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 a bench mark for scoring the confidence level in protein folding prediction based 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("""
  1. Take a screenshot of the protein structure above.
  2. Click the 'Share on X' button below to open a pre-filled post with your protein seed-words and score.
  3. Be sure to attach the screenshot of your protein before your post!
""", 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(""" ## What to do next: If you find an interesting protein from the sequence folding, you can explore it even further: 1. Click the 'analyze protein' button to use the [BLAST](https://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM=blastp&PAGE_TYPE=BlastSearch&LINK_LOC=blasthome) tool to see what you protein might do. The sequence identity will show how close of a match your protein is the the best match. *Note this can take several minutes 2. Download your protein data and visit the [Protein Data Bank (PDB)](https://www.rcsb.org/) to match your protein structure against known protein structures. 3. If you think you've discovered a new and useful protein message us! **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', )