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+---
+name: alphafold-database
+description: "Access AlphaFold's 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology."
+---
+
+# AlphaFold Database
+
+## Overview
+
+AlphaFold DB is a public repository of AI-predicted 3D protein structures for over 200 million proteins, maintained by DeepMind and EMBL-EBI. Access structure predictions with confidence metrics, download coordinate files, retrieve bulk datasets, and integrate predictions into computational workflows.
+
+## When to Use This Skill
+
+This skill should be used when working with AI-predicted protein structures in scenarios such as:
+
+- Retrieving protein structure predictions by UniProt ID or protein name
+- Downloading PDB/mmCIF coordinate files for structural analysis
+- Analyzing prediction confidence metrics (pLDDT, PAE) to assess reliability
+- Accessing bulk proteome datasets via Google Cloud Platform
+- Comparing predicted structures with experimental data
+- Performing structure-based drug discovery or protein engineering
+- Building structural models for proteins lacking experimental structures
+- Integrating AlphaFold predictions into computational pipelines
+
+## Core Capabilities
+
+### 1. Searching and Retrieving Predictions
+
+**Using Biopython (Recommended):**
+
+The Biopython library provides the simplest interface for retrieving AlphaFold structures:
+
+```python
+from Bio.PDB import alphafold_db
+
+# Get all predictions for a UniProt accession
+predictions = list(alphafold_db.get_predictions("P00520"))
+
+# Download structure file (mmCIF format)
+for prediction in predictions:
+    cif_file = alphafold_db.download_cif_for(prediction, directory="./structures")
+    print(f"Downloaded: {cif_file}")
+
+# Get Structure objects directly
+from Bio.PDB import MMCIFParser
+structures = list(alphafold_db.get_structural_models_for("P00520"))
+```
+
+**Direct API Access:**
+
+Query predictions using REST endpoints:
+
+```python
+import requests
+
+# Get prediction metadata for a UniProt accession
+uniprot_id = "P00520"
+api_url = f"https://alphafold.ebi.ac.uk/api/prediction/{uniprot_id}"
+response = requests.get(api_url)
+prediction_data = response.json()
+
+# Extract AlphaFold ID
+alphafold_id = prediction_data[0]['entryId']
+print(f"AlphaFold ID: {alphafold_id}")
+```
+
+**Using UniProt to Find Accessions:**
+
+Search UniProt to find protein accessions first:
+
+```python
+import urllib.parse, urllib.request
+
+def get_uniprot_ids(query, query_type='PDB_ID'):
+    """Query UniProt to get accession IDs"""
+    url = 'https://www.uniprot.org/uploadlists/'
+    params = {
+        'from': query_type,
+        'to': 'ACC',
+        'format': 'txt',
+        'query': query
+    }
+    data = urllib.parse.urlencode(params).encode('ascii')
+    with urllib.request.urlopen(urllib.request.Request(url, data)) as response:
+        return response.read().decode('utf-8').splitlines()
+
+# Example: Find UniProt IDs for a protein name
+protein_ids = get_uniprot_ids("hemoglobin", query_type="GENE_NAME")
+```
+
+### 2. Downloading Structure Files
+
+AlphaFold provides multiple file formats for each prediction:
+
+**File Types Available:**
+
+- **Model coordinates** (`model_v4.cif`): Atomic coordinates in mmCIF/PDBx format
+- **Confidence scores** (`confidence_v4.json`): Per-residue pLDDT scores (0-100)
+- **Predicted Aligned Error** (`predicted_aligned_error_v4.json`): PAE matrix for residue pair confidence
+
+**Download URLs:**
+
+```python
+import requests
+
+alphafold_id = "AF-P00520-F1"
+version = "v4"
+
+# Model coordinates (mmCIF)
+model_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-model_{version}.cif"
+response = requests.get(model_url)
+with open(f"{alphafold_id}.cif", "w") as f:
+    f.write(response.text)
+
+# Confidence scores (JSON)
+confidence_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-confidence_{version}.json"
+response = requests.get(confidence_url)
+confidence_data = response.json()
+
+# Predicted Aligned Error (JSON)
+pae_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-predicted_aligned_error_{version}.json"
+response = requests.get(pae_url)
+pae_data = response.json()
+```
+
+**PDB Format (Alternative):**
+
+```python
+# Download as PDB format instead of mmCIF
+pdb_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-model_{version}.pdb"
+response = requests.get(pdb_url)
+with open(f"{alphafold_id}.pdb", "wb") as f:
+    f.write(response.content)
+```
+
+### 3. Working with Confidence Metrics
+
+AlphaFold predictions include confidence estimates critical for interpretation:
+
+**pLDDT (per-residue confidence):**
+
+```python
+import json
+import requests
+
+# Load confidence scores
+alphafold_id = "AF-P00520-F1"
+confidence_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-confidence_v4.json"
+confidence = requests.get(confidence_url).json()
+
+# Extract pLDDT scores
+plddt_scores = confidence['confidenceScore']
+
+# Interpret confidence levels
+# pLDDT > 90: Very high confidence
+# pLDDT 70-90: High confidence
+# pLDDT 50-70: Low confidence
+# pLDDT < 50: Very low confidence
+
+high_confidence_residues = [i for i, score in enumerate(plddt_scores) if score > 90]
+print(f"High confidence residues: {len(high_confidence_residues)}/{len(plddt_scores)}")
+```
+
+**PAE (Predicted Aligned Error):**
+
+PAE indicates confidence in relative domain positions:
+
+```python
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Load PAE matrix
+pae_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-predicted_aligned_error_v4.json"
+pae = requests.get(pae_url).json()
+
+# Visualize PAE matrix
+pae_matrix = np.array(pae['distance'])
+plt.figure(figsize=(10, 8))
+plt.imshow(pae_matrix, cmap='viridis_r', vmin=0, vmax=30)
+plt.colorbar(label='PAE (Å)')
+plt.title(f'Predicted Aligned Error: {alphafold_id}')
+plt.xlabel('Residue')
+plt.ylabel('Residue')
+plt.savefig(f'{alphafold_id}_pae.png', dpi=300, bbox_inches='tight')
+
+# Low PAE values (<5 Å) indicate confident relative positioning
+# High PAE values (>15 Å) suggest uncertain domain arrangements
+```
+
+### 4. Bulk Data Access via Google Cloud
+
+For large-scale analyses, use Google Cloud datasets:
+
+**Google Cloud Storage:**
+
+```bash
+# Install gsutil
+uv pip install gsutil
+
+# List available data
+gsutil ls gs://public-datasets-deepmind-alphafold-v4/
+
+# Download entire proteomes (by taxonomy ID)
+gsutil -m cp gs://public-datasets-deepmind-alphafold-v4/proteomes/proteome-tax_id-9606-*.tar .
+
+# Download specific files
+gsutil cp gs://public-datasets-deepmind-alphafold-v4/accession_ids.csv .
+```
+
+**BigQuery Metadata Access:**
+
+```python
+from google.cloud import bigquery
+
+# Initialize client
+client = bigquery.Client()
+
+# Query metadata
+query = """
+SELECT
+  entryId,
+  uniprotAccession,
+  organismScientificName,
+  globalMetricValue,
+  fractionPlddtVeryHigh
+FROM `bigquery-public-data.deepmind_alphafold.metadata`
+WHERE organismScientificName = 'Homo sapiens'
+  AND fractionPlddtVeryHigh > 0.8
+LIMIT 100
+"""
+
+results = client.query(query).to_dataframe()
+print(f"Found {len(results)} high-confidence human proteins")
+```
+
+**Download by Species:**
+
+```python
+import subprocess
+
+def download_proteome(taxonomy_id, output_dir="./proteomes"):
+    """Download all AlphaFold predictions for a species"""
+    pattern = f"gs://public-datasets-deepmind-alphafold-v4/proteomes/proteome-tax_id-{taxonomy_id}-*_v4.tar"
+    cmd = f"gsutil -m cp {pattern} {output_dir}/"
+    subprocess.run(cmd, shell=True, check=True)
+
+# Download E. coli proteome (tax ID: 83333)
+download_proteome(83333)
+
+# Download human proteome (tax ID: 9606)
+download_proteome(9606)
+```
+
+### 5. Parsing and Analyzing Structures
+
+Work with downloaded AlphaFold structures using BioPython:
+
+```python
+from Bio.PDB import MMCIFParser, PDBIO
+import numpy as np
+
+# Parse mmCIF file
+parser = MMCIFParser(QUIET=True)
+structure = parser.get_structure("protein", "AF-P00520-F1-model_v4.cif")
+
+# Extract coordinates
+coords = []
+for model in structure:
+    for chain in model:
+        for residue in chain:
+            if 'CA' in residue:  # Alpha carbons only
+                coords.append(residue['CA'].get_coord())
+
+coords = np.array(coords)
+print(f"Structure has {len(coords)} residues")
+
+# Calculate distances
+from scipy.spatial.distance import pdist, squareform
+distance_matrix = squareform(pdist(coords))
+
+# Identify contacts (< 8 Å)
+contacts = np.where((distance_matrix > 0) & (distance_matrix < 8))
+print(f"Number of contacts: {len(contacts[0]) // 2}")
+```
+
+**Extract B-factors (pLDDT values):**
+
+AlphaFold stores pLDDT scores in the B-factor column:
+
+```python
+from Bio.PDB import MMCIFParser
+
+parser = MMCIFParser(QUIET=True)
+structure = parser.get_structure("protein", "AF-P00520-F1-model_v4.cif")
+
+# Extract pLDDT from B-factors
+plddt_scores = []
+for model in structure:
+    for chain in model:
+        for residue in chain:
+            if 'CA' in residue:
+                plddt_scores.append(residue['CA'].get_bfactor())
+
+# Identify high-confidence regions
+high_conf_regions = [(i, score) for i, score in enumerate(plddt_scores, 1) if score > 90]
+print(f"High confidence residues: {len(high_conf_regions)}")
+```
+
+### 6. Batch Processing Multiple Proteins
+
+Process multiple predictions efficiently:
+
+```python
+from Bio.PDB import alphafold_db
+import pandas as pd
+
+uniprot_ids = ["P00520", "P12931", "P04637"]  # Multiple proteins
+results = []
+
+for uniprot_id in uniprot_ids:
+    try:
+        # Get prediction
+        predictions = list(alphafold_db.get_predictions(uniprot_id))
+
+        if predictions:
+            pred = predictions[0]
+
+            # Download structure
+            cif_file = alphafold_db.download_cif_for(pred, directory="./batch_structures")
+
+            # Get confidence data
+            alphafold_id = pred['entryId']
+            conf_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-confidence_v4.json"
+            conf_data = requests.get(conf_url).json()
+
+            # Calculate statistics
+            plddt_scores = conf_data['confidenceScore']
+            avg_plddt = np.mean(plddt_scores)
+            high_conf_fraction = sum(1 for s in plddt_scores if s > 90) / len(plddt_scores)
+
+            results.append({
+                'uniprot_id': uniprot_id,
+                'alphafold_id': alphafold_id,
+                'avg_plddt': avg_plddt,
+                'high_conf_fraction': high_conf_fraction,
+                'length': len(plddt_scores)
+            })
+    except Exception as e:
+        print(f"Error processing {uniprot_id}: {e}")
+
+# Create summary DataFrame
+df = pd.DataFrame(results)
+print(df)
+```
+
+## Installation and Setup
+
+### Python Libraries
+
+```bash
+# Install Biopython for structure access
+uv pip install biopython
+
+# Install requests for API access
+uv pip install requests
+
+# For visualization and analysis
+uv pip install numpy matplotlib pandas scipy
+
+# For Google Cloud access (optional)
+uv pip install google-cloud-bigquery gsutil
+```
+
+### 3D-Beacons API Alternative
+
+AlphaFold can also be accessed via the 3D-Beacons federated API:
+
+```python
+import requests
+
+# Query via 3D-Beacons
+uniprot_id = "P00520"
+url = f"https://www.ebi.ac.uk/pdbe/pdbe-kb/3dbeacons/api/uniprot/summary/{uniprot_id}.json"
+response = requests.get(url)
+data = response.json()
+
+# Filter for AlphaFold structures
+af_structures = [s for s in data['structures'] if s['provider'] == 'AlphaFold DB']
+```
+
+## Common Use Cases
+
+### Structural Proteomics
+- Download complete proteome predictions for analysis
+- Identify high-confidence structural regions across proteins
+- Compare predicted structures with experimental data
+- Build structural models for protein families
+
+### Drug Discovery
+- Retrieve target protein structures for docking studies
+- Analyze binding site conformations
+- Identify druggable pockets in predicted structures
+- Compare structures across homologs
+
+### Protein Engineering
+- Identify stable/unstable regions using pLDDT
+- Design mutations in high-confidence regions
+- Analyze domain architectures using PAE
+- Model protein variants and mutations
+
+### Evolutionary Studies
+- Compare ortholog structures across species
+- Analyze conservation of structural features
+- Study domain evolution patterns
+- Identify functionally important regions
+
+## Key Concepts
+
+**UniProt Accession:** Primary identifier for proteins (e.g., "P00520"). Required for querying AlphaFold DB.
+
+**AlphaFold ID:** Internal identifier format: `AF-[UniProt accession]-F[fragment number]` (e.g., "AF-P00520-F1").
+
+**pLDDT (predicted Local Distance Difference Test):** Per-residue confidence metric (0-100). Higher values indicate more confident predictions.
+
+**PAE (Predicted Aligned Error):** Matrix indicating confidence in relative positions between residue pairs. Low values (<5 Å) suggest confident relative positioning.
+
+**Database Version:** Current version is v4. File URLs include version suffix (e.g., `model_v4.cif`).
+
+**Fragment Number:** Large proteins may be split into fragments. Fragment number appears in AlphaFold ID (e.g., F1, F2).
+
+## Confidence Interpretation Guidelines
+
+**pLDDT Thresholds:**
+- **>90**: Very high confidence - suitable for detailed analysis
+- **70-90**: High confidence - generally reliable backbone structure
+- **50-70**: Low confidence - use with caution, flexible regions
+- **<50**: Very low confidence - likely disordered or unreliable
+
+**PAE Guidelines:**
+- **<5 Å**: Confident relative positioning of domains
+- **5-10 Å**: Moderate confidence in arrangement
+- **>15 Å**: Uncertain relative positions, domains may be mobile
+
+## Resources
+
+### references/api_reference.md
+
+Comprehensive API documentation covering:
+- Complete REST API endpoint specifications
+- File format details and data schemas
+- Google Cloud dataset structure and access patterns
+- Advanced query examples and batch processing strategies
+- Rate limiting, caching, and best practices
+- Troubleshooting common issues
+
+Consult this reference for detailed API information, bulk download strategies, or when working with large-scale datasets.
+
+## Important Notes
+
+### Data Usage and Attribution
+
+- AlphaFold DB is freely available under CC-BY-4.0 license
+- Cite: Jumper et al. (2021) Nature and Varadi et al. (2022) Nucleic Acids Research
+- Predictions are computational models, not experimental structures
+- Always assess confidence metrics before downstream analysis
+
+### Version Management
+
+- Current database version: v4 (as of 2024-2025)
+- File URLs include version suffix (e.g., `_v4.cif`)
+- Check for database updates regularly
+- Older versions may be deprecated over time
+
+### Data Quality Considerations
+
+- High pLDDT doesn't guarantee functional accuracy
+- Low confidence regions may be disordered in vivo
+- PAE indicates relative domain confidence, not absolute positioning
+- Predictions lack ligands, post-translational modifications, and cofactors
+- Multi-chain complexes are not predicted (single chains only)
+
+### Performance Tips
+
+- Use Biopython for simple single-protein access
+- Use Google Cloud for bulk downloads (much faster than individual files)
+- Cache downloaded files locally to avoid repeated downloads
+- BigQuery free tier: 1 TB processed data per month
+- Consider network bandwidth for large-scale downloads
+
+## Additional Resources
+
+- **AlphaFold DB Website:** https://alphafold.ebi.ac.uk/
+- **API Documentation:** https://alphafold.ebi.ac.uk/api-docs
+- **Google Cloud Dataset:** https://cloud.google.com/blog/products/ai-machine-learning/alphafold-protein-structure-database
+- **3D-Beacons API:** https://www.ebi.ac.uk/pdbe/pdbe-kb/3dbeacons/
+- **AlphaFold Papers:**
+  - Nature (2021): https://doi.org/10.1038/s41586-021-03819-2
+  - Nucleic Acids Research (2024): https://doi.org/10.1093/nar/gkad1011
+- **Biopython Documentation:** https://biopython.org/docs/dev/api/Bio.PDB.alphafold_db.html
+- **GitHub Repository:** https://github.com/google-deepmind/alphafold