alphafold-database-access
Access AlphaFold DB's 200M+ predicted structures by UniProt ID. Download PDB/mmCIF, analyze pLDDT/PAE, bulk-fetch proteomes via Google Cloud. For experimental structures use PDB; for prediction use ColabFold or ESMFold.
npx skills add BioTender-max/awesome-bio-agent-skills --skill alphafold-database-access --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# AlphaFold Database Access ## 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 predictions via BioPython or REST API, download coordinate files in multiple formats, analyze confidence metrics, and retrieve bulk proteome datasets via Google Cloud. ## When to Use - Retrieving AI-predicted protein structures by UniProt accession - Downloading PDB/mmCIF coordinate files for structural analysis or docking - Analyzing prediction confidence (pLDDT per-residue, PAE domain-level) - Bulk-downloading entire proteome predictions via Google Cloud - Comparing predicted structures with experimental PDB structures - Building structural models for proteins lacking experimental data - Identifying high-confidence binding sites for drug discovery - For experimental structures only → use PDB directly - For running AlphaFold predictions → use ColabFold or local AlphaFold ## Prerequisites ```bash # Core (BioPython for structure access) pip install biopython requests numpy matplotlib # Optional: Google Cloud for bulk access pip install google-cloud-bigquery google-cloud-storage ``` ## Quick Start
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- 1. Prediction Retrieval
- 2. Structure File Download
- 3. Confidence Metrics Analysis
- 4. Bulk Data Access (Google Cloud)
- 5. Structure Parsing & Analysis
- Key Concepts
- Confidence Interpretation
- AlphaFold ID Format
- File Types
Core (BioPython for structure access) pip install biopython requests numpy matplotlib pip install google-cloud-bigquery google-cloud-storage List available data gsutil ls gs://public-datasets-deepmind-alphafold-v4/ Download entire proteome by taxonomy ID gsutil -m cp gs://public-datasets-deepmind-alphafold-v4/proteomes/proteome-tax_id-9606-*_v4.tar . Download accession index gsutil cp gs://public-datasets-deepmind-alphafold-v4/accession_ids.csv .
What does the alphafold-database-access skill do?
Access AlphaFold DB's 200M+ predicted structures by UniProt ID. Download PDB/mmCIF, analyze pLDDT/PAE, bulk-fetch proteomes via Google Cloud. For experimental structures use PDB; for prediction use ColabFold or ESMFold.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill alphafold-database-access --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.