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.
Fetches alphafold-database from davila7/claude-code-templates and configures it for Cursor.
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Restart Cursor to activate alphafold-database. Access via /alphafold-database in your agent's command palette.
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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.
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:
from Bio.PDB import alphafold_db
# Get all predictions for a UniProt accessionpredictions =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 directlyfrom Bio.PDB import MMCIFParser
structures =list(alphafold_db.get_structural_models_for("P00520"))
Direct API Access:
Query predictions using REST endpoints:
import requests
# Get prediction metadata for a UniProt accessionuniprot_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 IDalphafold_id = prediction_data[0]['entryId']print(f"AlphaFold ID: {alphafold_id}")
Using UniProt to Find Accessions:
Search UniProt to find protein accessions first:
import urllib.parse, urllib.request
defget_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 nameprotein_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
# Download as PDB format instead of mmCIFpdb_url =f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-model_{version}.pdb"response = requests.get(pdb_url)withopen(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):
import json
import requests
# Load confidence scoresalphafold_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 scoresplddt_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 confidencehigh_confidence_residues =[i for i, score inenumerate(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:
›Access to product documentation and roadmap tools (Jira, Notion, etc.)
›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Steps
1Install product management skill
2Start with user story generation for known feature
3Progress to competitive analysis: research 2-3 competitors
4Use for roadmap prioritization: apply RICE/ICE scoring
5Draft stakeholder communications and refine based on feedback
6Build template library for recurring PM tasks
7Share effective prompts with product team
Common Pitfalls
⚠Not validating competitive research—verify facts before sharing
⚠Accepting user stories without involving engineering team
⚠Over-relying on frameworks without qualitative judgment
⚠Not customizing outputs to company culture and communication style
⚠Skipping stakeholder validation of generated requirements
Best Practices
✓ Do
+Validate research and competitive analysis with real data
+Collaborate with engineering when generating technical requirements
+Customize frameworks and templates to your company context
+Use skill for first drafts, refine with stakeholder input
+Document successful prompt patterns for PM tasks
+Combine AI efficiency with human judgment and intuition
✗ Don't
−Don't publish competitive analysis without fact-checking
−Don't finalize user stories without engineering review
−Don't make prioritization decisions solely on AI scoring
−Don't skip customer validation of generated requirements
−Don't ignore company-specific context and culture
💡 Pro Tips
★Provide context: company goals, constraints, customer feedback
★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
★Use skill for 70% generation + 30% customization to company needs
When to Use This
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path
1Basic: user stories, feature specs, status updates
