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from api_prediction import AptaTransPipeline_Dist
import gradio as gr
import pandas as pd
import torch
import tempfile
from tabulate import tabulate
from PIL import Image
import itertools
import os
import RNA
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import random
from scipy.cluster.hierarchy import dendrogram, linkage
# Visualization
from Bio.Phylo.PhyloXML import Phylogeny
from Bio import SeqIO
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
from Bio import AlignIO
from Bio.Align.Applications import MafftCommandline
from Bio import Phylo
from Bio.Phylo.TreeConstruction import DistanceCalculator, DistanceTreeConstructor
import io
os.environ['GRADIO_SERVER_NAME'] = '0.0.0.0'
title='DNAptaESM2 Model Infernence'
desc='AptaBLE (cross-attention network), trained to predict the likelihood a DNA aptamer will form a complex with a target protein!\n\nPass in a FASTA-formatted file of all aptamers and input your protein target amino acid sequence. Your output scores are available for download via an Excel file.'
global pipeline
pipeline = AptaTransPipeline_Dist(
lr=1e-6,
weight_decay=None,
epochs=None,
model_type=None,
model_version=None,
model_save_path=None,
accelerate_save_path=None,
tensorboard_logdir=None,
d_model=128,
d_ff=512,
n_layers=6,
n_heads=8,
dropout=0.1,
load_best_pt=True, # already loads the pretrained model using the datasets included in repo -- no need to run the bottom two cells
device='cuda',
seed=1004)
def comparison(protein, aptamer_file, analysis):
print('analysis: ', analysis)
display = []
table_data = pd.DataFrame()
r_names, aptamers = read_fasta(aptamer_file)
proteins = [protein for i in range(len(aptamers))]
df = pd.DataFrame(columns=['Protein', 'Protein Seq', 'Aptamer', 'Aptamer Seq', 'Score'])
# print('Number of aptamers: ', len(aptamers))
scores = get_scores(aptamers, proteins)
df['Protein'] = ['protein_prov.']*len(aptamers)
df['Aptamer'] = r_names
df['Protein Seq'] = proteins
df['Aptamer Seq'] = aptamers
df['Score'] = scores
with tempfile.NamedTemporaryFile(delete=False, suffix=".xlsx") as temp_file:
with pd.ExcelWriter(temp_file.name, engine='openpyxl') as writer:
df.to_excel(writer, index=False)
temp_file_path = temp_file.name
print('Saving to excel!')
df.to_excel(f'{aptamer_file}.xlsx')
torch.cuda.empty_cache()
return '\n'.join(display), temp_file_path
def read_fasta(file_path):
headers = []
sequences = []
with open(file_path, 'r') as file:
content = file.readlines()
for i in range(0, len(content), 2):
header = content[i].strip()
if header.startswith('>'):
headers.append(header)
sequences.append(content[i+1].strip())
return headers, sequences
def get_scores(aptamers, proteins):
pipeline.model.to('cuda')
scores = pipeline.inference(aptamers, proteins, [0]*len(aptamers))
pipeline.model.to('cpu')
return scores
iface = gr.Interface(
fn=comparison,
inputs=[
gr.Textbox(lines=2, placeholder="Protein"),
gr.File(type="filepath"),
],
outputs=[
gr.Textbox(placeholder="Scores"),
gr.File(label="Download Excel")
],
description=desc
)
iface.launch()
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