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from huggingface_hub import HfApi, ModelFilter
from transformers import AutoTokenizer, AutoModelForMaskedLM
import pandas as pd
import re
from tqdm import tqdm
import torch
import gradio as gr
import warnings
warnings.filterwarnings('ignore')
MODEL, MODEL_NAME, BATCH_CONVERTER, ALPHABET = None, None, None, None
OFFSET = 1
MODELS = [m.modelId for m in HfApi().list_models(filter=ModelFilter(author="facebook", model_name="esm", task="fill-mask"), sort="lastModified", direction=-1)]
SCORING = ["masked-marginals (more accurate)", "wt-marginals (faster)"]
def label_row(row, sequence, token_probs):
wt, idx, mt = row[0], int(row[1:-1]) - OFFSET, row[-1]
assert sequence[idx] == wt, "The listed wildtype does not match the provided sequence"
wt_encoded, mt_encoded = ALPHABET[wt], ALPHABET[mt]
score = token_probs[0, 1 + idx, mt_encoded] - token_probs[0, 1 + idx, wt_encoded]
return score.item()
def initialise_model(model_name):
global MODEL, MODEL_NAME, BATCH_CONVERTER, ALPHABET
MODEL_NAME = model_name
MODEL = AutoModelForMaskedLM.from_pretrained(model_name)
BATCH_CONVERTER = AutoTokenizer.from_pretrained(model_name)
ALPHABET = BATCH_CONVERTER.get_vocab()
if torch.cuda.is_available():
MODEL = MODEL.cuda()
def parse_input(seq, sub):
assert seq.isalpha(), "Sequence must be alphabetic"
substitutions, mode = list(), None
if len(sub.split()) == 1 and len(sub.split()[0]) == len(seq):
mode = 'seq vs seq'
for resi,(src,trg) in enumerate(zip(seq,sub), OFFSET):
if src != trg:
substitutions.append(f"{src}{resi}{trg}")
elif len(targets := sub.split()) > 1:
if all(re.match(r'\d+', x) for x in targets):
mode = 'deep mutational scan'
for resi in map(int, sub.split()):
src = seq[resi-OFFSET]
for trg in "ACDEFGHIKLMNPQRSTVWY".replace(src,''):
substitutions.append(f"{src}{resi}{trg}")
elif all(re.match(r'[A-Z]\d+[A-Z]', x) for x in targets):
mode = 'aa substitutions'
substitutions = targets
if not mode:
raise RuntimeError("Unrecognised running mode")
return mode, pd.DataFrame(substitutions, columns=['0'])
def run_model(sequence, substitutions, batch_tokens, scoring_strategy):
if scoring_strategy.startswith("wt-marginals"):
with torch.no_grad():
token_probs = torch.log_softmax(MODEL(batch_tokens)["logits"], dim=-1)
substitutions[MODEL_NAME] = substitutions.apply(
lambda row: label_row(
row['0'],
sequence,
token_probs,
),
axis=1,
)
elif scoring_strategy.startswith("masked-marginals"):
all_token_probs = []
for i in tqdm(range(batch_tokens.size()[1])):
batch_tokens_masked = batch_tokens.clone()
batch_tokens_masked[0, i] = ALPHABET['<mask>']
with torch.no_grad():
token_probs = torch.log_softmax(
MODEL(batch_tokens_masked)["logits"], dim=-1
)
all_token_probs.append(token_probs[:, i])
token_probs = torch.cat(all_token_probs, dim=0).unsqueeze(0)
substitutions[MODEL_NAME] = substitutions.apply(
lambda row: label_row(
row['0'],
sequence,
token_probs,
),
axis=1,
)
return substitutions
def parse_output(output, mode):
if mode == 'aa substitutions':
output = output.sort_values(MODEL_NAME, ascending=False)
elif mode == 'deep mutational scan':
output = pd.concat([(output.assign(resi=output['0'].str.extract(r'(\d+)', expand=False).astype(int))
.sort_values(['resi', MODEL_NAME], ascending=[True,False])
.groupby(['resi'])
.head(19)
.drop(['resi'], axis=1)).iloc[19*x:19*(x+1)].reset_index(drop=True) for x in range(output.shape[0]//19)]
, axis=1).set_axis(range(output.shape[0]//19*2), axis='columns')
return output.style.format(lambda x: f'{x:.2f}' if isinstance(x, float) else x).hide_index().hide_columns().background_gradient(cmap="RdYlGn", vmax=8, vmin=-8).to_html()
# mode = 'deep mutational scan' #@param ['seq vs seq', 'deep mutational scan', 'aa substitutions']
# sequence = "MVEQYLLEAIVRDARDGITISDCSRPDNPLVFVNDAFTRMTGYDAEEVIGKNCRFLQRGDINLSAVHTIKIAMLTHEPCLVTLKNYRKDGTIFWNELSLTPIINKNGLITHYLGIQKDVSAQVILNQTLHEENHLLKSNKEMLEYLVNIDALTGLHNRRFLEDQLVIQWKLASRHINTITIFMIDIDYFKAFNDTYGHTAGDEALRTIAKTLNNCFMRGSDFVARYGGEEFTILAIGMTELQAHEYSTKLVQKIENLNIHHKGSPLGHLTISLGYSQANPQYHNDQNLVIEQADRALYSAKVEGKNRAVAYREQ" #@param {type:"string"}
# target = "61 214 19 30 122 140" #@param {type:"string"}
# substitutions = list()
# scoring_strategy = "masked-marginals"
# if mode == 'seq vs seq':
# for resi,(seq,trg) in enumerate(zip(sequence,target), OFFSET):
# if seq != trg:
# substitutions.append(f"{seq}{resi}{trg}")
# elif mode == 'deep mutational scan':
# for resi in map(int, target.split()):
# seq = sequence[resi-OFFSET]
# for trg in "ACDEFGHIKLMNPQRSTVWY".replace(seq,''):
# substitutions.append(f"{seq}{resi}{trg}")
# elif mode == 'aa substitutions':
# substitutions = target.split()
# else:
# raise RuntimeError("Unrecognised running mode")
# df = pd.DataFrame(substitutions, columns=['0'])
# mutation_col = df.columns[0]
# batch_tokens = batch_converter(sequence, return_tensors='pt')['input_ids']
# if scoring_strategy == "wt-marginals":
# with torch.no_grad():
# token_probs = torch.log_softmax(model(batch_tokens)["logits"], dim=-1)
# df[model_name] = df.apply(
# lambda row: label_row(
# row[mutation_col],
# sequence,
# token_probs,
# alphabet,
# OFFSET,
# ),
# axis=1,
# )
# elif scoring_strategy == "masked-marginals":
# all_token_probs = []
# for i in tqdm(range(batch_tokens.size()[1])):
# batch_tokens_masked = batch_tokens.clone()
# batch_tokens_masked[0, i] = alphabet['<mask>']
# with torch.no_grad():
# token_probs = torch.log_softmax(
# model(batch_tokens_masked)["logits"], dim=-1
# )
# all_token_probs.append(token_probs[:, i]) # vocab size
# token_probs = torch.cat(all_token_probs, dim=0).unsqueeze(0)
# df[model_name] = df.apply(
# lambda row: label_row(
# row[mutation_col],
# sequence,
# token_probs,
# alphabet,
# OFFSET,
# ),
# axis=1,
# )
# if mode == 'aa substitutions':
# df = df.sort_values(model_name, ascending=False)
# elif mode == 'deep mutational scan':
# df = pd.concat([(df.assign(resi=df['0'].str.extract(f'(\d+)', expand=False).astype(int))
# .sort_values(['resi', model_name], ascending=[True,False])
# .groupby(['resi'])
# .head(19)
# .drop(['resi'], axis=1)).iloc[19*x:19*(x+1)].reset_index(drop=True) for x in range(df.shape[0]//19)]
# , axis=1).set_axis(range(df.shape[0]//19*2), axis='columns')
# df.style.hide_index().hide_columns().background_gradient(cmap="RdYlGn", vmax=8, vmin=-8)
def app(*argv):
seq, trg, model_name, scoring_strategy, *_ = argv
mode, substitutions = parse_input(seq, trg)
if model_name != MODEL_NAME:
initialise_model(model_name)
batch_tokens = BATCH_CONVERTER(seq, return_tensors='pt')['input_ids']
df = run_model(seq, substitutions, batch_tokens, scoring_strategy)
return parse_output(df, mode)
# demo = gr.Interface(
# theme=gr.themes.Base(),
# title="Protein Sequence Mutagenesis",
# description="Predict the effect of mutations on protein stability",
# fn=app,
# inputs=[gr.Textbox(lines=2, label="Sequence", placeholder="Sequence here...", required=True, value='MVEQYLLEAIVRDARDGITISDCSRPDNPLVFVNDAFTRMTGYDAEEVIGKNCRFLQRGDINLSAVHTIKIAMLTHEPCLVTLKNYRKDGTIFWNELSLTPIINKNGLITHYLGIQKDVSAQVILNQTLHEENHLLKSNKEMLEYLVNIDALTGLHNRRFLEDQLVIQWKLASRHINTITIFMIDIDYFKAFNDTYGHTAGDEALRTIAKTLNNCFMRGSDFVARYGGEEFTILAIGMTELQAHEYSTKLVQKIENLNIHHKGSPLGHLTISLGYSQANPQYHNDQNLVIEQADRALYSAKVEGKNRAVAYREQ'),
# gr.Textbox(lines=2, label="Substitutions", placeholder="Substitutions here...", required=True, value="61 214 19 30 122 140"),
# gr.Dropdown(MODELS, label="Model", value=MODELS[1]),
# gr.Dropdown(["masked-marginals (more accurate)", "wt-marginals (faster)"], label="Scoring strategy", value="wt-marginals (faster)"),
# ],
# outputs=gr.HTML(formatter="html", label="Output"),
# )
with gr.Blocks() as demo:
gr.Markdown("""Protein Sequence Mutagenesis""", name="title")
gr.Markdown("""Predict the effect of mutations on protein stability""", name="description")
seq = gr.Textbox(lines=2, label="Sequence", placeholder="Sequence here...", required=True, value='MVEQYLLEAIVRDARDGITISDCSRPDNPLVFVNDAFTRMTGYDAEEVIGKNCRFLQRGDINLSAVHTIKIAMLTHEPCLVTLKNYRKDGTIFWNELSLTPIINKNGLITHYLGIQKDVSAQVILNQTLHEENHLLKSNKEMLEYLVNIDALTGLHNRRFLEDQLVIQWKLASRHINTITIFMIDIDYFKAFNDTYGHTAGDEALRTIAKTLNNCFMRGSDFVARYGGEEFTILAIGMTELQAHEYSTKLVQKIENLNIHHKGSPLGHLTISLGYSQANPQYHNDQNLVIEQADRALYSAKVEGKNRAVAYREQ')
trg = gr.Textbox(lines=1, label="Substitutions", placeholder="Substitutions here...", required=True, value="61 214 19 30 122 140")
model_name = gr.Dropdown(MODELS, label="Model", value=MODELS[1])
scoring_strategy = gr.Dropdown(SCORING, label="Scoring strategy", value=SCORING[1])
btn = gr.Button(label="Submit", type="submit")
btn.click(fn=app, inputs=[seq, trg, model_name, scoring_strategy], outputs=[gr.HTML()])
if __name__ == '__main__':
demo.launch()
# demo.launch(share=True, server_name="0.0.0.0", server_port=7878) |