File size: 9,978 Bytes
105a853
32941b1
9ef616c
a3a5fc1
 
 
 
 
 
b879795
a3a5fc1
 
 
 
9a3c969
 
 
a3a5fc1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9a3c969
 
 
 
 
 
 
 
 
 
 
 
 
0150901
9a3c969
 
 
 
 
 
 
 
096a065
 
 
 
9a3c969
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
a3a5fc1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9a3c969
 
 
 
cd9cc79
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
105a853
32941b1
 
7bafb03
32941b1
 
a3a5fc1
9a3c969
a3a5fc1
 
3ed3216
a3a5fc1
 
80372ef
 
 
 
32941b1
 
f724df8
32941b1
 
 
 
 
 
 
 
 
 
 
a3a5fc1
b879795
a3a5fc1
b879795
a3a5fc1
 
 
 
 
 
b879795
 
a3a5fc1
0107ce5
dea771b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ea795dd
dea771b
 
 
105a853
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255


import gradio as gr
import py3Dmol
from Bio.PDB import *

import numpy as np
from Bio.PDB import PDBParser
import pandas as pd
import torch
import os
from MDmodel import GNN_MD
import h5py
from transformMD import GNNTransformMD
import sys
import pytraj as pt
import pickle 

# JavaScript functions
resid_hover = """function(atom,viewer) {{
    if(!atom.label) {{
        atom.label = viewer.addLabel('{0}:'+atom.atom+atom.serial,
            {{position: atom, backgroundColor: 'mintcream', fontColor:'black'}});
    }}
}}"""
hover_func = """
function(atom,viewer) {
    if(!atom.label) {
        atom.label = viewer.addLabel(atom.interaction,
            {position: atom, backgroundColor: 'black', fontColor:'white'});
    }
}"""
unhover_func = """
function(atom,viewer) {
    if(atom.label) {
        viewer.removeLabel(atom.label);
        delete atom.label;
    }
}"""
atom_mapping = {0:'H', 1:'C', 2:'N', 3:'O', 4:'F', 5:'P', 6:'S', 7:'CL', 8:'BR', 9:'I', 10: 'UNK'}

model = GNN_MD(11, 64)
state_dict = torch.load(
    "best_weights_rep0.pt",
    map_location=torch.device("cpu"),
)["model_state_dict"]
model.load_state_dict(state_dict)
model = model.to('cpu')
model.eval()


def run_leap(fileName, path):
    leapText = """
    source leaprc.protein.ff14SB
    source leaprc.water.tip3p
    exp = loadpdb PATH4amb.pdb
    saveamberparm exp PATHexp.top PATHexp.crd
    quit
    """
    with open(path+"leap.in", "w") as outLeap:
        outLeap.write(leapText.replace('PATH', path))	
    os.system("tleap -f "+path+"leap.in >> "+path+"leap.out")

def convert_to_amber_format(pdbName):
    fileName, path = pdbName+'.pdb', ''
    os.system("pdb4amber -i "+fileName+" -p -y -o "+path+"4amb.pdb -l "+path+"pdb4amber_protein.log")
    run_leap(fileName, path)
    traj = pt.iterload(path+'exp.crd', top = path+'exp.top')
    pt.write_traj(path+fileName, traj, overwrite= True)
    print(path+fileName+' was created. Please always use this file for inspection because the coordinates might get translated during amber file generation and thus might vary from the input pdb file.')
    return pt.iterload(path+'exp.crd', top = path+'exp.top')

def get_maps(mapPath):
    residueMap = pickle.load(open(os.path.join(mapPath,'atoms_residue_map_generate.pickle'),'rb'))
    nameMap = pickle.load(open(os.path.join(mapPath,'atoms_name_map_generate.pickle'),'rb'))
    typeMap = pickle.load(open(os.path.join(mapPath,'atoms_type_map_generate.pickle'),'rb'))
    elementMap = pickle.load(open(os.path.join(mapPath,'map_atomType_element_numbers.pickle'),'rb'))
    return residueMap, nameMap, typeMap, elementMap

def get_residues_atomwise(residues):
    atomwise = []
    for name, nAtoms in residues:
        for i in range(nAtoms):
            atomwise.append(name)
    return atomwise

def get_begin_atom_index(traj):
    natoms = [m.n_atoms for m in traj.top.mols]
    molecule_begin_atom_index = [0] 
    x = 0
    for i in range(len(natoms)):
        x += natoms[i]
        molecule_begin_atom_index.append(x)
    print('molecule begin atom index', molecule_begin_atom_index, natoms)
    return molecule_begin_atom_index

def get_traj_info(traj, mapPath):
    coordinates  = traj.xyz
    residueMap, nameMap, typeMap, elementMap = get_maps(mapPath)
    types = [typeMap[a.type] for a in traj.top.atoms]
    elements = [elementMap[typ] for typ in types]
    atomic_numbers = [a.atomic_number for a in traj.top.atoms]
    molecule_begin_atom_index = get_begin_atom_index(traj)
    residues = [(residueMap[res.name], res.n_atoms) for res in traj.top.residues]
    residues_atomwise = get_residues_atomwise(residues)
    return coordinates[0], elements, types, atomic_numbers, residues_atomwise, molecule_begin_atom_index

def write_h5_info(outName, struct, atoms_type, atoms_number, atoms_residue, atoms_element, molecules_begin_atom_index, atoms_coordinates_ref):
    if os.path.isfile(outName):
        os.remove(outName)
    with h5py.File(outName, 'w') as oF:
        subgroup = oF.create_group(struct)     
        subgroup.create_dataset('atoms_residue', data= atoms_residue, compression = "gzip", dtype='i8')
        subgroup.create_dataset('molecules_begin_atom_index', data= molecules_begin_atom_index, compression = "gzip", dtype='i8')
        subgroup.create_dataset('atoms_type', data= atoms_type, compression = "gzip", dtype='i8')
        subgroup.create_dataset('atoms_number', data= atoms_number, compression = "gzip", dtype='i8')  
        subgroup.create_dataset('atoms_element', data= atoms_element, compression = "gzip", dtype='i8')
        subgroup.create_dataset('atoms_coordinates_ref', data= atoms_coordinates_ref, compression = "gzip", dtype='f8')

def preprocess(pdbid: str = None, ouputfile: str = "inference_for_md.hdf5", mask: str = "!@H=", mappath: str = "/maps/"):
    traj = convert_to_amber_format(pdbid)
    atoms_coordinates_ref, atoms_element, atoms_type, atoms_number, atoms_residue, molecules_begin_atom_index = get_traj_info(traj[mask], mappath)
    write_h5_info(ouputfile, pdbid, atoms_type, atoms_number, atoms_residue, atoms_element, molecules_begin_atom_index, atoms_coordinates_ref)

def get_pdb(pdb_code="", filepath=""):
    try:
        return filepath.name
    except AttributeError as e:
        if pdb_code is None or pdb_code == "":
            return None
        else:
            os.system(f"wget -qnc https://files.rcsb.org/view/{pdb_code}.pdb")
            return f"{pdb_code}.pdb"


def get_offset(pdb):
    pdb_multiline = pdb.split("\n")
    for line in pdb_multiline:
        if line.startswith("ATOM"):
            return int(line[22:27])


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(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()

    view = py3Dmol.view(width=600, height=400)
    view.setBackgroundColor('white')
    view.addModel(pdb, "pdb")
    view.setStyle({'stick': {'colorscheme': {'prop': 'resi', 'C': 'turquoise'}}})
    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
    
    for i in range(10):
        adaptability_value = adaptability[topN_ind[i]]
        color = 'orange'
        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_value / 1.5,
            'color': color,
            'alpha': 0.75
        })
        
    view.zoomTo()

    output = view._make_html().replace("'", '"')

    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'])


callback = gr.CSVLogger()

def run():
    with gr.Blocks() as demo:
        gr.Markdown("# Protein Adaptability Prediction")
        
        #text_input = gr.Textbox()
        #text_output = gr.Textbox()
        #text_button = gr.Button("Flip")
        inp = gr.Textbox(placeholder="PDB Code or upload file below", label="Input structure")
        pdb_file = gr.File(label="PDB File Upload")
        #with gr.Row():
        #    helix = gr.ColorPicker(label="helix")
        #    sheet = gr.ColorPicker(label="sheet")
        #    loop = gr.ColorPicker(label="loop")
        single_btn = gr.Button(label="Run")
        with gr.Row():
            html = gr.HTML()
        with gr.Row():
            dataframe = gr.Dataframe()
                
        single_btn.click(fn=predict, inputs=[inp, pdb_file], outputs=[html, dataframe])


    demo.launch(server_name="0.0.0.0", server_port=7860)


if __name__ == "__main__":
    run()