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#@title Lauch the Interface
#@markdown Run to launch the interface.
#@markdown 1. Enter the protein sequence.
#@markdown 2. Specify the start and end index (inclusive) of the domain for which you would like to predict mutations (note that indexing starts at 1).
#@markdown 3. Select the number of tokens you would like the model to predict for each position.
#@markdown 4. Click 'Submit'.
#@markdown 5. Click 'Download Outputs' to download the zip file.
def process_sequence(sequence, domain_bounds, n):
start_index = int(domain_bounds['start'][0]) - 1
end_index = int(domain_bounds['end'][0])
top_n_mutations = {}
all_logits = []
for i in range(len(sequence)):
if start_index <= i <= (end_index - 1):
masked_seq = sequence[:i] + '<mask>' + sequence[i+1:]
inputs = tokenizer(masked_seq, return_tensors="pt", padding=True, truncation=True, max_length=2000)
inputs = {k: v.to(device) for k, v in inputs.items()}
with torch.no_grad():
logits = model(**inputs).logits
mask_token_index = torch.where(inputs["input_ids"] == tokenizer.mask_token_id)[1]
mask_token_logits = logits[0, mask_token_index, :]
# Define amino acid tokens
AAs_tokens = ['L', 'A', 'G', 'V', 'S', 'E', 'R', 'T', 'I', 'D', 'P', 'K', 'Q', 'N', 'F', 'Y', 'M', 'H', 'W', 'C']
all_tokens_logits = mask_token_logits.squeeze(0)
top_tokens_indices = torch.argsort(all_tokens_logits, dim=0, descending=True)
top_tokens_logits = all_tokens_logits[top_tokens_indices]
mutation = []
# make sure we don't include non-AA tokens
for token_index in top_tokens_indices:
decoded_token = tokenizer.decode([token_index.item()])
if decoded_token in AAs_tokens:
mutation.append(decoded_token)
if len(mutation) == n:
break
top_n_mutations[(sequence[i], i)] = mutation
# collecting logits for the heatmap
logits_array = mask_token_logits.cpu().numpy()
# filter out non-amino acid tokens
filtered_indices = list(range(4, 23 + 1))
filtered_logits = logits_array[:, filtered_indices]
all_logits.append(filtered_logits)
token_indices = torch.arange(logits.size(-1))
tokens = [tokenizer.decode([idx]) for idx in token_indices]
filtered_tokens = [tokens[i] for i in filtered_indices]
all_logits_array = np.vstack(all_logits)
normalized_logits_array = F.softmax(torch.tensor(all_logits_array), dim=-1).numpy()
transposed_logits_array = normalized_logits_array.T
# Plotting the heatmap
x_tick_positions = np.arange(start_index, end_index, 10)
x_tick_labels = [str(pos + 1) for pos in x_tick_positions]
plt.figure(figsize=(15, 8))
plt.rcParams.update({'font.size': 18})
sns.heatmap(transposed_logits_array, cmap='plasma', xticklabels=x_tick_labels, yticklabels=filtered_tokens)
plt.title('Token Probability Heatmap')
plt.ylabel('Token')
plt.xlabel('Residue Index')
plt.yticks(rotation=0)
plt.xticks(x_tick_positions - start_index + 0.5, x_tick_labels, rotation=0)
# Save the figure to a BytesIO object
buf = BytesIO()
plt.savefig(buf, format='png', dpi = 300)
buf.seek(0)
plt.close()
# Convert BytesIO object to an image
img = Image.open(buf)
original_residues = []
mutations = []
positions = []
for key, value in top_n_mutations.items():
original_residue, position = key
original_residues.append(original_residue)
mutations.append(value)
positions.append(position + 1)
df = pd.DataFrame({
'Original Residue': original_residues,
'Predicted Residues': mutations,
'Position': positions
})
df.to_csv("predicted_tokens.csv", index=False)
img.save("heatmap.png", dpi = 300)
zip_path = "outputs.zip"
with zipfile.ZipFile(zip_path, 'w') as zipf:
zipf.write("predicted_tokens.csv")
zipf.write("heatmap.png")
return df, img, zip_path
demo = gr.Interface(
fn=process_sequence,
inputs=[
gr.Textbox(label="Sequence", placeholder="Enter the protein sequence here"),
gr.Dataframe(
headers=["start", "end"],
datatype=["number", "number"],
row_count=(1, "fixed"),
col_count=(2, "fixed"),
label="Domain Bounds"
),
gr.Dropdown([i for i in range(1, 21)], label="Top N Tokens"),
],
outputs=[
gr.Dataframe(label="Predicted Tokens (in order of decreasing likelihood)"),
gr.Image(type="pil", label="Heatmap"),
gr.File(label="Download Outputs"),
],
)
if __name__ == "__main__":
with suppress_output():
demo.launch(show_error=False, debug=False) |