alphafold-database — База данных 3D-структур белков, предсказанных ИИ

--- name: alphafold-database description: Access AlphaFold 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. license: Unknown metadata: skill-author: K-Dense Inc. --- # 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:** > ⚠️ **Security Note**: The example below uses `shell=True` for simplicity. In production environments, prefer using `subprocess.run()` with a list of arguments to prevent command injection vulnerabilities. See [Python subprocess security](https://docs.python.org/3/library/subprocess.html#security-considerations). ```python import subprocess import shlex def download_proteome(taxonomy_id, output_dir="./proteomes"): """Download all AlphaFold predictions for a species""" # Validate taxonomy_id is an integer to prevent injection if not isinstance(taxonomy_id, int): raise ValueError("taxonomy_id must be an integer") pattern = f"gs://public-datasets-deepmind-alphafold-v4/proteomes/proteome-tax_id-{taxonomy_id}-*_v4.tar" # Use list form instead of shell=True for security subprocess.run(["gsutil", "-m", "cp", pattern, f"{output_dir}/"], 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