+ MRIVAADTGGYLLDENYRPIGPIATVAVLVEKPYRTSKEFLVRYHDPENYDLTGNQGLYDEFELAIELAEKVKPDLIHLDIDLGGVPVAELTPEVINKLPISEETKKTLIELSKTLTPKAQAFYKKTGIPILAIGDRSVPVKIADIYASIAAVKWALENVKERKGLRVLLEEGVRVEIKENSIVGTSLDPRDGGLYGEIETEVPEGVTYKLYPNPLRLGHLVFEIST/MKIVAADTGGAVLDESFQPVGRIATVAVVVEEPYTTSKEFLVRYLDPFAYDLSSHEGLREEVELAIELARKVKPDLIHLDIDLGGVDVADLDEAVIDALQISPETKAVLKELAKELAPLAKAFKAETGIPILATGHRSVPVHIAHIAASGAAVKWALEHVKELKGLRVLLEEATAVEIKENKIVVTSLDPRHGGLHFEIETEVPEGIEYELFPNPLDAGHMVFEVTV
ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_5_outputs/assigned_pdbs.jsonl ADDED
@@ -0,0 +1 @@
 
 
1
+ {"4YOW": [["A", "C"], ["B", "D", "E", "F"]], "3HTN": [["A", "C"], ["B"]]}
ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_5_outputs/fixed_pdbs.jsonl ADDED
@@ -0,0 +1 @@
 
 
1
+ {"4YOW": {"A": [9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], "C": [10, 11, 18, 19, 20, 22], "B": [], "D": [], "E": [], "F": []}, "3HTN": {"A": [9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], "C": [10, 11, 18, 19, 20, 22], "B": []}}
ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_5_outputs/parsed_pdbs.jsonl ADDED
The diff for this file is too large to render. See raw diff
 
ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_5_outputs/seqs/3HTN.fa ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ >3HTN, score=1.1439, fixed_chains=['B'], designed_chains=['A', 'C'], model_name=v_48_020
2
+ NMYSYKKIGNKYIVSINNHTEIVKALNAFCKEKGILSGSINGIGAIGELTLRFFNPKTKAYDDKTFREQMEISNLTGNISSMNEQVYLHLHITVGRSDYSALAGHLLSAIQNGAGEFVVEDYSERISRTYNPDLGLNIYDFER/NMYSYKKIGNKYIVSINNHTEIVKALNAFCKEKGILSGSINGIGAIGELTLRFFNPKTKAYDDKTFREQMEISNLTGNISSMNEQVYLHLHITVGRSDYSALAGHLLSAIQNGAGEFVVEDYSERISRTYNPDLGLNIYDFER
3
+ >T=0.1, sample=0, score=0.7451, seq_recovery=0.5849
4
+ NMYKYKKIGNKYIVSINNHTEIVKAIKEFCKEKNIKSGTVNGTGQISKLTLRFYNMETKTSTDTTFNQNLDISNLTGFISEHENEVFLDLHITAGDSNFSALAGHLISAISNGTCELVVEDFKEKLSTKYDEETGLYLLDFEK/NMYKYKKIGNKYIVSINNHTEIVKAIKKFCEDKNIKSGTINGIGTISSLTLEFYDIKTKKKKLKTFNAQLEISNLTGIISTKNGEVFLDLHVTVGDSNFSALAGHLVSAVVNGTAKLVVEDYKEEVNVKYDEELGLYLLDFNL
5
+ >T=0.1, sample=0, score=0.7454, seq_recovery=0.5887
6
+ NMYKYKKIGNKYIVSINNHTEIVKAIKEFCKEKNIKSGTVNGIGQVSSVTLKFYNPETKESTLKTFNKLLDISNLTGFISTYNNEVFLDLHITFGDSDFSALAGHLVSAIVDGYAELIVEDYNENISMKYDEELGLWKLDFEK/NMYKYKKIGNKYIVSINNHTNIVKAIKKFCEDKKIKSGTINGIGQISKLTLAFRNIETGEVDLKTFNDNYTISNLTGFISTINGKVFLDLHITVGNSNFSALAGHLIDAISNGTVNLVIEDYKEEINKKYNEELGLWLLDFNL
ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_5_outputs/seqs/4YOW.fa ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ >4YOW, score=1.1085, fixed_chains=['B', 'D', 'E', 'F'], designed_chains=['A', 'C'], model_name=v_48_020
2
+ MRIVAADTGGAVLDESFQPVGLIATVAVLVEKPYKTSKRFLVKYADPYNYDLSGRQAIRDEIELAIELAREVSPDVIHLNSTLGGIEVRKLDESTIDALQISDRGKEIWKELSKDLQPLAKKFWEETGIEIIAIGKSSVPVRIAEIYAGIFSVKWALDNVKEKGGLLVGLPRYMEVEIKKDKIIGKSLDPREGGLYGEVKTEVPQGIKWELYPNPLVRRFMVFEITS/MRIVAADTGGAVLDESFQPVGLIATVAVLVEKPYKTSKRFLVKYADPYNYDLSGRQAIRDEIELAIELAREVSPDVIHLNSTLGGIEVRKLDESTIDALQISDRGKEIWKELSKDLQPLAKKFWEETGIEIIAIGKSSVPVRIAEIYAGIFSVKWALDNVKEKGGLLVGLPRYMEVEIKKDKIIGKSLDPREGGLYGEVKTEVPQGIKWELYPNPLVRRFMVFEITS
3
+ >T=0.1, sample=0, score=0.7336, seq_recovery=0.6074
4
+ MKIVAADTGGAVLDESFQPVGLIATVAVVVEKPYRTSERYKVEYLDPFNYDLTGHEGIYREIRLAIELAREVKPDLIHLDIDLGGVNVAELTPEVIDALQISAETKEVLKELAKELTPLAQEFLAETGIPILAIGDRSVPVHIADIAASGAAVKWALEHVKERKGLRVGLVYATEVEIKEDKIIVRSLDPRDGGLYFEIETEIPEGITWELYPNPLELNHMVFEVTV/MKIVAADTGGALLDENYQPVGLIATVAVVVTYPYRTSDVFKVRYLDPLAYDLASDEHLRLELELAIELAKEVKPDEIHLDLDLGGVDVASLTPEVINALQISPETKARLLELAKELAPLAAAFRKETGIPIKAVGERSVAVRIAEIYASAEAVRWALEHVKERGGLRVLLEEAVSVEIGEDSITARSLDPRHGGLYQEVPVEVPEGVTWELYPNPLRANHMIFEVTV
5
+ >T=0.1, sample=0, score=0.7137, seq_recovery=0.6143
6
+ MKIVAADTGGAVLDESFQPVGLIATVAVLVEKPYRTSDEYLVRYHDPYKYDLTGHQDLRDEVELAIELAEKVKPDLIHLDVDLGGVELATLTPEVIDALPISAETKATLKELAKTLTPLAQKFLAKTGIPIRLIGDRSVPVHIADIAASVYAVKWALENVKKHKGLRVRLVEATEVEIGENEIIGRSLDPRDGGLYFRVETKIPEGIEYKLYPDPLRRHHMVFEVTV/MKIVAADTGGALLDENYQPVGLIATVAVVVTYPYTTSDVFKVRYLDPTAYDLSSDEHLRHEVELAIELAKEVNPDEIHLDLDLGGVDVADLTPEVIDALQISPETRARLKELAKELAPLAAAFKAETGIPIKAIGERSVAVHIAEIYASIYSVKWALEHVKERKGLRVGLEEAVSVEIKEDRIIGRSLDPRDGGLYGEVEVEVPEGIEWELYPNPLRSGHMVFEVTV
ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_5_outputs/tied_pdbs.jsonl ADDED
@@ -0,0 +1 @@
 
 
1
+ {"4YOW": [{"A": [1], "C": [1]}, {"A": [2], "C": [2]}, {"A": [3], "C": [3]}, {"A": [4], "C": [4]}, {"A": [5], "C": [5]}, {"A": [6], "C": [6]}, {"A": [7], "C": [7]}, {"A": [8], "C": [8]}], "3HTN": [{"A": [1], "C": [1]}, {"A": [2], "C": [2]}, {"A": [3], "C": [3]}, {"A": [4], "C": [4]}, {"A": [5], "C": [5]}, {"A": [6], "C": [6]}, {"A": [7], "C": [7]}, {"A": [8], "C": [8]}]}
ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_7_outputs/bias_pdbs.jsonl ADDED
@@ -0,0 +1 @@
 
 
1
+ {"G": 40.1, "P": 0.3, "A": -0.05}
ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_7_outputs/parsed_pdbs.jsonl ADDED
The diff for this file is too large to render. See raw diff
 
ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_7_outputs/seqs/3HTN.fa ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ >3HTN, score=1.2048, fixed_chains=[], designed_chains=['A', 'B', 'C'], model_name=v_48_020
2
+ NMYSYKKIGNKYIVSINNHTEIVKALNAFCKEKGILSGSINGIGAIGELTLRFFNPKTKAYDDKTFREQMEISNLTGNISSMNEQVYLHLHITVGRSDYSALAGHLLSAIQNGAGEFVVEDYSERISRTYNPDLGLNIYDFER/NMYSYKKIGNKYIVSINNHTEIVKALNAFCKEKGILSGSINGIGAIGELTLRFFNPKXXXXDDKTFREQMEISNLTGNISSMNEQVYLHLHITVGRSDYSALAGHLLSAIQNGAGEFVVEDYSERISRTYNPDLGLNIYDFER/NMYSYKKIGNKYIVSINNHTEIVKALNAFCKEKGILSGSINGIGAIGELTLRFFNPKTKAYDDKTFREQMEISNLTGNISSMNEQVYLHLHITVGRSDYSALAGHLLSAIQNGAGEFVVEDYSERISRTYNPDLGLNIYDFER
3
+ >T=0.1, sample=0, score=2.4061, seq_recovery=0.0847
4
+ GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG/GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGXXXXGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG/GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG
5
+ >T=0.1, sample=0, score=2.4041, seq_recovery=0.0847
6
+ GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG/GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGXXXXGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG/GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG
ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/example_7_outputs/seqs/4YOW.fa ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ >4YOW, score=1.1322, fixed_chains=[], designed_chains=['A', 'B', 'C', 'D', 'E', 'F'], model_name=v_48_020
2
+ MRIVAADTGGAVLDESFQPVGLIATVAVLVEKPYKTSKRFLVKYADPYNYDLSGRQAIRDEIELAIELAREVSPDVIHLNSTLGGIEVRKLDESTIDALQISDRGKEIWKELSKDLQPLAKKFWEETGIEIIAIGKSSVPVRIAEIYAGIFSVKWALDNVKEKGGLLVGLPRYMEVEIKKDKIIGKSLDPREGGLYGEVKTEVPQGIKWELYPNPLVRRFMVFEITS/XXXX/MRIVAADTGGAVLDESFQPVGLIATVAVLVEKPYKTSKRFLVKYADPYNYDLSGRQAIRDEIELAIELAREVSPDVIHLNSTLGGIEVRKLDESTIDALQISDRGKEIWKELSKDLQPLAKKFWEETGIEIIAIGKSSVPVRIAEIYAGIFSVKWALDNVKEKGGLLVGLPRYMEVEIKKDKIIGKSLDPREGGLYGEVKTEVPQGIKWELYPNPLVRRFMVFEITS/XXXX/MRIVAADTGGAVLDESFQPVGLIATVAVLVEKPYKTSKRFLVKYADPYNYDLSGRQAIRDEIELAIELAREVSPDVIHLNSTLGGIEVRKLDESTIDALQISDRGKEIWKELSKDLQPLAKKFWEETGIEIIAIGKSSVPVRIAEIYAGIFSVKWALDNVKEKGGLLVGLPRYMEVEIKKDKIIGKSLDPREGGLYGEVKTEVPQGIKWELYPNPLVRRFMVFEITS/XXXX
3
+ >T=0.1, sample=0, score=2.7441, seq_recovery=0.0793
4
+ GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG/XXXX/GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG/XXXX/GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG/XXXX
5
+ >T=0.1, sample=0, score=2.6962, seq_recovery=0.0793
6
+ GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG/XXXX/GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG/XXXX/GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG/XXXX
ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/pdbs/3HTN.pdb ADDED
The diff for this file is too large to render. See raw diff
 
ProteinMPNN/vanilla_proteinmpnn/PDB_complexes/pdbs/4YOW.pdb ADDED
The diff for this file is too large to render. See raw diff
 
ProteinMPNN/vanilla_proteinmpnn/PDB_homooligomers/example_6_outputs/parsed_pdbs.jsonl ADDED
The diff for this file is too large to render. See raw diff
 
ProteinMPNN/vanilla_proteinmpnn/PDB_homooligomers/example_6_outputs/seqs/4GYT.fa ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ >4GYT, score=1.6782, fixed_chains=[], designed_chains=['A', 'B'], model_name=v_48_020
2
+ SLHLPKYDDFVQSISVLALTMSGSELHGIMCGYLCAGADSQGEAYIRALLNNKKDEQSRNALLSMFSVFSISQQQMNNFDFEFEMLLPDDDESLVTRAQAFSEWCEGFTQGLTIAGVGMEQFYEEESQDALQHLMEFAELDCESLEVGEEDERALMEVSEYTRMAVLRLHSDLVLHE/SLHLPKYDDFVQSISVLALTMSGSELHGIMCGYLCAGADSQGEAYIRALLNNKKDEQSRNALLSMFSVFSISQQQMNNFDFEFEMLLPDDDESLVTRAQAFSEWCEGFTQGLTIAGVGMEQFYEEESQDALQHLMEFAELDCESLEVGEEDERALMEVSEYTRMAVLRLHSDLVLHE
3
+ >T=0.2, sample=0, score=0.8880, seq_recovery=0.4463
4
+ ELRLPPYEEFAAAIAVLQLPVSPSELAGLILGYLAAGKIDLGRAWIRSLLGGRTDAASQAALAALLEVFDILEEQLNNPELELELLLPPPDASLRERARALSEFARGFALGLELAGVDKESFKTEESKEAYERILELARLDASALREGPADRARLAELEEWLREAILQIHDDLVNHG/ELRLPPYEEFAAAIAVLQLPVSPSELAGLILGYLAAGKIDLGRAWIRSLLGGRTDAASQAALAALLEVFDILEEQLNNPELELELLLPPPDASLRERARALSEFARGFALGLELAGVDKESFKTEESKEAYERILELARLDASALREGPADRARLAELEEWLREAILQIHDDLVNHG
5
+ >T=0.2, sample=0, score=0.8673, seq_recovery=0.3898
6
+ SLALPPYDEFAAAVAPLKLPFSASYLAGLILGFIVAGKLELGRAWIKSLAKGKTDAATQAAVAALLDVFEILTRQLNDSSLELELLLPPKDASLKERAKALSEFAKGFVEGLELAGVTEESFSKESSKKAYKEIKELAKMDVSKLKEGEEDEKELEEKKEWLKNSILEIHKDLKENK/SLALPPYDEFAAAVAPLKLPFSASYLAGLILGFIVAGKLELGRAWIKSLAKGKTDAATQAAVAALLDVFEILTRQLNDSSLELELLLPPKDASLKERAKALSEFAKGFVEGLELAGVTEESFSKESSKKAYKEIKELAKMDVSKLKEGEEDEKELEEKKEWLKNSILEIHKDLKENK
ProteinMPNN/vanilla_proteinmpnn/PDB_homooligomers/example_6_outputs/seqs/6EHB.fa ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ >6EHB, score=1.3507, fixed_chains=[], designed_chains=['A', 'B', 'C'], model_name=v_48_020
2
+ DGINQSGDKAGSTVYSAKGTSLEVGGRAEARLSLKDGKAQDNSRVRLNFLGKAEINDSLYGVGFYEGEFTTNDQGKNASNNSLDNRYTYAGIGGTYGEVTYGKNDGALGVITDFTDIMSYHGNTAAEKIAVADRVDNMLAYKGQFGDLGVKASYRFADRNAVDAMGNVVTETNAAKYSDNGEDGYSLSAIYTFGDTGFNVGAGYADQDDQNEYMLAASYRMENLYFAGLFTDGELAKDVDYTGYELAAGYKLGQAAFTATYNNAETAKKTSADNFAIDATYYFKPNFRSYISYQFNLLDSXXASKVASEDELAIGLRYDF/DGINQSGDKAGSTVYSAKGTSLEVGGRAEARLSLKDGKAQDNSRVRLNFLGKAEINDSLYGVGFYEGEFTTNDQGKNASNNSLDNRYTYAGIGGTYGEVTYGKNDGALGVITDFTDIMSYHGNTAAEKIAVADRVDNMLAYKGQFGDLGVKASYRFADRNAVDAMGNVVTETNAAKYSDNGEDGYSLSAIYTFGDTGFNVGAGYADQDDQNEYMLAASYRMENLYFAGLFTDGELAKDVDYTGYELAAGYKLGQAAFTATYNNAETAKKTSADNFAIDATYYFKPNFRSYISYQFNLLDXXXASKVASEDELAIGLRYDF/DGINQSGDKAGSTVYSAKGTSLEVGGRAEARLSLKDGKAQDNSRVRLNFLGKAEINDSLYGVGFYEGEFTTNDQGKNASNNSLDNRYTYAGIGGTYGEVTYGKNDGALGVITDFTDIMSYHGNTAAEKIAVADRVDNMLAYKGQFGDLGVKASYRFADRNAVDAMGNVVTETNAAKYSDNGEDGYSLSAIYTFGDTGFNVGAGYADQDDQNEYMLAASYRMENLYFAGLFTDGELAKDVDYTGYELAAGYKLGQAAFTATYNNAETAKKTSADNFAIDATYYFKPNFRSYISYQFNLLDSDKASKVASEDELAIGLRYDF
3
+ >T=0.2, sample=0, score=0.8934, seq_recovery=0.5435
4
+ GGRNLSGPKPGQTVYSSNGSTLKIGGFADANLDIVDGKAKDNSKGRVSLLRTDKINDDLYGVGYIEVELTTNDNGTNAINNNLNVKKLYAGIGGKWGTVTYGKNDGALQIIADFTDIMPYGGNKAAPLIPVADNVDNTLSYSATYGDLTVRAAYQFATQVAVDSNGNVVDEENAAKYSDNGKDGWSASAVYDFGDSGWSVGAGAAKQGDQWAVAVAASYEKDNFYVAALYVAGDLAEGVPYAGLSLAASYKVGNTTYTVNYDVAFVDGKVSEDVLSYGVTYKFTDRFSTYVEYEDNLLDXXXASYVDSADVLRVGIRTDF/GGRNLSGPKPGQTVYSSNGSTLKIGGFADANLDIVDGKAKDNSKGRVSLLRTDKINDDLYGVGYIEVELTTNDNGTNAINNNLNVKKLYAGIGGKWGTVTYGKNDGALQIIADFTDIMPYGGNKAAPLIPVADNVDNTLSYSATYGDLTVRAAYQFATQVAVDSNGNVVDEENAAKYSDNGKDGWSASAVYDFGDSGWSVGAGAAKQGDQWAVAVAASYEKDNFYVAALYVAGDLAEGVPYAGLSLAASYKVGNTTYTVNYDVAFVDGKVSEDVLSYGVTYKFTDRFSTYVEYEDNLLDXXXASYVDSADVLRVGIRTDF/GGRNLSGPKPGQTVYSSNGSTLKIGGFADANLDIVDGKAKDNSKGRVSLLRTDKINDDLYGVGYIEVELTTNDNGTNAINNNLNVKKLYAGIGGKWGTVTYGKNDGALQIIADFTDIMPYGGNKAAPLIPVADNVDNTLSYSATYGDLTVRAAYQFATQVAVDSNGNVVDEENAAKYSDNGKDGWSASAVYDFGDSGWSVGAGAAKQGDQWAVAVAASYEKDNFYVAALYVAGDLAEGVPYAGLSLAASYKVGNTTYTVNYDVAFVDGKVSEDVLSYGVTYKFTDRFSTYVEYEDNLLDXXXASYVDSADVLRVGIRTDF
5
+ >T=0.2, sample=0, score=0.8911, seq_recovery=0.5497
6
+ GGLNLSSPKAGKTVYESGGSTLELGGRADAILKVVDGKFEDKSYGTVSLLKTDQINDDLYGTGYVELEFTVNDNGTNAVNNNLNNVKLYAGIGGKWGTVTYGKNDGALKPIRDFTDIMPYGGNRAAPLIPVADNIDNTLSYSATYGNLSVRASYRFANRIYVDENGNVVAKEEAARVSDNGNDGWSASAIYDFGDTGISVGAGAAHQGDQWQVALAASYKKDNFYVAALLTAGQLAKDVPYLGLSLAASYDFGNWRFTASYDLALVDGKVSEDRLTYGVTYDFTPNFSVSVEYTDNLLDXXXSSYVDSLDELVLGVRTDF/GGLNLSSPKAGKTVYESGGSTLELGGRADAILKVVDGKFEDKSYGTVSLLKTDQINDDLYGTGYVELEFTVNDNGTNAVNNNLNNVKLYAGIGGKWGTVTYGKNDGALKPIRDFTDIMPYGGNRAAPLIPVADNIDNTLSYSATYGNLSVRASYRFANRIYVDENGNVVAKEEAARVSDNGNDGWSASAIYDFGDTGISVGAGAAHQGDQWQVALAASYKKDNFYVAALLTAGQLAKDVPYLGLSLAASYDFGNWRFTASYDLALVDGKVSEDRLTYGVTYDFTPNFSVSVEYTDNLLDXXXSSYVDSLDELVLGVRTDF/GGLNLSSPKAGKTVYESGGSTLELGGRADAILKVVDGKFEDKSYGTVSLLKTDQINDDLYGTGYVELEFTVNDNGTNAVNNNLNNVKLYAGIGGKWGTVTYGKNDGALKPIRDFTDIMPYGGNRAAPLIPVADNIDNTLSYSATYGNLSVRASYRFANRIYVDENGNVVAKEEAARVSDNGNDGWSASAIYDFGDTGISVGAGAAHQGDQWQVALAASYKKDNFYVAALLTAGQLAKDVPYLGLSLAASYDFGNWRFTASYDLALVDGKVSEDRLTYGVTYDFTPNFSVSVEYTDNLLDXXXSSYVDSLDELVLGVRTDF
ProteinMPNN/vanilla_proteinmpnn/PDB_homooligomers/example_6_outputs/tied_pdbs.jsonl ADDED
@@ -0,0 +1 @@
 
 
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ProteinMPNN/vanilla_proteinmpnn/PDB_homooligomers/pdbs/4GYT.pdb ADDED
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ProteinMPNN/vanilla_proteinmpnn/PDB_homooligomers/pdbs/6EHB.pdb ADDED
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ProteinMPNN/vanilla_proteinmpnn/PDB_monomers/example_1_outputs/parsed_pdbs.jsonl ADDED
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55.737], [17.945, 16.136, 55.375], [17.279, 13.513, 53.17], [16.307, 10.372, 53.735], [16.304, 6.85, 53.277], [14.36, 4.368, 54.732], [14.218, 1.096, 55.78], [12.396, -1.822, 56.732], [11.509, -5.193, 57.372], [10.625, -5.836, 54.804], [9.326, -3.567, 53.956], [9.713, -0.36, 52.528], [10.495, 2.99, 53.244], [10.35, 6.575, 52.736], [11.862, 9.008, 54.514], [12.232, 12.164, 56.203], [14.836, 14.014, 57.563], [15.572, 16.566, 59.658], [18.051, 18.728, 60.656], [19.208, 20.958, 63.088], [22.156, 22.563, 63.663], [23.645, 24.166, 66.175], [26.134, 26.306, 67.255], [28.264, 24.429, 66.79], [27.157, 22.089, 67.871], [24.652, 19.433, 68.054], [21.475, 20.587, 67.962], [18.615, 20.547, 67.532], [16.573, 18.007, 66.237], [14.476, 16.857, 63.707], [12.331, 14.421, 62.706], [10.453, 13.297, 60.044], [7.846, 11.327, 58.803], [5.177, 10.579, 57.057]], "CA_chain_A": [[36.936, 18.773, 53.168], [33.829, 19.307, 55.268], [33.003, 22.335, 57.475], [32.383, 21.616, 61.147], [28.63, 22.278, 61.041], [27.969, 19.998, 58.095], [30.255, 17.336, 59.605], [28.319, 17.193, 62.883], [24.978, 16.74, 61.124], [26.544, 14.088, 58.891], [27.832, 12.133, 61.893], [24.312, 12.112, 63.413], [23.007, 10.631, 60.175], [25.91, 8.164, 60.045], [25.045, 6.895, 63.536], [21.519, 6.158, 62.308], [22.821, 4.501, 59.135], [25.19, 2.299, 61.114], [22.592, 1.729, 63.824], [25.424, 2.426, 66.209], [24.548, 3.825, 69.667], [28.216, 4.325, 70.466], [28.703, 6.763, 67.587], [25.452, 8.572, 68.459], [26.576, 9.042, 72.062], [29.65, 10.906, 70.821], [27.638, 13.226, 68.553], [24.834, 14.469, 70.801], [24.761, 16.77, 73.826], [22.683, 16.47, 77.021], [20.351, 19.16, 75.595], [19.926, 17.722, 72.07], [16.203, 17.421, 71.347], [14.694, 15.122, 68.734], [11.136, 14.692, 67.52], [10.007, 11.689, 65.501], [6.346, 11.777, 64.428], [5.194, 13.191, 67.767], [7.53, 11.223, 70.04], [10.037, 13.505, 71.773], [13.571, 12.523, 72.816], [16.186, 14.512, 74.768], [19.828, 13.667, 75.507], [20.521, 9.981, 76.157], [17.012, 9.105, 74.937], [18.376, 9.832, 71.466], [20.98, 7.138, 72.128], [18.19, 4.839, 73.298], [16.414, 5.594, 69.984], [19.465, 4.246, 68.135], [19.602, 1.154, 70.325], [16.045, 0.407, 69.247], [16.922, 0.85, 65.574], [19.706, -1.684, 66.003], [17.363, -4.009, 67.906], [14.558, -3.966, 65.32], [17.02, -4.306, 62.438], [15.647, -1.091, 60.918], [17.816, 0.015, 57.986], [18.627, 3.687, 57.284], [20.338, 4.921, 54.124], [21.169, 8.544, 53.342], [20.076, 9.473, 49.814], [20.579, 13.205, 49.457], [20.616, 16.466, 51.352], [20.718, 20.236, 51.207], [21.091, 23.102, 53.628], [17.708, 24.807, 54.042], [18.051, 28.065, 55.926], [20.011, 27.214, 59.057], [19.161, 23.525, 59.1], [20.28, 20.496, 57.163], [17.581, 18.51, 55.443], [18.39, 14.859, 54.825], [16.355, 12.518, 52.643], [16.831, 9.069, 54.073], [15.447, 5.708, 53.01], [14.308, 3.533, 55.936], [13.632, -0.203, 55.453], [12.292, -2.933, 57.665], [10.532, -6.258, 57.193], [10.11, -5.81, 53.444], [8.72, -2.286, 53.633], [10.651, 0.732, 52.332], [9.888, 4.302, 53.432], [11.125, 7.801, 52.577], [11.761, 9.852, 55.689], [12.975, 13.407, 56.146], [15.508, 14.281, 58.831], [15.997, 17.954, 59.618], [18.953, 18.92, 61.785], [19.776, 22.273, 63.355], [23.418, 22.556, 64.378], [23.916, 25.488, 66.721], [27.402, 26.364, 67.981], [28.974, 23.169, 66.639], [26.507, 20.955, 68.531], [23.321, 19.08, 67.562], [20.524, 21.296, 68.816], [17.262, 20.046, 67.347], [16.415, 17.077, 65.126], [13.085, 16.599, 63.409], [12.153, 13.313, 61.776], [9.088, 13.272, 59.537], [7.438, 10.301, 57.858], [3.735, 10.392, 57.071]], "C_chain_A": [[35.516, 19.181, 53.529], [33.835, 20.246, 56.472], [32.424, 21.668, 58.723], [30.875, 21.424, 61.352], [28.045, 21.144, 60.221], [28.428, 18.625, 58.602], [29.459, 16.766, 60.777], [26.977, 16.58, 62.491], [25.152, 15.408, 60.402], [26.83, 12.921, 59.83], [26.562, 11.609, 62.585], [23.641, 11.12, 62.474], [23.742, 9.311, 60.031], [25.597, 7.187, 61.182], [23.732, 6.145, 63.321], [21.733, 5.074, 61.25], [23.514, 3.243, 59.644], [24.422, 1.619, 62.24], [23.493, 1.584, 65.033], [25.271, 3.432, 67.346], [25.829, 4.473, 70.157], [28.568, 5.591, 69.685], [27.729, 7.873, 67.973], [25.765, 9.271, 69.778], [27.63, 10.133, 71.946], [29.225, 12.164, 70.07], [26.725, 14.189, 69.313], [25.32, 15.147, 72.075], [23.586, 16.509, 74.765], [21.4, 17.227, 76.67], [19.883, 19.001, 74.14], [18.479, 17.252, 72.148], [15.814, 16.879, 69.984], [13.215, 14.791, 68.796], [11.019, 13.732, 66.357], [8.511, 11.383, 65.495], [5.617, 11.906, 65.766], [5.97, 12.779, 69.006], [8.693, 12.188, 70.248], [11.14, 12.773, 72.507], [14.571, 13.629, 73.143], [17.412, 13.767, 75.307], [19.981, 12.179, 75.234], [19.346, 9.184, 75.593], [17.272, 8.974, 73.437], [19.264, 8.61, 71.249], [20.105, 5.892, 72.234], [17.42, 4.6, 71.996], [17.271, 4.767, 69.027], [19.569, 2.782, 68.557], [18.408, 0.378, 69.783], [16.076, 0.161, 67.749], [17.732, -0.411, 65.332], [18.974, -2.968, 66.397], [16.379, -4.492, 66.836], [15.096, -4.299, 63.928], [16.594, -3.31, 61.369], [16.837, -0.53, 60.144], [17.671, 1.442, 57.459], [19.7, 3.886, 56.222], [20.172, 6.366, 53.668], [21.132, 8.588, 51.81], [20.646, 10.787, 49.314], [20.267, 14.245, 50.522], [21.046, 17.824, 50.859], [20.686, 21.006, 52.51], [19.918, 24.019, 53.329], [17.639, 25.811, 55.176], [19.214, 27.689, 56.819], [19.807, 25.792, 59.584], [19.526, 22.664, 57.913], [19.11, 19.552, 56.986], [18.028, 17.274, 54.701], [17.2, 14.029, 54.397], [16.961, 11.15, 52.89], [15.812, 8.012, 53.681], [15.513, 4.779, 54.22], [13.552, 2.243, 55.625], [13.475, -1.054, 56.689], [11.196, -3.906, 57.263], [9.861, -6.201, 55.824], [9.414, -4.523, 53.038], [9.757, -1.167, 53.588], [9.951, 2.083, 52.441], [10.884, 5.43, 53.152], [10.835, 8.756, 53.723], [12.571, 11.114, 55.473], [13.554, 13.659, 57.513], [16.084, 15.679, 58.809], [16.836, 18.239, 60.853], [19.393, 20.364, 61.913], [21.031, 22.116, 64.2], [23.671, 23.966, 64.867], [25.19, 25.419, 67.548], [28.163, 25.044, 67.963], [28.334, 21.936, 67.267], [25.122, 20.677, 67.946], [22.274, 19.664, 68.485], [19.119, 20.714, 68.743], [17.234, 19.151, 66.116], [14.943, 16.776, 64.944], [12.96, 15.527, 62.351], [10.703, 13.266, 61.349], [8.891, 12.117, 58.572], [5.949, 10.026, 57.982], [3.267, 9.765, 55.77]], "O_chain_A": [[34.75, 19.627, 52.679], [34.466, 19.951, 57.486], [31.745, 20.64, 58.632], [30.444, 20.43, 61.936], [27.223, 20.37, 60.71], [27.666, 17.644, 58.56], [29.228, 15.556, 60.851], [26.587, 15.54, 63.01], [24.344, 14.49, 60.563], [26.43, 11.793, 59.561], [26.466, 10.42, 62.876], [23.133, 10.086, 62.899], [23.11, 8.253, 59.861], [25.565, 5.978, 60.976], [23.587, 5.004, 63.732], [21.208, 3.961, 61.365], [23.214, 2.143, 59.19], [24.797, 0.534, 62.681], [23.414, 0.602, 65.768], [25.628, 4.597, 67.202], [25.794, 5.601, 70.653], [28.946, 6.599, 70.283], [28.147, 9.011, 68.154], [25.567, 10.478, 69.921], [27.585, 11.13, 72.657], [29.904, 13.182, 70.145], [26.795, 15.401, 69.13], [26.4, 14.846, 72.582], [22.509, 16.12, 74.303], [20.32, 16.76, 77.023], [19.016, 19.744, 73.693], [18.171, 16.339, 72.914], [16.109, 17.485, 68.964], [12.702, 14.379, 69.847], [11.564, 13.984, 65.281], [8.025, 10.649, 66.357], [4.861, 11.02, 66.147], [5.876, 13.441, 70.042], [9.374, 12.559, 69.286], [10.86, 11.923, 73.362], [14.807, 14.512, 72.329], [17.271, 12.751, 75.992], [19.846, 11.725, 74.084], [19.535, 8.068, 75.133], [16.802, 8.045, 72.793], [19.141, 7.904, 70.238], [20.301, 4.917, 71.505], [17.186, 3.453, 71.614], [16.744, 3.928, 68.26], [19.568, 1.88, 67.714], [18.527, -0.796, 69.411], [15.514, -0.823, 67.273], [17.375, -1.244, 64.501], [19.117, -3.994, 65.744], [16.312, -5.687, 66.557], [14.345, -4.733, 63.062], [16.717, -3.592, 60.189], [17.874, -0.25, 60.724], [16.729, 1.724, 56.722], [20.833, 3.459, 56.399], [19.101, 6.761, 53.213], [21.922, 7.924, 51.14], [21.54, 10.807, 48.463], [19.441, 14.03, 51.403], [21.88, 17.95, 49.948], [20.154, 20.521, 53.506], [19.91, 24.707, 52.301], [17.308, 25.452, 56.305], [20.323, 27.467, 56.337], [20.162, 25.477, 60.718], [19.425, 23.084, 56.754], [18.544, 19.031, 57.959], [18.427, 17.355, 53.535], [16.231, 13.89, 55.157], [18.022, 10.811, 52.366], [14.608, 8.257, 53.699], [16.601, 4.461, 54.699], [12.391, 2.3, 55.229], [14.338, -1.043, 57.557], [10.101, -3.503, 56.86], [8.668, -6.468, 55.697], [8.959, -4.394, 51.905], [10.569, -1.042, 54.499], [8.934, 2.298, 51.817], [12.103, 5.252, 53.305], [9.722, 9.241, 53.893], [13.499, 11.135, 54.676], [12.845, 13.511, 58.513], [16.983, 15.964, 58.026], [16.401, 17.994, 61.976], [19.882, 20.939, 60.945], [20.986, 21.581, 65.294], [23.86, 24.873, 64.065], [25.32, 24.562, 68.418], [28.641, 24.59, 68.998], [28.902, 20.843, 67.213], [24.492, 21.583, 67.413], [22.176, 19.279, 69.665], [18.494, 20.423, 69.772], [17.819, 19.495, 65.079], [14.236, 16.503, 65.911], [13.432, 15.7, 61.239], [9.814, 13.225, 62.192], [9.69, 11.926, 57.664], [5.503, 9.336, 58.905], [3.96, 9.844, 54.754]]}, "name": "5L33", "num_of_chains": 1, "seq": "HMPEEEKAARLFIEALEKGDPELMRKVISPDTRMEDNGREFTGDEVVEYVKEIQKRGEQWHLRRYTKEGNSWRFEVQVDNNGQTEQWEVQIEVRNGRIKRVTITHV"}
ProteinMPNN/vanilla_proteinmpnn/PDB_monomers/example_1_outputs/seqs/5L33.fa ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ >5L33, score=1.5848, fixed_chains=[], designed_chains=['A'], model_name=v_48_020
2
+ HMPEEEKAARLFIEALEKGDPELMRKVISPDTRMEDNGREFTGDEVVEYVKEIQKRGEQWHLRRYTKEGNSWRFEVQVDNNGQTEQWEVQIEVRNGRIKRVTITHV
3
+ >T=0.1, sample=0, score=0.8419, seq_recovery=0.4245
4
+ SVDPETAKALAFVKALEKADPELMAKVITPDTEMEVNGKKYKGDEIVEYVKKLKEEGIKYKLLSYKKDGDKYVFTMEKSYKGKTYTVTIEIEVKDGKVAKVVITEK
5
+ >T=0.1, sample=0, score=0.8087, seq_recovery=0.4811
6
+ SINEEEKKALDFIEALEKADPELMKKVIEPDTKMEVNGKKYEGEEIVKFVEELKKSGVKYKLKSYKKEGDKYVFTVEKSENGKTYTVTIEVKVENGKVKEVKITEE
ProteinMPNN/vanilla_proteinmpnn/PDB_monomers/example_1_outputs/seqs/6MRR.fa ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ >6MRR, score=1.4854, fixed_chains=[], designed_chains=['A'], model_name=v_48_020
2
+ GWSTELEKHREELKEFLKKEGITNVEIRIDNGRLEVRVEGGTERLKRFLEELRQKLEKKGYTVDIKIE
3
+ >T=0.1, sample=0, score=0.9197, seq_recovery=0.5147
4
+ GIDPELEEKVEELKKFLKEKGIDNVEIEVEDGVLKIKVKGASEELKEFLKKLKEELEEKGYEVEVEIE
5
+ >T=0.1, sample=0, score=0.9356, seq_recovery=0.5588
6
+ GKDPELEKYVKELKEFLKKQGITNVKIEVKDGTLTITTKGASEELKKFLEKLKKELEAKGYKVNVKIE
ProteinMPNN/vanilla_proteinmpnn/PDB_monomers/pdbs/5L33.pdb ADDED
The diff for this file is too large to render. See raw diff
 
ProteinMPNN/vanilla_proteinmpnn/PDB_monomers/pdbs/6MRR.pdb ADDED
The diff for this file is too large to render. See raw diff
 
ProteinMPNN/vanilla_proteinmpnn/examples/submit_example_1.sh ADDED
@@ -0,0 +1,27 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/bin/bash
2
+ #SBATCH -p gpu
3
+ #SBATCH --mem=32g
4
+ #SBATCH --gres=gpu:rtx2080:1
5
+ #SBATCH -c 2
6
+ #SBATCH --output=example_1.out
7
+
8
+ source activate mlfold
9
+
10
+ folder_with_pdbs="../PDB_monomers/pdbs/"
11
+
12
+ output_dir="../PDB_monomers/example_1_outputs"
13
+ if [ ! -d $output_dir ]
14
+ then
15
+ mkdir -p $output_dir
16
+ fi
17
+
18
+ path_for_parsed_chains=$output_dir"/parsed_pdbs.jsonl"
19
+
20
+ python ../helper_scripts/parse_multiple_chains.py --input_path=$folder_with_pdbs --output_path=$path_for_parsed_chains
21
+
22
+ python ../protein_mpnn_run.py \
23
+ --jsonl_path $path_for_parsed_chains \
24
+ --out_folder $output_dir \
25
+ --num_seq_per_target 2 \
26
+ --sampling_temp "0.1" \
27
+ --batch_size 1
ProteinMPNN/vanilla_proteinmpnn/examples/submit_example_2.sh ADDED
@@ -0,0 +1,32 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/bin/bash
2
+ #SBATCH -p gpu
3
+ #SBATCH --mem=32g
4
+ #SBATCH --gres=gpu:rtx2080:1
5
+ #SBATCH -c 2
6
+ #SBATCH --output=example_2.out
7
+
8
+ source activate mlfold
9
+
10
+ folder_with_pdbs="../PDB_complexes/pdbs/"
11
+
12
+ output_dir="../PDB_complexes/example_2_outputs"
13
+ if [ ! -d $output_dir ]
14
+ then
15
+ mkdir -p $output_dir
16
+ fi
17
+
18
+ path_for_parsed_chains=$output_dir"/parsed_pdbs.jsonl"
19
+ path_for_assigned_chains=$output_dir"/assigned_pdbs.jsonl"
20
+ chains_to_design="A B"
21
+
22
+ python ../helper_scripts/parse_multiple_chains.py --input_path=$folder_with_pdbs --output_path=$path_for_parsed_chains
23
+
24
+ python ../helper_scripts/assign_fixed_chains.py --input_path=$path_for_parsed_chains --output_path=$path_for_assigned_chains --chain_list "$chains_to_design"
25
+
26
+ python ../protein_mpnn_run.py \
27
+ --jsonl_path $path_for_parsed_chains \
28
+ --chain_id_jsonl $path_for_assigned_chains \
29
+ --out_folder $output_dir \
30
+ --num_seq_per_target 2 \
31
+ --sampling_temp "0.1" \
32
+ --batch_size 1
ProteinMPNN/vanilla_proteinmpnn/examples/submit_example_3.sh ADDED
@@ -0,0 +1,26 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/bin/bash
2
+ #SBATCH -p gpu
3
+ #SBATCH --mem=32g
4
+ #SBATCH --gres=gpu:rtx2080:1
5
+ #SBATCH -c 3
6
+ #SBATCH --output=example_3.out
7
+
8
+ source activate mlfold
9
+
10
+ path_to_PDB="../PDB_complexes/pdbs/3HTN.pdb"
11
+
12
+ output_dir="../PDB_complexes/example_3_outputs"
13
+ if [ ! -d $output_dir ]
14
+ then
15
+ mkdir -p $output_dir
16
+ fi
17
+
18
+ chains_to_design="A B"
19
+
20
+ python ../protein_mpnn_run.py \
21
+ --pdb_path $path_to_PDB \
22
+ --pdb_path_chains "$chains_to_design" \
23
+ --out_folder $output_dir \
24
+ --num_seq_per_target 2 \
25
+ --sampling_temp "0.1" \
26
+ --batch_size 1
ProteinMPNN/vanilla_proteinmpnn/examples/submit_example_4.sh ADDED
@@ -0,0 +1,39 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/bin/bash
2
+ #SBATCH -p gpu
3
+ #SBATCH --mem=32g
4
+ #SBATCH --gres=gpu:rtx2080:1
5
+ #SBATCH -c 3
6
+ #SBATCH --output=example_4.out
7
+
8
+ source activate mlfold
9
+
10
+ folder_with_pdbs="../PDB_complexes/pdbs/"
11
+
12
+ output_dir="../PDB_complexes/example_4_outputs"
13
+ if [ ! -d $output_dir ]
14
+ then
15
+ mkdir -p $output_dir
16
+ fi
17
+
18
+
19
+ path_for_parsed_chains=$output_dir"/parsed_pdbs.jsonl"
20
+ path_for_assigned_chains=$output_dir"/assigned_pdbs.jsonl"
21
+ path_for_fixed_positions=$output_dir"/fixed_pdbs.jsonl"
22
+ chains_to_design="A C"
23
+ #The first amino acid in the chain corresponds to 1 and not PDB residues index for now.
24
+ fixed_positions="1 2 3 4 5 6 7 8 23 25, 10 11 12 13 14 15 16 17 18 19 20 40" #fixing/not designing residues 1 2 3...25 in chain A and residues 10 11 12...40 in chain C
25
+
26
+ python ../helper_scripts/parse_multiple_chains.py --input_path=$folder_with_pdbs --output_path=$path_for_parsed_chains
27
+
28
+ python ../helper_scripts/assign_fixed_chains.py --input_path=$path_for_parsed_chains --output_path=$path_for_assigned_chains --chain_list "$chains_to_design"
29
+
30
+ python ../helper_scripts/make_fixed_positions_dict.py --input_path=$path_for_parsed_chains --output_path=$path_for_fixed_positions --chain_list "$chains_to_design" --position_list "$fixed_positions"
31
+
32
+ python ../protein_mpnn_run.py \
33
+ --jsonl_path $path_for_parsed_chains \
34
+ --chain_id_jsonl $path_for_assigned_chains \
35
+ --fixed_positions_jsonl $path_for_fixed_positions \
36
+ --out_folder $output_dir \
37
+ --num_seq_per_target 2 \
38
+ --sampling_temp "0.1" \
39
+ --batch_size 1
ProteinMPNN/vanilla_proteinmpnn/examples/submit_example_5.sh ADDED
@@ -0,0 +1,43 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/bin/bash
2
+ #SBATCH -p gpu
3
+ #SBATCH --mem=32g
4
+ #SBATCH --gres=gpu:rtx2080:1
5
+ #SBATCH -c 3
6
+ #SBATCH --output=example_5.out
7
+
8
+ source activate mlfold
9
+
10
+ folder_with_pdbs="../PDB_complexes/pdbs/"
11
+
12
+ output_dir="../PDB_complexes/example_5_outputs"
13
+ if [ ! -d $output_dir ]
14
+ then
15
+ mkdir -p $output_dir
16
+ fi
17
+
18
+
19
+ path_for_parsed_chains=$output_dir"/parsed_pdbs.jsonl"
20
+ path_for_assigned_chains=$output_dir"/assigned_pdbs.jsonl"
21
+ path_for_fixed_positions=$output_dir"/fixed_pdbs.jsonl"
22
+ path_for_tied_positions=$output_dir"/tied_pdbs.jsonl"
23
+ chains_to_design="A C"
24
+ fixed_positions="9 10 11 12 13 14 15 16 17 18 19 20 21 22 23, 10 11 18 19 20 22"
25
+ tied_positions="1 2 3 4 5 6 7 8, 1 2 3 4 5 6 7 8" #two list must match in length; residue 1 in chain A and C will be sampled togther;
26
+
27
+ python ../helper_scripts/parse_multiple_chains.py --input_path=$folder_with_pdbs --output_path=$path_for_parsed_chains
28
+
29
+ python ../helper_scripts/assign_fixed_chains.py --input_path=$path_for_parsed_chains --output_path=$path_for_assigned_chains --chain_list "$chains_to_design"
30
+
31
+ python ../helper_scripts/make_fixed_positions_dict.py --input_path=$path_for_parsed_chains --output_path=$path_for_fixed_positions --chain_list "$chains_to_design" --position_list "$fixed_positions"
32
+
33
+ python ../helper_scripts/make_tied_positions_dict.py --input_path=$path_for_parsed_chains --output_path=$path_for_tied_positions --chain_list "$chains_to_design" --position_list "$tied_positions"
34
+
35
+ python ../protein_mpnn_run.py \
36
+ --jsonl_path $path_for_parsed_chains \
37
+ --chain_id_jsonl $path_for_assigned_chains \
38
+ --fixed_positions_jsonl $path_for_fixed_positions \
39
+ --tied_positions_jsonl $path_for_tied_positions \
40
+ --out_folder $output_dir \
41
+ --num_seq_per_target 2 \
42
+ --sampling_temp "0.1" \
43
+ --batch_size 1
ProteinMPNN/vanilla_proteinmpnn/examples/submit_example_6.sh ADDED
@@ -0,0 +1,33 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/bin/bash
2
+ #SBATCH -p gpu
3
+ #SBATCH --mem=32g
4
+ #SBATCH --gres=gpu:rtx2080:1
5
+ #SBATCH -c 3
6
+ #SBATCH --output=example_6.out
7
+
8
+ source activate mlfold
9
+
10
+ folder_with_pdbs="../PDB_homooligomers/pdbs/"
11
+
12
+ output_dir="../PDB_homooligomers/example_6_outputs"
13
+ if [ ! -d $output_dir ]
14
+ then
15
+ mkdir -p $output_dir
16
+ fi
17
+
18
+
19
+ path_for_parsed_chains=$output_dir"/parsed_pdbs.jsonl"
20
+ path_for_tied_positions=$output_dir"/tied_pdbs.jsonl"
21
+ path_for_designed_sequences=$output_dir"/temp_0.1"
22
+
23
+ python ../helper_scripts/parse_multiple_chains.py --input_path=$folder_with_pdbs --output_path=$path_for_parsed_chains
24
+
25
+ python ../helper_scripts/make_tied_positions_dict.py --input_path=$path_for_parsed_chains --output_path=$path_for_tied_positions --homooligomer 1
26
+
27
+ python ../protein_mpnn_run.py \
28
+ --jsonl_path $path_for_parsed_chains \
29
+ --tied_positions_jsonl $path_for_tied_positions \
30
+ --out_folder $output_dir \
31
+ --num_seq_per_target 2 \
32
+ --sampling_temp "0.2" \
33
+ --batch_size 1
ProteinMPNN/vanilla_proteinmpnn/examples/submit_example_7.sh ADDED
@@ -0,0 +1,40 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/bin/bash
2
+ #SBATCH -p gpu
3
+ #SBATCH --mem=32g
4
+ #SBATCH --gres=gpu:rtx2080:1
5
+ #SBATCH -c 3
6
+ #SBATCH --output=example_7.out
7
+
8
+ source activate mlfold
9
+
10
+ folder_with_pdbs="../PDB_complexes/pdbs/"
11
+
12
+ output_dir="../PDB_complexes/example_7_outputs"
13
+ if [ ! -d $output_dir ]
14
+ then
15
+ mkdir -p $output_dir
16
+ fi
17
+
18
+
19
+ path_for_parsed_chains=$output_dir"/parsed_pdbs.jsonl"
20
+ path_for_assigned_chains=$output_dir"/PDB_complexes/assigned_pdbs.jsonl"
21
+ path_for_bias=$output_dir"/bias_pdbs.jsonl"
22
+ AA_list="G P A"
23
+ bias_list="40.1 0.3 -0.05" #for G P A respectively; global AA bias in the logit space
24
+ chains_to_design="A B"
25
+
26
+
27
+ python ../helper_scripts/parse_multiple_chains.py --input_path=$folder_with_pdbs --output_path=$path_for_parsed_chains
28
+
29
+ python ../helper_scripts/assign_fixed_chains.py --input_path=$path_for_parsed_chains --output_path=$path_for_assigned_chains --chain_list "$chains_to_design"
30
+
31
+ python ../helper_scripts/make_bias_AA.py --output_path=$path_for_bias --AA_list="$AA_list" --bias_list="$bias_list"
32
+
33
+ python ../protein_mpnn_run.py \
34
+ --jsonl_path $path_for_parsed_chains \
35
+ --chain_id_jsonl $path_for_assigned_chains \
36
+ --out_folder $output_dir \
37
+ --bias_AA_jsonl $path_for_bias \
38
+ --num_seq_per_target 2 \
39
+ --sampling_temp "0.1" \
40
+ --batch_size 1
ProteinMPNN/vanilla_proteinmpnn/helper_scripts/assign_fixed_chains.py ADDED
@@ -0,0 +1,39 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import argparse
2
+
3
+ def main(args):
4
+ import json
5
+
6
+ with open(args.input_path, 'r') as json_file:
7
+ json_list = list(json_file)
8
+
9
+ global_designed_chain_list = []
10
+ if args.chain_list != '':
11
+ global_designed_chain_list = [str(item) for item in args.chain_list.split()]
12
+ my_dict = {}
13
+ for json_str in json_list:
14
+ result = json.loads(json_str)
15
+ all_chain_list = [item[-1:] for item in list(result) if item[:9]=='seq_chain'] #['A','B', 'C',...]
16
+ if len(global_designed_chain_list) > 0:
17
+ designed_chain_list = global_designed_chain_list
18
+ else:
19
+ #manually specify, e.g.
20
+ designed_chain_list = ["A"]
21
+ fixed_chain_list = [letter for letter in all_chain_list if letter not in designed_chain_list] #fix/do not redesign these chains
22
+ my_dict[result['name']]= (designed_chain_list, fixed_chain_list)
23
+
24
+ with open(args.output_path, 'w') as f:
25
+ f.write(json.dumps(my_dict) + '\n')
26
+
27
+
28
+ if __name__ == "__main__":
29
+ argparser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
30
+ argparser.add_argument("--input_path", type=str, help="Path to the parsed PDBs")
31
+ argparser.add_argument("--output_path", type=str, help="Path to the output dictionary")
32
+ argparser.add_argument("--chain_list", type=str, default='', help="List of the chains that need to be designed")
33
+
34
+ args = argparser.parse_args()
35
+ main(args)
36
+
37
+ # Output looks like this:
38
+ # {"5TTA": [["A"], ["B"]], "3LIS": [["A"], ["B"]]}
39
+
ProteinMPNN/vanilla_proteinmpnn/helper_scripts/make_bias_AA.py ADDED
@@ -0,0 +1,27 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import argparse
2
+
3
+ def main(args):
4
+
5
+ import numpy as np
6
+ import json
7
+
8
+ bias_list = [float(item) for item in args.bias_list.split()]
9
+ AA_list = [str(item) for item in args.AA_list.split()]
10
+
11
+ my_dict = dict(zip(AA_list, bias_list))
12
+
13
+ with open(args.output_path, 'w') as f:
14
+ f.write(json.dumps(my_dict) + '\n')
15
+
16
+
17
+ if __name__ == "__main__":
18
+ argparser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
19
+ argparser.add_argument("--output_path", type=str, help="Path to the output dictionary")
20
+ argparser.add_argument("--AA_list", type=str, default='', help="List of AAs to be biased")
21
+ argparser.add_argument("--bias_list", type=str, default='', help="AA bias strengths")
22
+
23
+ args = argparser.parse_args()
24
+ main(args)
25
+
26
+ #e.g. output
27
+ #{"A": -0.01, "G": 0.02}