Agent skill · research science · aiskillstore
alphafold-database
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.
Why this skill is useful
Provides specific API commands and workflows for retrieving and analyzing protein structure predictions that the AI wouldn't reliably generate on its own.
What it needs
Requires google-cloud account access. About 8k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
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: Direct API Access: Query predictions using REST endpoints: Using UniProt to Find Accessions: Search UniProt to find protein accessions first: 2. Downloading Structure Files AlphaFold provides multiple file formats for each prediction: File Types Available: Model coordinates (modelv4.cif): Atomic coordinates in mmCIF/PDBx format Confidence scores (confidencev4.json): Per-residue pLDDT scores (0-100) Predicted Aligned Error (predictedalignederrorv4.json): PAE matrix for residue pair confidence Download URLs: PDB Format (Alternative): 3. Working with Confidence Metrics AlphaFold predictions include confidence estimates critical for interpretation: pLDDT (per-residue confidence): PAE (Predicted Aligned Error): PAE indicates confidence in relative domain positions: 4. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/alphafold-database--8d5528