simonduerr commited on
Commit
99db958
1 Parent(s): 10f2049

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +15 -15
app.py CHANGED
@@ -837,7 +837,7 @@ def update_AF(seq_dict, pdb, num_recycles, selectedResidues):
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  plt.figure(),
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  plt.figure(),
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  )
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- random_dir = tempfile.NamedTemporaryDir(delete=False)
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  plddts, paes, num_res = ray.get(run_alphafold.remote(allSeqs, num_recycles, random_dir ))
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@@ -1257,23 +1257,23 @@ proteinMPNN = gr.Blocks()
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  with proteinMPNN:
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- gr.Markdown("# MAINTENANC, CURRENTLY NOT WORKING")
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- gr.HTML("<span style='font-size:3em;color:red'>⚠</span>")
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- # gr.Markdown("# ProteinMPNN")
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- # gr.Markdown(
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- # """This model takes as input a protein structure and based on its backbone predicts new sequences that will fold into that backbone.
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- # Optionally, we can run AlphaFold2 on the predicted sequence to check whether the predicted sequences adopt the same backbone.
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-
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- # If you use this space please cite the ProteinMPNN paper
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- # > J. Dauparas, I. Anishchenko, N. Bennett, H. Bai, R. J. Ragotte, L. F. Milles, B. I. M. Wicky, A. Courbet, R. J. de Haas, N. Bethel, P. J. Y. Leung, T. F. Huddy, S. Pellock, D. Tischer, F. Chan, B. Koepnick, H. Nguyen, A. Kang, B. Sankaran, A. K. Bera, N. P. King, D. Baker, Robust deep learning–based protein sequence design using ProteinMPNN. Science 378, 49–56 (2022).
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- # and this webapp:
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- # > Simon L. Dürr. (2023). ProteinMPNN Gradio Webapp (v0.3). Zenodo. https://doi.org/10.5281/zenodo.7630417
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- # """
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- # )
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- # gr.Markdown("![](https://simonduerr.eu/ProteinMPNN.png)")
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  with gr.Tabs():
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  with gr.TabItem("Input"):
 
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  plt.figure(),
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  plt.figure(),
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  )
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+ random_dir = tempfile.TemporaryDirectory()
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  plddts, paes, num_res = ray.get(run_alphafold.remote(allSeqs, num_recycles, random_dir ))
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  with proteinMPNN:
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+ # gr.Markdown("# MAINTENANC, CURRENTLY NOT WORKING")
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+ # gr.HTML("<span style='font-size:3em;color:red'>⚠</span>")
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+ gr.Markdown("# ProteinMPNN")
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+ gr.Markdown(
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+ """This model takes as input a protein structure and based on its backbone predicts new sequences that will fold into that backbone.
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+ Optionally, we can run AlphaFold2 on the predicted sequence to check whether the predicted sequences adopt the same backbone.
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+
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+ If you use this space please cite the ProteinMPNN paper
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+ > J. Dauparas, I. Anishchenko, N. Bennett, H. Bai, R. J. Ragotte, L. F. Milles, B. I. M. Wicky, A. Courbet, R. J. de Haas, N. Bethel, P. J. Y. Leung, T. F. Huddy, S. Pellock, D. Tischer, F. Chan, B. Koepnick, H. Nguyen, A. Kang, B. Sankaran, A. K. Bera, N. P. King, D. Baker, Robust deep learning–based protein sequence design using ProteinMPNN. Science 378, 49–56 (2022).
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+ and this webapp:
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+ > Simon L. Dürr. (2023). ProteinMPNN Gradio Webapp (v0.3). Zenodo. https://doi.org/10.5281/zenodo.7630417
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+ """
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+ )
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+ gr.Markdown("![](https://simonduerr.eu/ProteinMPNN.png)")
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  with gr.Tabs():
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  with gr.TabItem("Input"):