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