Create app.py

app.py

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