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
import urllib.parse

urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)

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

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))

def render_mol(pdb):
    pdbview = py3Dmol.view(width=800, height=500)
    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)

def perform_blast_analysis(sequence):
    st.subheader('Protein Analysis')
    with st.spinner("Analyzing generated protein... This may take a several minutes. Stay tuned!"):
        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
                hsp = alignment.hsps[0]  # Get the first (best) HSP
                
                # Extract protein name and organism
                title_parts = alignment.title.split('|')
                protein_name = title_parts[-1].strip()
                organism = title_parts[-2].split('OS=')[-1].split('OX=')[0].strip()
                
                # Calculate identity percentage
                identity_percentage = (hsp.identities / alignment.length) * 100
                
                st.write(f"**Top Match:** {protein_name}")
                st.write(f"**Organism Code:** {organism}")
                st.write(f"**Sequence Identity:** {identity_percentage:.2f}%")
               
                
                # Fetch protein function (if available)
                if hasattr(alignment, 'description') and alignment.description:
                    st.write(f"**Potential Function:** {alignment.description}")
                
                st.write("No significant matches found. This might be a unique protein sequence!")
        except Exception as e:
            st.error(f"An error occurred during protein analysis: {str(e)}")
            st.write("Please try again later or contact support if the issue persists.")

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,
                                 timeout=300)
        response.raise_for_status()
        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)
        
        st.session_state.structure_info = {
            'pdb_string': pdb_string,
            'b_value': b_value,
            'word1': word1,
            'word2': word2,
            'word3': word3,
            'sequence_length': sequence_length
        }
        
        st.session_state.show_analyze_button = True

    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.")

def share_on_twitter(word1, word2, word3, length, plddt):
    tweet_text = f"I just generated a new protein using #GenPro2 from the seed-words '{word1}', '{word2}', and '{word3}' + sequence length {length}! It's Predictive Protein Score is: {plddt}%.  -- made by @WandAI"
    tweet_url = f"https://twitter.com/intent/tweet?text={urllib.parse.quote(tweet_text)}"
    return tweet_url

# Initialize session state variables
if 'sequence' not in st.session_state:
    st.session_state.sequence = None
if 'show_analyze_button' not in st.session_state:
    st.session_state.show_analyze_button = False
if 'structure_info' not in st.session_state:
    st.session_state.structure_info = None

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

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)

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
        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)
    else:
        st.sidebar.warning("Please enter all three words to generate a sequence.")

# Display structure information if available
if st.session_state.structure_info:
    info = st.session_state.structure_info
    st.subheader(f'Predicted protein structure using seed: {info["word1"]}, {info["word2"]}, and {info["word3"]} + length {info["sequence_length"]}')
    render_mol(info['pdb_string'])
    
    st.subheader('plDDT Confidence Score')
    st.write('plDDT is bench mark for scoring the confidence in prediction on a scale from 0-100%. 70% or more is really good!')
    plddt_score = int(info["b_value"] * 100)
    st.info(f'Average plDDT: {plddt_score}%')
 
    st.subheader("Share your unique protein on X(Twitter)")
    
    st.markdown("""
    <div style='background-color: #e6f2ff; padding: 10px; border-radius: 5px; font-size: 0.8em;'>
    <ol>
        <li>Take a screenshot of the protein structure above.</li>
        <li>Click the 'Share on X' button below to open a pre-filled protein seed-words and score.</li>
        <li>Attach your screenshot to your post before posting.</li>
    </ol>
    </div>
    """, unsafe_allow_html=True)
    
    tweet_url = share_on_twitter(info["word1"], info["word2"], info["word3"], info["sequence_length"], plddt_score)
    st.markdown(f"[Share Results]({tweet_url})")

    st.markdown("""
    ## Think might have discovered a useful and unique protein?  Here is what to do next:
    1. Analyze your protein using (BLAST)](https://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM=blastp&PAGE_TYPE=BlastSearch&LINK_LOC=blasthome).
    2. Download protein data and visit the [Protein Data Bank (PDB)](https://www.rcsb.org/) for known protein structures.
    3. Compare your folded protein structure and data with known functional proteins.

  
    **Remember, this folding is based on randomly generated sequences. Interpret the results with caution.
    Enjoy exploring the world of protein sequences! 
    """)

   
    col1, col2 = st.columns(2)
    with col1:
        if st.button('Analyze Protein'):
            perform_blast_analysis(st.session_state.sequence)
    
    with col2:
        st.download_button(
            label="Download PDB",
            data=info['pdb_string'],
            file_name='predicted.pdb',
            mime='text/plain',
        )