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99db958
1
Parent(s):
10f2049
Update app.py
Browse files
app.py
CHANGED
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@@ -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.
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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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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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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("")
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with gr.Tabs():
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with gr.TabItem("Input"):
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