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Adaptability_protein_dynamics

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RajarshiMondal commited on Jun 3, 2023

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0107ce5

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1 Parent(s): cd9cc79

Update main.py

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main.py +10 -87

main.py CHANGED Viewed

@@ -143,42 +143,6 @@ def get_pdbid_from_filename(filename: str):
     # Assuming the filename would be of the standard form 11GS.pdb
     return filename.split(".")[0]
-def predict(pdb_code, pdb_file):
-    #path_to_pdb = get_pdb(pdb_code=pdb_code, filepath=pdb_file)
-    #pdb = open(path_to_pdb, "r").read()
-    # switch to misato env if not running from container
-    pdbid = get_pdbid_from_filename(pdb_file.name)
-    mdh5_file = "inference_for_md.hdf5"
-    mappath = "/maps"
-    mask = "!@H="
-    preprocess(pdbid=pdbid, ouputfile=mdh5_file, mask=mask, mappath=mappath)
-    md_H5File = h5py.File(mdh5_file)
-    column_names = ["x", "y", "z", "element"]
-    atoms_protein = pd.DataFrame(columns = column_names)
-    cutoff = md_H5File[pdbid]["molecules_begin_atom_index"][:][-1] # cutoff defines protein atoms
-    atoms_protein["x"] = md_H5File[pdbid]["atoms_coordinates_ref"][:][:cutoff, 0]
-    atoms_protein["y"] = md_H5File[pdbid]["atoms_coordinates_ref"][:][:cutoff, 1]
-    atoms_protein["z"] = md_H5File[pdbid]["atoms_coordinates_ref"][:][:cutoff, 2]
-    atoms_protein["element"] = md_H5File[pdbid]["atoms_element"][:][:cutoff]
-    item = {}
-    item["scores"] = 0
-    item["id"] = pdbid
-    item["atoms_protein"] = atoms_protein
-    transform = GNNTransformMD()
-    data_item = transform(item)
-    adaptability = model(data_item)
-    adaptability = adaptability.detach().numpy()
-    data = []
     for i in range(10):
         data.append([i, atom_mapping[atoms_protein.iloc[i, atoms_protein.columns.get_loc("element")] - 1], atoms_protein.iloc[topN_ind[i], atoms_protein.columns.get_loc("x")],
@@ -189,54 +153,7 @@ def predict(pdb_code, pdb_file):
     pdb = open(pdb_file.name, "r").read()
-    view = nv.NGLWidget()
-    view._remote_call("setSize", target="Widget", args=["800px", "600px"])
-    view.add_pdbstr(pdb, defaultRepresentation=True)
-    """
-    view = py3Dmol.view(width=600, height=400)
-    view.setBackgroundColor('white')
-    view.addModel(pdb, "pdb")
-    view.setStyle({'stick': {'colorscheme': {'prop': 'resi', 'C': 'turquoise'}}})
-   """
-    for i in range(10):
-        view.addSphere({'center':{'x':atoms_protein.iloc[topN_ind[i], atoms_protein.columns.get_loc("x")], 'y':atoms_protein.iloc[topN_ind[i], atoms_protein.columns.get_loc("y")],'z':atoms_protein.iloc[topN_ind[i], atoms_protein.columns.get_loc("z")]},'radius':adaptability[topN_ind[i]]/1.5,'color':'orange','alpha':0.75})
-         # Add lighting and shading options to the view object:
-        view.setStyle({'stick': {'colorscheme': {'prop': 'resi', 'C': 'turquoise'}}})
-        view.setStyle({'sphere': {}})
-        view.addLight([0, 0, 10], [1, 1, 1], 1)  # Add directional light from the z-axis
-        view.setSpecular(0.5)  # Adjust the specular lighting effect
-        view.setAmbient(0.5)  # Adjust the ambient lighting effect
-    view.zoomTo()
-    view.rotate(180, "y")  # Rotate the structure by 180 degrees along the y-axis
-    # Add animation options to the view object:
-    view.animate({'loop': 'forward', 'reps': 2})  # Animate the visualization to loop forward 2 times
-    """
-    output = view._make_html().replace("'", '"')
-   """
-    output = view.render_notebook()
-"""
-    x = f"""<!DOCTYPE html><html> {output} </html>"""  # do not use ' in this input
-    return f"""<iframe  style="width: 100%; height:420px" name="result" allow="midi; geolocation; microphone; camera;
-    display-capture; encrypted-media;" sandbox="allow-modals allow-forms
-    allow-scripts allow-same-origin allow-popups
-    allow-top-navigation-by-user-activation allow-downloads" allowfullscreen=""
-    allowpaymentrequest="" frameborder="0" srcdoc='{x}'></iframe>""", pd.DataFrame(data, columns=['index','element','x','y','z','Adaptability'])
-"""
-return output, pd.DataFrame(data, columns=['index','element','x','y','z','Adaptability'])
-callback = gr.CSVLogger()
-"""
 def predict(pdb_code, pdb_file):
     #path_to_pdb = get_pdb(pdb_code=pdb_code, filepath=pdb_file)
@@ -273,12 +190,18 @@ def predict(pdb_code, pdb_file):
     data = []
     for i in range(adaptability.shape[0]):
         data.append([i, atom_mapping[atoms_protein.iloc[i, atoms_protein.columns.get_loc("element")] - 1], atoms_protein.iloc[i, atoms_protein.columns.get_loc("x")],atoms_protein.iloc[i, atoms_protein.columns.get_loc("y")],atoms_protein.iloc[i, atoms_protein.columns.get_loc("z")],adaptability[i]])
-    topN = 100
-    topN_ind = np.argsort(adaptability)[::-1][:topN]
     pdb = open(pdb_file.name, "r").read()
@@ -303,7 +226,7 @@ def predict(pdb_code, pdb_file):
 callback = gr.CSVLogger()
-"""
 def run():
     with gr.Blocks() as demo:

     # Assuming the filename would be of the standard form 11GS.pdb
     return filename.split(".")[0]
     for i in range(10):
         data.append([i, atom_mapping[atoms_protein.iloc[i, atoms_protein.columns.get_loc("element")] - 1], atoms_protein.iloc[topN_ind[i], atoms_protein.columns.get_loc("x")],
     pdb = open(pdb_file.name, "r").read()
 def predict(pdb_code, pdb_file):
     #path_to_pdb = get_pdb(pdb_code=pdb_code, filepath=pdb_file)
     data = []
+    for i in range(10):
+        data.append([i, atom_mapping[atoms_protein.iloc[i, atoms_protein.columns.get_loc("element")] - 1], atoms_protein.iloc[topN_ind[i], atoms_protein.columns.get_loc("x")],
+        atoms_protein.iloc[topN_ind[i], atoms_protein.columns.get_loc("y")],
+        atoms_protein.iloc[topN_ind[i], atoms_protein.columns.get_loc("z")],
+        adaptability[topN_ind[i]]
+    ])
     for i in range(adaptability.shape[0]):
         data.append([i, atom_mapping[atoms_protein.iloc[i, atoms_protein.columns.get_loc("element")] - 1], atoms_protein.iloc[i, atoms_protein.columns.get_loc("x")],atoms_protein.iloc[i, atoms_protein.columns.get_loc("y")],atoms_protein.iloc[i, atoms_protein.columns.get_loc("z")],adaptability[i]])
+    #topN = 100
+    #topN_ind = np.argsort(adaptability)[::-1][:topN]
     pdb = open(pdb_file.name, "r").read()
 callback = gr.CSVLogger()
 def run():
     with gr.Blocks() as demo: