-
Notifications
You must be signed in to change notification settings - Fork 0
/
app.py
189 lines (162 loc) · 7.85 KB
/
app.py
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
import gradio as gr
from transformers import AutoTokenizer, EsmForProteinFolding
from transformers.models.esm.openfold_utils.protein import to_pdb, Protein as OFProtein
from transformers.models.esm.openfold_utils.feats import atom14_to_atom37
import torch
from logging import getLogger
logger = getLogger(__name__)
def convert_outputs_to_pdb(outputs):
final_atom_positions = atom14_to_atom37(outputs["positions"][-1], outputs)
outputs = {k: v.to("cpu").numpy() for k, v in outputs.items()}
final_atom_positions = final_atom_positions.cpu().numpy()
final_atom_mask = outputs["atom37_atom_exists"]
pdbs = []
for i in range(outputs["aatype"].shape[0]):
aa = outputs["aatype"][i]
pred_pos = final_atom_positions[i]
mask = final_atom_mask[i]
resid = outputs["residue_index"][i] + 1
pred = OFProtein(
aatype=aa,
atom_positions=pred_pos,
atom_mask=mask,
residue_index=resid,
b_factors=outputs["plddt"][i],
chain_index=outputs["chain_index"][i] if "chain_index" in outputs else None,
)
pdbs.append(to_pdb(pred))
return pdbs[0]
def fold_prot_locally(sequence):
logger.info("Folding: " + sequence)
tokenized_input = tokenizer([sequence], return_tensors="pt", add_special_tokens=False)['input_ids'].cuda()
with torch.no_grad():
output = model(tokenized_input)
pdb = convert_outputs_to_pdb(output)
return pdb
def get_esm2_embeddings(sequence):
logger.info("Getting embeddings for: " + sequence)
tokenized_input = tokenizer([sequence], return_tensors="pt", add_special_tokens=False)['input_ids'].cuda()
with torch.no_grad():
aa = tokenized_input
L = aa.shape[1]
device = tokenized_input.device
attention_mask = torch.ones_like(aa, device=device)
# === ESM ===
esmaa = model.af2_idx_to_esm_idx(aa, attention_mask)
esm_s = model.compute_language_model_representations(esmaa)
return {"res": esm_s.cpu().tolist()}
def get_esmfold_embeddings(sequence):
logger.info("Getting embeddings for: " + sequence)
tokenized_input = tokenizer([sequence], return_tensors="pt", add_special_tokens=False)['input_ids'].cuda()
with torch.no_grad():
output = model(tokenized_input)
return {"res": output["s_s"].cpu().tolist()}
def suggest(option):
if option == "Plastic degradation protein":
suggestion = "MGSSHHHHHHSSGLVPRGSHMRGPNPTAASLEASAGPFTVRSFTVSRPSGYGAGTVYYPTNAGGTVGAIAIVPGYTARQSSIKWWGPRLASHGFVVITIDTNSTLDQPSSRSSQQMAALRQVASLNGTSSSPIYGKVDTARMGVMGWSMGGGGSLISAANNPSLKAAAPQAPWDSSTNFSSVTVPTLIFACENDSIAPVNSSALPIYDSMSRNAKQFLEINGGSHSCANSGNSNQALIGKKGVAWMKRFMDNDTRYSTFACENPNSTRVSDFRTANCSLEDPAANKARKEAELAAATAEQ"
elif option == "Antifreeze protein":
suggestion = "QCTGGADCTSCTGACTGCGNCPNAVTCTNSQHCVKANTCTGSTDCNTAQTCTNSKDCFEANTCTDSTNCYKATACTNSSGCPGH"
elif option == "AI Generated protein":
suggestion = "MSGMKKLYEYTVTTLDEFLEKLKEFILNTSKDKIYKLTITNPKLIKDIGKAIAKAAEIADVDPKEIEEMIKAVEENELTKLVITIEQTDDKYVIKVELENEDGLVHSFEIYFKNKEEMEKFLELLEKLISKLSGS"
elif option == "7-bladed propeller fold":
suggestion = "VKLAGNSSLCPINGWAVYSKDNSIRIGSKGDVFVIREPFISCSHLECRTFFLTQGALLNDKHSNGTVKDRSPHRTLMSCPVGEAPSPYNSRFESVAWSASACHDGTSWLTIGISGPDNGAVAVLKYNGIITDTIKSWRNNILRTQESECACVNGSCFTVMTDGPSNGQASYKIFKMEKGKVVKSVELDAPNYHYEECSCYPNAGEITCVCRDNWHGSNRPWVSFNQNLEYQIGYICSGVFGDNPRPNDGTGSCGPVSSNGAYGVKGFSFKYGNGVWIGRTKSTNSRSGFEMIWDPNGWTETDSSFSVKQDIVAITDWSGYSGSFVQHPELTGLDCIRPCFWVELIRGRPKESTIWTSGSSISFCGVNSDTVGWSWPDGAELPFTIDK"
else:
suggestion = ""
return suggestion
def molecule(mol):
x = (
"""<!DOCTYPE html>
<html>
<head>
<meta http-equiv="content-type" content="text/html; charset=UTF-8" />
<style>
body{
font-family:sans-serif
}
.mol-container {
width: 100%;
height: 600px;
position: relative;
}
.mol-container select{
background-image:None;
}
</style>
<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.6.3/jquery.min.js" integrity="sha512-STof4xm1wgkfm7heWqFJVn58Hm3EtS31XFaagaa8VMReCXAkQnJZ+jEy8PCC/iT18dFy95WcExNHFTqLyp72eQ==" crossorigin="anonymous" referrerpolicy="no-referrer"></script>
<script src="https://3Dmol.csb.pitt.edu/build/3Dmol-min.js"></script>
</head>
<body>
<div id="container" class="mol-container"></div>
<script>
let pdb = `"""
+ mol
+ """`
$(document).ready(function () {
let element = $("#container");
let config = { backgroundColor: "white" };
let viewer = $3Dmol.createViewer(element, config);
viewer.addModel(pdb, "pdb");
viewer.getModel(0).setStyle({}, { cartoon: { colorscheme:"whiteCarbon" } });
viewer.zoomTo();
viewer.render();
viewer.zoom(0.8, 2000);
})
</script>
</body></html>"""
)
return f"""<iframe style="width: 100%; height: 600px" 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>"""
sample_code = """
from gradio_client import Client
client = Client("https://wwydmanski-esmfold.hf.space/")
def fold_huggingface(sequence, fname=None):
result = client.predict(
sequence, # str in 'sequence' Textbox component
api_name="/pdb")
if fname is None:
with tempfile.NamedTemporaryFile("w", delete=False, suffix=".pdb", prefix="esmfold_") as fp:
fp.write(result)
fp.flush()
return fp.name
else:
with open(fname, "w") as fp:
fp.write(result)
fp.flush()
return fname
pdb_fname = fold_huggingface("MALWMRLLPLLALLALWGPDPAAAFVNQHLCGSHLVEALYLVCGERGFFYTPKTRREAEDLQVGQVELGGGPGAGSLQPLALEGSLQKRGIVEQCCTSICSLYQLENYCN")
"""
tokenizer = AutoTokenizer.from_pretrained("facebook/esmfold_v1")
model = EsmForProteinFolding.from_pretrained("facebook/esmfold_v1", low_cpu_mem_usage=True).cuda()
model.esm = model.esm.half()
torch.backends.cuda.matmul.allow_tf32 = True
with gr.Blocks() as demo:
gr.Markdown("# ESMFold")
with gr.Row():
with gr.Column():
inp = gr.Textbox(lines=1, label="Sequence")
name = gr.Dropdown(label="Choose a Sample Protein", value="Plastic degradation protein", choices=["Antifreeze protein", "Plastic degradation protein", "AI Generated protein", "7-bladed propeller fold", "custom"])
btn = gr.Button("🔬 Predict Structure ")
with gr.Row():
with gr.Column():
gr.Markdown("## Sample code")
gr.Code(sample_code, label="Sample usage", language="python", interactive=False)
with gr.Row():
gr.Markdown("## Output")
with gr.Row():
with gr.Column():
out = gr.Code(label="Output", interactive=False)
with gr.Column():
out_mol = gr.HTML(label="3D Structure")
with gr.Row(visible=False):
with gr.Column():
gr.Markdown("## Embeddings")
embs = gr.JSON(label="Embeddings")
name.change(fn=suggest, inputs=name, outputs=inp)
btn.click(fold_prot_locally, inputs=[inp], outputs=[out], api_name="pdb")
btn.click(get_esmfold_embeddings, inputs=[inp], outputs=[embs], api_name="embeddings")
btn.click(get_esm2_embeddings, inputs=[inp], outputs=[embs], api_name="esm2_embeddings")
out.change(fn=molecule, inputs=[out], outputs=[out_mol], api_name="3d_fold")
demo.launch()