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.
Detected risks:
The AlphaFold Database skill provides access to a vast repository of AI-predicted protein structures, specifically over 200 million models generated by DeepMind and EMBL-EBI. This skill solves the problem of accessing reliable structural data for proteins where experimental structures may be unavailable or incomplete. By utilizing this skill, users can retrieve detailed structural predictions along with confidence metrics, enabling informed decisions in research and development across various biological and pharmaceutical domains.
You can retrieve predictions by using either the Biopython library or direct API access via REST endpoints, allowing you to query structures by UniProt ID.
Structure files can be downloaded in multiple formats, including mmCIF/PDB for model coordinates, JSON for confidence scores, and PAE matrices.
While there is no specific limit mentioned, users should be mindful of the API usage guidelines to avoid throttling.
Yes, this skill is particularly useful for structure-based drug discovery and protein engineering where structural data is necessary.
The skill is compatible with Python, and it is recommended to use the Biopython library for simpler interactions.
Quick Setup:
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