/emdb-database

name: "emdb-database"
description: "Look up EMDB cryo-EM density maps and fitted atomic models via the entry REST API + EBI Search WS. Fetch entry metadata (resolution, method, organism, sample), map download URLs, fitted PDB IDs, and citations. Keyword search via EBI Search. No auth. For atomic coordinates use pdb-database; for AlphaFold predictions use alphafold-database-access."
license: "CC-BY-4.0"

EMDB Database

Overview

The Electron Microscopy Data Bank (EMDB) at EBI archives 3D electron microscopy density maps — primarily cryo-EM and cryo-ET — for macromolecular assemblies (30,000+ entries: ribosomes, membrane proteins, viruses, large complexes). Access is split across two services:

• **EMDB Entry API** (`https://www.ebi.ac.uk/emdb/api/entry/{EMD-XXXXX}`) — the canonical per-entry JSON containing metadata, map header, fitted PDB list, and citation. Sub-endpoints like `/map`, `/fitted`, `/publications` do **not** exist — all those data live inside the single entry response.
• **EBI Search WS** (`https://www.ebi.ac.uk/ebisearch/ws/rest/emdb`) — the real keyword search backend (the bare `https://www.ebi.ac.uk/emdb/api/search/` endpoint ignores the query and just returns recent entries).

No authentication or API key is required.

When to Use

• Finding cryo-EM density maps by keyword (e.g., "spike protein", "ribosome 70S")
• Fetching the download URL of a `.map.gz` density file for use in ChimeraX / PyMOL
• Identifying fitted PDB atomic models for an EMDB map (and the reverse)
• Retrieving entry metadata — resolution, reconstruction method, organism, sample
• Listing cryo-EM structures filtered by organism or resolution cutoff
• Pulling the primary citation (journal, DOI, PubMed ID) for an EMDB entry
• Use `pdb-database` instead when you need experimentally determined atomic coordinates
• Use `alphafold-database-access` for AI-predicted structures; EMDB is for experimental EM maps only

Prerequisites

• **Python packages**: `requests`, `pandas`, `matplotlib`
• **Data requirements**: EMDB entry IDs (`EMD-XXXXX`), keyword search strings, or PDB IDs for cross-referencing
• **Environment**: internet connection; no API key
• **Rate limits**: no official published limits; add `time.sleep(0.2)` between requests in batch loops for polite access

pip install requests pandas matplotlib

Quick Start

import requests

EMDB_API   = "https://www.ebi.ac.uk/emdb/api"
EBI_SEARCH = "https://www.ebi.ac.uk/ebisearch/ws/rest/emdb"

# Keyword search via EBI Search WS (NOT /emdb/api/search/, which ignores q=)
r = requests.get(EBI_SEARCH,
                 params={"query": "sars-cov-2 spike", "size": 5, "format": "json",
                         "fields": "id,name,resolution,em_method,organism"},
                 timeout=30)
r.raise_for_status()
res = r.json()
print(f"Total hits: {res['hitCount']}")
for e in res["entries"]:
    f = e["fields"]
    name = (f.get("name") or [""])[0][:60]
    resol = (f.get("resolution") or ["?"])[0]
    print(f"  {e['id']}: {name}  ({resol} Å)")

Core API

Query 1: Keyword Search (EBI Search WS)

Returns a paged hit list keyed by EMDB ID, with the requested `fields` per entry.

import requests, pandas as pd

EBI_SEARCH = "https://www.ebi.ac.uk/ebisearch/ws/rest/emdb"

def emdb_search(query, size=20, start=0,
                fields="id,name,resolution,em_method,organism"):
    r = requests.get(EBI_SEARCH,
                     params={"query": query, "size": size, "start": start,
                             "format": "json", "fields": fields},
                     timeout=30)
    r.raise_for_status()
    return r.json()

data = emdb_search("ribosome 70S", size=10)
rows = []
for e in data["entries"]:
    f = e["fields"]
    rows.append({
        "emdb_id": e["id"],
        "name": (f.get("name") or [""])[0],
        "resolution_A": float((f.get("resolution") or [0])[0] or 0) or None,
        "em_method": (f.get("em_method") or [""])[0],
        "organism": (f.get("organism") or [""])[0],
    })
df = pd.DataFrame(rows)
print(f"hitCount={data['hitCount']}; first {len(df)} rows:")
print(df.to_string(index=False))

# Paged retrieval — iterate `start` until exhausting hitCount
def emdb_search_all(query, size=100, max_pages=5,
                    fields="id,name,resolution,em_method"):
    out = []
    for page in range(max_pages):
        d = emdb_search(query, size=size, start=page * size, fields=fields)
        if not d["entries"]:
            break
        out.extend(d["entries"])
        if len(out) >= d["hitCount"]:
            break
    return out

hits = emdb_search_all("ferritin", size=50, max_pages=2)
print(f"Pulled {len(hits)} ferritin entries")

Query 2: Entry Metadata

The single entry endpoint returns all metadata, the map header, fitted PDB list, and the citation in one document. Read field paths carefully — most are nested.

import requests

EMDB_API = "https://www.ebi.ac.uk/emdb/api"

def emdb_entry(emdb_id):
    r = requests.get(f"{EMDB_API}/entry/{emdb_id}", timeout=30)
    r.raise_for_status()
    return r.json()

e = emdb_entry("EMD-30210")  # nsp12-nsp7-nsp8 + Remdesivir (RdRp)
print(f"Title       : {e['admin']['title']}")
print(f"Status      : {e['admin']['current_status']}")
print(f"Key dates   : {e['admin'].get('key_dates')}")

sd = e["structure_determination_list"]["structure_determination"][0]
ip = sd["image_processing"][0]
res = ip["final_reconstruction"]["resolution"]
print(f"Method      : {sd['method']}")
print(f"Resolution  : {res['valueOf_']} {res['units']}  (type: {res['res_type']})")

Query 3: Map Header / Download Info

`entry["map"]` contains the file name, format, voxel grid, axis order, cell, contour level(s), and recommended display threshold.

import requests

EMDB_API = "https://www.ebi.ac.uk/emdb/api"
e = requests.get(f"{EMDB_API}/entry/EMD-30210", timeout=30).json()

m = e["map"]
print(f"Map file     : {m.get('file')}")