first

.gitignore

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+env/

README.txt

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+Create a Gradio app which requests the uniprot ID of a protein and returns the protein sequence as a data frame.
+Use the XML file returned. Parse the annotations and returns the following columns in the dataframe:
+
+ - Residue Number
+ - Residue code (1 letter) for the wild type residue
+ - Secondary structure (taken from the annotations)
+ - Pfam domain  
+ - Disorder  
+ - Disulfilde bridges 
+ - Glycosylation sites
+ - Phosphorylation sites 
+ - active sites
+ - metal binding sites
+ - DNA binding sites
+ - RNA binding sites
+ - ligand binding sites
+ - modified
+ 
+ 

app.py

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+import gradio as gr
+import pandas as pd
+from io import StringIO
+from urllib.request import urlopen
+import re
+import xml.etree.ElementTree as ET
+
+def get_uniprot_data(uniprot_id):
+    """
+    Fetches protein sequence and annotation data from UniProt in XML format.
+
+    Args:
+        uniprot_id: The UniProt ID of the protein.
+
+    Returns:
+        A tuple containing:
+        - protein_sequence: The protein sequence as a string.
+        - annotations: A dictionary containing annotations.
+        - error_message: An error message if something goes wrong, otherwise None
+    """
+    try:
+        # Fetch XML data
+        url = f"https://www.uniprot.org/uniprot/{uniprot_id}.xml"
+        response = urlopen(url).read().decode('utf-8')
+        
+        # Parse XML
+        root = ET.fromstring(response)
+        
+        # Get sequence
+        sequence_elem = root.find(".//{http://uniprot.org/uniprot}sequence")
+        if sequence_elem is None:
+            return None, None, "Could not find sequence in UniProt response"
+        protein_sequence = sequence_elem.text.strip()
+        
+        # Get feature annotations
+        annotations = {}
+        for feature in root.findall(".//{http://uniprot.org/uniprot}feature"):
+            feature_type = feature.get('type')
+            description = feature.get('description', '')
+            
+            # Get position information
+            location = feature.find("{http://uniprot.org/uniprot}location")
+            if location is None:
+                continue
+                
+            position = location.find("{http://uniprot.org/uniprot}position")
+            if position is not None:
+                start = end = int(position.get('position'))
+            else:
+                begin = location.find("{http://uniprot.org/uniprot}begin")
+                end_elem = location.find("{http://uniprot.org/uniprot}end")
+                if begin is None or end_elem is None:
+                    continue
+                start = int(begin.get('position'))
+                end = int(end_elem.get('position'))
+            
+            if feature_type not in annotations:
+                annotations[feature_type] = []
+            annotations[feature_type].append((start, end, description))
+            
+        return protein_sequence, annotations, None
+        
+    except Exception as e:
+        return None, None, f"Error fetching or processing data from UniProt: {e}"
+
+def create_dataframe(protein_sequence, annotations):
+    """
+    Creates a Pandas DataFrame from protein sequence and annotations.
+    """
+    data = []
+    for i, residue in enumerate(protein_sequence):
+        residue_number = i + 1
+        row = {
+            "Residue Number": residue_number,
+            "Residue code": residue,
+            "Secondary structure": "",
+            "Domain": "",
+            "Pfam domain": "",
+            "Disorder": "",
+            "Disulfide bridges": "",
+            "Glycosylation sites": "",
+            "Phosphorylation sites": "",
+            "active sites": "",
+            "metal binding sites": "",
+            "DNA binding sites": "",
+            "RNA binding sites": "",
+            "ligand binding sites": "",
+            "modified": ""
+        }
+        data.append(row)
+
+    df = pd.DataFrame(data)
+
+    # Map UniProt feature types to our column names
+    feature_mapping = {
+        'helix': 'Secondary structure',
+        'strand': 'Secondary structure',
+        'turn': 'Secondary structure',
+        'domain': 'Domain',
+        'region': ['Pfam domain', 'Disorder'],  # Will check description
+        'disulfide bond': 'Disulfide bridges',
+        'glycosylation site': 'Glycosylation sites',
+        'modified residue': 'modified',
+        'active site': 'active sites',
+        'binding site': ['metal binding sites', 'DNA binding sites', 'RNA binding sites', 'ligand binding sites'],  # Will check description
+        'site': 'Phosphorylation sites'  # Will check description for phosphorylation
+    }
+
+    for feature_type, values in annotations.items():
+        for start, end, description in values:
+            feature_type_lower = feature_type.lower()
+            
+            # Get the corresponding column(s)
+            column = feature_mapping.get(feature_type_lower)
+            if not column:
+                continue
+                
+            # Handle cases where one feature type maps to multiple possible columns
+            if isinstance(column, list):
+                if feature_type_lower == 'region':
+                    if 'Pfam' in description:
+                        column = 'Pfam domain'
+                    elif 'disorder' in description.lower():
+                        column = 'Disorder'
+                    else:
+                        continue
+                elif feature_type_lower == 'binding site':
+                    if 'metal' in description.lower():
+                        column = 'metal binding sites'
+                    elif 'DNA' in description:
+                        column = 'DNA binding sites'
+                    elif 'RNA' in description:
+                        column = 'RNA binding sites'
+                    else:
+                        column = 'ligand binding sites'
+                        
+            # Fill in the annotation
+            for i in range(start - 1, end):
+                if i < len(df):
+                    current_value = df.loc[i, column]
+                    if current_value:
+                        df.loc[i, column] = f"{current_value}; {description}"
+                    else:
+                        df.loc[i, column] = description
+
+    return df
+
+def process_uniprot_id(uniprot_id):
+    """
+    Main function to process a UniProt ID.
+
+    Args:
+        uniprot_id: The UniProt ID.
+
+    Returns:
+        A Pandas DataFrame or an error message.
+    """
+    protein_sequence, annotations, error_message = get_uniprot_data(uniprot_id)
+
+    if error_message:
+        return error_message
+
+    if protein_sequence and annotations:
+        df = create_dataframe(protein_sequence, annotations)
+        return df
+    else:
+        return "Could not retrieve or process data for the given Uniprot ID"
+
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=process_uniprot_id,
+    inputs=gr.Textbox(label="UniProt ID", placeholder="e.g., P04637"),
+    outputs=gr.Dataframe(label="Protein Sequence and Annotations"),
+    title="UniProt Protein Sequence and Annotation Viewer",
+    description="Enter a UniProt ID to view the protein sequence and its annotations in a DataFrame."
+)
+
+iface.launch()