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import streamlit as st |
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import random |
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import hashlib |
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import py3Dmol |
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import requests |
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import io |
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from Bio import PDB |
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def generate_sequence_from_words(words, length): |
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seed = ' '.join(words).encode('utf-8') |
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random.seed(hashlib.md5(seed).hexdigest()) |
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amino_acids = "ACDEFGHIKLMNPQRSTVWY" |
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return ''.join(random.choice(amino_acids) for _ in range(length)) |
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def predict_structure(sequence): |
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url = "https://api.colabfold.com/batch" |
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data = { |
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"queries": [["query", sequence]], |
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"num_relax": 0, |
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"use_templates": False, |
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"num_models": 1 |
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} |
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response = requests.post(url, json=data) |
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if response.status_code == 200: |
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return response.json() |
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else: |
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st.error(f"Error in structure prediction: {response.text}") |
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return None |
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def visualize_protein(pdb_string): |
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view = py3Dmol.view(width=800, height=400) |
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view.addModel(pdb_string, 'pdb') |
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view.setStyle({'cartoon': {'color': 'spectrum'}}) |
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view.zoomTo() |
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return view |
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st.title("Protein Sequence Generator and Structure Predictor") |
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st.write("Enter three random words to seed your protein sequence:") |
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word1 = st.text_input("Word 1") |
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word2 = st.text_input("Word 2") |
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word3 = st.text_input("Word 3") |
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sequence_length = st.number_input("Enter desired sequence length", |
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min_value=50, |
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max_value=200, |
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value=100, |
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step=10) |
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if st.button("Generate Sequence and Predict Structure"): |
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if word1 and word2 and word3: |
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words = [word1, word2, word3] |
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sequence = generate_sequence_from_words(words, sequence_length) |
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st.write(f"Generated sequence inspired by '{word1}', '{word2}', and '{word3}' with length '{sequence_length}':") |
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st.code(sequence) |
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st.header("Protein Structure Prediction") |
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with st.spinner("Predicting protein structure... This may take a few minutes."): |
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prediction = predict_structure(sequence) |
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if prediction and 'pdb_string' in prediction[0]: |
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pdb_string = prediction[0]['pdb_string'] |
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view = visualize_protein(pdb_string) |
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st_py3dmol = py3Dmol.show3d(view, width=800, height=400) |
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st.components.v1.html(st_py3dmol.startjs, height=400) |
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plddt_scores = prediction[0].get('plddt', []) |
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if plddt_scores: |
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avg_plddt = sum(plddt_scores) / len(plddt_scores) |
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st.write(f"Average pLDDT score: {avg_plddt:.2f}") |
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st.write("pLDDT > 90: Very high confidence") |
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st.write("90 > pLDDT > 70: Confident") |
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st.write("70 > pLDDT > 50: Low confidence") |
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st.write("pLDDT < 50: Very low confidence") |
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else: |
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st.error("Failed to predict structure. Please try again.") |
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else: |
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st.error("Please enter all three words.") |
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st.markdown(""" |
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## What to do next: |
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1. Experiment with different seed words and sequence lengths. |
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2. Learn about how protein sequences relate to their predicted structures. |
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3. Remember that these are computational predictions and may not represent the actual biological structure. |
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4. For real protein structures, visit the [Protein Data Bank (PDB)](https://www.rcsb.org/). |
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Enjoy exploring the world of protein sequences and predicted structures! |
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""") |
