app.py

import streamlit as st
from stmol import showmol
import py3Dmol
import requests
import biotite.structure.io as bsio
import random
import hashlib
import urllib3
from Bio.Blast import NCBIWWW, NCBIXML
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
import time

urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)

st.set_page_config(layout='wide')
st.sidebar.title('🔮 GenPro2 Protein Generator & Structure Predictor')
st.sidebar.write('GenPro2 is an end-to-end single sequence protein generator and structure predictor based [*ESMFold*](https://esmatlas.com/about) and the ESM-2 language model.')

# Function to generate protein sequence from random words
def generate_sequence_from_words(words, length):
    seed = ' '.join(words).encode('utf-8')
    random.seed(hashlib.md5(seed).hexdigest())
    amino_acids = "ACDEFGHIKLMNPQRSTVWY"
    return ''.join(random.choice(amino_acids) for _ in range(length))

# stmol
def render_mol(pdb):
    pdbview = py3Dmol.view()
    pdbview.addModel(pdb,'pdb')
    pdbview.setStyle({'cartoon':{'color':'spectrum'}})
    pdbview.setBackgroundColor('white')
    pdbview.zoomTo()
    pdbview.zoom(2, 800)
    pdbview.spin(True)
    showmol(pdbview, height = 500,width=800)

# BLAST analysis function
def perform_blast_analysis(sequence):
    st.subheader('Protein Analysis')
    with st.spinner("Analyzing generated protein... This may take a few minutes."):
        progress_bar = st.progress(0)
        for i in range(100):
            progress_bar.progress(i + 1)
            time.sleep(0.1)  # Simulate analysis time
        
        try:
            record = SeqRecord(Seq(sequence), id='random_protein')
            result_handle = NCBIWWW.qblast("blastp", "swissprot", record.seq)
            
            blast_record = NCBIXML.read(result_handle)
            
            if blast_record.alignments:
                alignment = blast_record.alignments[0]  # Get the top hit
                
                # Extract organism
                organism = alignment.title.split('OS=')[-1].split('OX=')[0].strip()
                
                # Simplify organism name if it's too complex
                organism = organism.split()[0] if len(organism.split()) > 1 else organism
                
                st.write(f"**Estimated Organism:** This protein sequence shares similarities with proteins found in {organism}.")
                
                # Fetch protein function (if available)
                if hasattr(alignment, 'description') and alignment.description:
                    function = alignment.description.split('[')[0].strip()  # Remove organism info in brackets
                    st.write(f"**Potential Function:** This protein might be involved in {function.lower()}.")
                else:
                    st.write("**Potential Function:** Unable to determine a specific function for this protein sequence.")
                
                st.markdown("[Learn more about protein functions](https://www.nature.com/scitable/topicpage/protein-function-14123348/)")
            else:
                st.write("No close matches found. This might be a unique protein sequence!")
        except Exception as e:
            st.error("An error occurred during protein analysis. Please try again later.")

# ESMfold
def update(sequence, word1, word2, word3, sequence_length):
    headers = {
        'Content-Type': 'application/x-www-form-urlencoded',
    }
    try:
        response = requests.post('https://api.esmatlas.com/foldSequence/v1/pdb/', 
                                 headers=headers, 
                                 data=sequence, 
                                 verify=False,  # Disable SSL verification
                                 timeout=300)  # Set a longer timeout
        response.raise_for_status()  # Raise an exception for bad status codes
        pdb_string = response.content.decode('utf-8')
        
        with open('predicted.pdb', 'w') as f:
            f.write(pdb_string)
        
        struct = bsio.load_structure('predicted.pdb', extra_fields=["b_factor"])
        b_value = round(struct.b_factor.mean(), 2)
        
        # Display protein structure
        st.subheader(f'Predicted protein structure using seed: {word1}, {word2}, and {word3} + length {sequence_length}')
        render_mol(pdb_string)
        
        # plDDT value is stored in the B-factor field
        st.subheader('plDDT Score')
        st.write('plDDT is a per-residue estimate of the confidence in prediction on a scale from 0-100%.')
        st.info(f'Average plDDT: {int(b_value * 100)}%')
        
        st.download_button(
            label="Download PDB",
            data=pdb_string,
            file_name='predicted.pdb',
            mime='text/plain',
        )

        # Perform BLAST analysis
        perform_blast_analysis(sequence)
    except requests.exceptions.RequestException as e:
        st.error(f"An error occurred while calling the API: {str(e)}")
        st.write("Please try again later or contact support if the issue persists.")

# Streamlit app
st.title("Word-Seeded Protein Sequence Generator and Structure Predictor")

# Input for word-seeded sequence generation
st.sidebar.subheader("Generate Sequence from Words")
word1 = st.sidebar.text_input("Word 1")
word2 = st.sidebar.text_input("Word 2")
word3 = st.sidebar.text_input("Word 3")
sequence_length = st.sidebar.number_input("Sequence Length", min_value=50, max_value=400, value=100, step=10)

# Generate and predict button
if st.sidebar.button('Generate and Predict'):
    if word1 and word2 and word3:
        sequence = generate_sequence_from_words([word1, word2, word3], sequence_length)
        st.session_state.sequence = sequence  # Store the sequence in session state
        st.sidebar.text_area("Generated Sequence", sequence, height=100)
        st.sidebar.info("Note: The same words and sequence length will always produce the same sequence.")
        
        with st.spinner("Predicting protein structure... This may take a few minutes."):
            update(sequence, word1, word2, word3, sequence_length)
        
        # Add Analyze Protein button
        if st.button('Analyze Protein'):
            perform_blast_analysis(st.session_state.sequence)
    else:
        st.sidebar.warning("Please enter all three words to generate a sequence.")


# Information display
st.sidebar.markdown("""
## What to do next:
If you find interesting results from the sequence folding, you can explore further:
1. Learn more about protein structures and sequences.
2. Visit the [Protein Data Bank (PDB)](https://www.rcsb.org/) for known protein structures.
3. Compare your folded structure with known functional proteins by downloading your results.
4. Read about similar proteins to gain insights into potential functions.
**Remember, this folding is based on randomly generated sequences. Interpret the results with caution.
Enjoy exploring the world of protein sequences!  Share your high-confidence protein images with us on X [*@WandsAI*](https://x.com/wandsai)!
""")