brycewang-stanford/auto-empirical-research-skills/alphafold-api

Name: alphafold-api
Author: brycewang-stanford

Claude Code and agent skill by brycewang-stanford/auto-empirical-research-skills

Trust tier 3

community

By brycewang-stanford
Coding
brycewang-stanford/auto-empirical-research-skills
skills/43-wentorai-research-plugins/skills/domains/biomedical/alphafold-api/SKILL.md
Updated 6 Oct 2026
Version 1.0.01 version recorded

Source check pending

Checking that the SKILL.md file is still in the repository.

Query AlphaFold protein structure predictions by UniProt accession

Install

npx vskill@latest install brycewang-stanford/auto-empirical-research-skills/alphafold-api

SKILL.md

The same SKILL.md was published earlier as wentorai/research-plugins/alphafold-api.

AlphaFold Protein Structure Database API

Overview

The AlphaFold DB, maintained by EMBL-EBI and DeepMind, provides open access to over 200 million protein structure predictions. The REST API enables programmatic lookup of predicted structures, confidence metrics (pLDDT, PAE), and downloadable structure files (PDB, mmCIF, BinaryCIF) keyed on UniProt accessions. Free, no authentication required.

Authentication

None. All endpoints are publicly accessible without API keys or tokens.

Core Endpoints

Base URL: https://alphafold.ebi.ac.uk/api

1. Get Prediction by UniProt Accession

Retrieves all AlphaFold models for a given UniProt accession or model ID.

bash

curl "https://alphafold.ebi.ac.uk/api/prediction/P04637"

Response (first entry, abbreviated):

json

[
  {
    "entryId": "AF-P04637-F1",
    "uniprotAccession": "P04637",
    "uniprotId": "P53_HUMAN",
    "uniprotDescription": "Cellular tumor antigen p53",
    "gene": "TP53",
    "organismScientificName": "Homo sapiens",
    "taxId": 9606,
    "globalMetricValue": 75.06,
    "fractionPlddtVeryHigh": 0.527,
    "fractionPlddtConfident": 0.071,
    "fractionPlddtLow": 0.104,
    "fractionPlddtVeryLow": 0.298,
    "latestVersion": 6,
    "modelCreatedDate": "2025-08-01T00:00:00Z",
    "sequenceStart": 1,
    "sequenceEnd": 393,
    "pdbUrl": "https://alphafold.ebi.ac.uk/files/AF-P04637-F1-model_v6.pdb",
    "cifUrl": "https://alphafold.ebi.ac.uk/files/AF-P04637-F1-model_v6.cif",
    "bcifUrl": "https://alphafold.ebi.ac.uk/files/AF-P04637-F1-model_v6.bcif",
    "paeImageUrl": "https://alphafold.ebi.ac.uk/files/AF-P04637-F1-predicted_aligned_error_v6.png",
    "paeDocUrl": "https://alphafold.ebi.ac.uk/files/AF-P04637-F1-predicted_aligned_error_v6.json",
    "plddtDocUrl": "https://alphafold.ebi.ac.uk/files/AF-P04637-F1-confidence_v6.json",
    "amAnnotationsUrl": "https://alphafold.ebi.ac.uk/files/AF-P04637-F1-aa-substitutions.csv"
  }
]

2. Per-Residue Confidence Scores (pLDDT)

Download the per-residue pLDDT confidence JSON linked in plddtDocUrl:

bash

curl "https://alphafold.ebi.ac.uk/files/AF-P04637-F1-confidence_v6.json"

Response (truncated):

json

{
  "residueNumber": [1, 2, 3, 4, 5],
  "confidenceScore": [40.66, 44.53, 49.97, 48.59, 44.88],
  "confidenceCategory": ["D", "D", "D", "D", "D"]
}

Categories: A (Very High, >90), B (Confident, 70-90), C (Low, 50-70), D (Very Low, <50).

3. UniProt Summary (3D-Beacons Format)

Returns model metadata following the 3D-Beacons data standard:

bash

curl "https://alphafold.ebi.ac.uk/api/uniprot/summary/P04637.json"

Response (abbreviated):

json

{
  "uniprot_entry": {
    "ac": "P04637",
    "id": "P53_HUMAN",
    "sequence_length": 393
  },
  "structures": [
    {
      "summary": {
        "model_identifier": "AF-P04637-F1",
        "model_url": "https://alphafold.ebi.ac.uk/files/AF-P04637-F1-model_v6.cif",
        "provider": "AlphaFold DB",
        "confidence_type": "pLDDT",
        "confidence_avg_local_score": 75.06,
        "coverage": 1.0
      }
    }
  ]
}

4. Download Structure Files

Structure files are available at the URLs returned in prediction responses:

bash

# PDB format
curl -O "https://alphafold.ebi.ac.uk/files/AF-P04637-F1-model_v6.pdb"
 
# mmCIF format
curl -O "https://alphafold.ebi.ac.uk/files/AF-P04637-F1-model_v6.cif"
 
# Predicted Aligned Error (PAE) matrix
curl -O "https://alphafold.ebi.ac.uk/files/AF-P04637-F1-predicted_aligned_error_v6.json"

Key Response Fields

Field	Type	Description
`entryId`	string	AlphaFold model ID (e.g., `AF-P04637-F1`)
`uniprotAccession`	string	UniProt accession code
`gene`	string	Gene symbol
`globalMetricValue`	float	Average pLDDT score (0-100)
`fractionPlddtVeryHigh`	float	Fraction of residues with pLDDT > 90
`fractionPlddtConfident`	float	Fraction with pLDDT 70-90
`fractionPlddtLow`	float	Fraction with pLDDT 50-70
`fractionPlddtVeryLow`	float	Fraction with pLDDT < 50
`pdbUrl`	string	Direct download URL for PDB file
`cifUrl`	string	Direct download URL for mmCIF file
`paeDocUrl`	string	URL for predicted aligned error JSON
`plddtDocUrl`	string	URL for per-residue confidence JSON
`latestVersion`	int	Model version number

Rate Limits

The AlphaFold DB API has no published per-request rate limits. EMBL-EBI's general fair use policy applies: usage that degrades service for others may result in blocking. For bulk downloads (entire proteomes), use the FTP archive at https://ftp.ebi.ac.uk/pub/databases/alphafold/ rather than repeated API calls.

Python Example

python

import requests
def get_alphafold_prediction(uniprot_id: str) -> dict:
    """Fetch AlphaFold structure prediction for a UniProt accession."""
    url = f"https://alphafold.ebi.ac.uk/api/prediction/{uniprot_id}"
    resp = requests.get(url)
    resp.raise_for_status()
    entries = resp.json()
    # Return the canonical (first) entry
    return entries[0] if entries else None
def get_confidence_scores(prediction: dict) -> dict:
    """Download per-residue pLDDT confidence scores."""
    resp = requests.get(prediction["plddtDocUrl"])
    resp.raise_for_status()
    return resp.json()
def download_structure(prediction: dict, fmt: str = "pdb",
                       output_dir: str = ".") -> str:
    """Download structure file in pdb, cif, or bcif format."""
    url_key = {"pdb": "pdbUrl", "cif": "cifUrl", "bcif": "bcifUrl"}[fmt]
    url = prediction[url_key]
    filename = url.split("/")[-1]
    path = f"{output_dir}/{filename}"
    resp = requests.get(url)
    resp.raise_for_status()
    with open(path, "wb") as f:
        f.write(resp.content)
    return path
# Example: fetch p53 structure and assess quality
pred = get_alphafold_prediction("P04637")
print(f"Gene: {pred['gene']} ({pred['uniprotDescription']})")
print(f"Organism: {pred['organismScientificName']}")
print(f"Average pLDDT: {pred['globalMetricValue']}")
print(f"Very high confidence: {pred['fractionPlddtVeryHigh']:.1%}")
# Download per-residue scores
scores = get_confidence_scores(pred)
high_conf = [i+1 for i, c in enumerate(scores["confidenceCategory"])
             if c in ("A", "B")]
print(f"High-confidence residues: {len(high_conf)}/{len(scores['residueNumber'])}")
# Download PDB file
path = download_structure(pred, fmt="pdb")
print(f"Structure saved to: {path}")

Academic Use Cases

Drug target assessment: Check pLDDT scores in binding pockets before docking
Homology model comparison: Compare AlphaFold predictions with experimental PDB structures
Disorder prediction: Low pLDDT regions (<50) correlate with intrinsically disordered regions
Variant interpretation: Use AlphaMissense annotations (via amAnnotationsUrl) to assess pathogenicity
Structural coverage: Quickly check if a protein of interest has a predicted structure
Batch proteome analysis: Retrieve predictions for all proteins in a reference proteome

References

AlphaFold Protein Structure Database
AlphaFold DB API Documentation
AlphaFold DB FTP (Bulk Downloads)
Jumper, J. et al. (2021). "Highly accurate protein structure prediction with AlphaFold." Nature 596, 583-589.
Varadi, M. et al. (2022). "AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models." Nucleic Acids Research 50(D1).

Security

Security scanPassed, 100 of 100Pattern checks on the skill's files.
Model reviewPassed

See the full security report

Quality evaluation

Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

See evaluation results

Source

SKILL.md in the repositoryCheckingskills/43-wentorai-research-plugins/skills/domains/biomedical/alphafold-api/SKILL.md

Popularity

Repository stars: 4,455
Repository forks: 532
7-day trend score: 42

Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.

Works with

Any agent vskill installs to: 56 targets in all. These 39 have a page on this site.

Claude Code
Cursor
GitHub Copilot
Windsurf
Codex
Gemini CLI
Cline
Amp
Roo Code
Goose
Continue
Kilo Code
Junie
Trae
OpenHands
Qwen Code
AI Agent
Tabnine Chat
Sourcegraph Cody
Amazon Q
Firebase Genkit
Sweep
Devin
Phind
Mentat
Claude Desktop
Aider
GPT Pilot
Plandex
bolt.new
v0
Replit Agent
Anse
TypingMind
BetterChatGPT
LibreChat
Warp AI
Pieces
Fig AI

Embed badge

[![vskill VERIFIED](https://verified-skill.com/api/v1/skills/brycewang-stanford/auto-empirical-research-skills/alphafold-api/badge)](https://verified-skill.com/skills/brycewang-stanford/auto-empirical-research-skills/alphafold-api)

Something wrong with this skill or its page? Tell us.