PDB Database

> Why no SDK? The rcsb-api Python SDK is convenient sugar over three public, no-auth REST endpoints (search.rcsb.org, data.rcsb.org, files.rcsb.org). When the SDK is unavailable, every operation can be reproduced with plain requests and a small JSON payload. This SKILL.md uses the REST path throughout so the code runs in any environment with requests installed.

Overview

RCSB PDB is the worldwide repository for 3D structural data of biological macromolecules with 200,000+ experimentally determined structures. Programmatic access is via three free, no-auth endpoints:

| API | Base URL | Method | Purpose | |---|---|---|---| | Search | https://search.rcsb.org/rcsbsearch/v2/query | POST JSON | Find PDB IDs by text, attribute filters, sequence, or 3D similarity | | Data | https://data.rcsb.org/graphql | POST GraphQL | Retrieve structured metadata (entries, polymer entities, assemblies, ligands) | | Files | https://files.rcsb.org/download/{id}.{format} | GET | Download coordinate files (mmCIF, PDB, FASTA) |

Use this skill for programmatic structural biology queries, drug target analysis, and protein family comparisons.

When to Use

• Searching for protein or nucleic acid crystal/cryo-EM/NMR structures by keyword or property
• Finding structures similar to a query sequence (MMseqs2) or 3D geometry (BioZernike)
• Retrieving experimental metadata (resolution, method, organism, deposition date) for structure sets
• Downloading coordinate files (PDB, mmCIF) for molecular dynamics, docking, or visualization
• Building structure-based datasets for machine learning or drug discovery pipelines
• Comparing protein-ligand complexes across a target family
• For AlphaFold predicted structures, use alphafold-database-access instead
• For protein sequence/annotation queries without structures, use uniprot-protein-database instead

Prerequisites

• Python packages: requests (only requirement). Optional: biopython for parsing downloaded coordinate files.
• No API key required: RCSB PDB is freely accessible.
• Rate limits: No published hard limit. Polite delays of time.sleep(0.2-0.5) between requests are sufficient; implement exponential backoff on HTTP 429.

bash
pip install requests
# Optional, for coordinate parsing:
pip install biopython

Quick Start

Typical search-then-fetch pattern: hit the Search API, get a list of PDB IDs, then resolve metadata via the GraphQL Data API.

python
import requests

SEARCH = "https://search.rcsb.org/rcsbsearch/v2/query"
DATA   = "https://data.rcsb.org/graphql"

# 1. Search: human X-ray structures of "kinase" at resolution < 2.0 Å
payload = {
    "query": {
        "type": "group", "logical_operator": "and",
        "nodes": [
            {"type": "terminal", "service": "full_text",
             "parameters": {"value": "kinase"}},
            {"type": "terminal", "service": "text",
             "parameters": {"attribute": "rcsb_entity_source_organism.scientific_name",
                            "operator": "exact_match", "value": "Homo sapiens"}},
            {"type": "terminal", "service": "text",
             "parameters": {"attribute": "rcsb_entry_info.resolution_combined",
                            "operator": "less", "value": 2.0}},
        ],
    },
    "return_type": "entry",
    "request_options": {"paginate": {"rows": 10}},
}
r = requests.post(SEARCH, json=payload, timeout=30)
r.raise_for_status()
result = r.json()
pdb_ids = [hit["identifier"] for hit in result["result_set"]]
print(f"Total matches: {result['total_count']}, first batch: {pdb_ids}")

# 2. Fetch metadata for the first hit via GraphQL
gql = """{ entry(entry_id: "%s") {
  struct { title }
  exptl { method }
  rcsb_entry_info { resolution_combined deposited_atom_count polymer_entity_count }
} }""" % pdb_ids[0]
r2 = requests.post(DATA, json={"query": gql}, timeout=30)
entry = r2.json()["data"]["entry"]
print(entry["struct"]["title"])
print(f"Method: {entry['exptl'][0]['method']}, Resolution: {entry['rcsb_entry_info']['resolution_combined']} Å")

Core API

Module 1: Text and Attribute Search

Free-text search uses service: "full_text" and searches across all indexed fields.

python
import requests
SEARCH = "https://search.rcsb.org/rcsbsearch/v2/query"

def text_search(keyword, rows=25):
    payload = {
        "query": {"type": "terminal", "service": "full_text",
                  "parameters": {"value": keyword}},
        "return_type": "entry",
        "request_options": {"paginate": {"rows": rows}},
    }
    r = requests.post(SEARCH, json=payload, timeout=30)
    r.raise_for_status()
    data = r.json()
    return [hit["identifier"] for hit in data["result_set"]], data["total_count"]

ids, total = text_search("hemoglobin")
print(f"Found {total} structures; first batch: {ids[:5]}")

Attribute search uses service: "text" with structured attribute/operator/value parameters.

python
import requests
SEARCH = "https://search.rcsb.org/rcsbsearch/v2/query"

def attribute_search(attribute, operator, value, return_type="entry", rows=25):
    payload = {
        "query": {"type": "terminal", "service": "text",
                  "parameters": {"attribute": attribute,
                                 "operator": operator,
                                 "value": value}},
        "return_type": return_type,
        "request_options": {"paginate": {"rows": rows}},
    }
    r = requests.post(SEARCH, json=payload, timeout=30)
    r.raise_for_status()
    return r.json()

# Human proteins
human = attribute_search("rcsb_entity_source_organism.scientific_name",
                          "exact_match", "Homo sapiens", rows=5)
print(f"Human structures: {human['total_count']}")

# X-ray only
xray = attribute_search("exptl.method", "exact_match", "X-RAY DIFFRACTION", rows=5)
print(f"X-ray structures: {xray['total_count']}")

# Resolution range: 1.5–2.5 Å
res = attribute_search(
    "rcsb_entry_info.resolution_combined", "range",
    {"from": 1.5, "to": 2.5, "include_lower": True, "include_upper": True},
    rows=5
)
print(f"1.5–2.5 Å: {res['total_count']}")

Module 2: Sequence Similarity Search

Find structures with similar sequences using MMseqs2. Service is "sequence"; target selects protein vs. nucleic acid.

python
import requests
SEARCH = "https://search.rcsb.org/rcsbsearch/v2/query"

kras_seq = ("MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVIDGETCLLDILDTAGQ"
            "EEYSAMRDQYMRTGEGFLCVFAINNTKSFEDIHHYREQIKRVKDSEDVPMVLVGNKCDLPS"
            "RTVDTKQAQDLARSYGIPFIETSAKTRQGVDDAFYTLVREIRKHKEKMSK")

payload = {
    "query": {
        "type": "terminal", "service": "sequence",
        "parameters": {
            "target": "pdb_protein_sequence",  # or "pdb_dna_sequence", "pdb_rna_sequence"
            "value": kras_seq,
            "evalue_cutoff": 0.1,
            "identity_cutoff": 0.9,
        },
    },
    "return_type": "polymer_entity",
    "request_options": {"paginate": {"rows": 10}},
}
r = requests.post(SEARCH, json=payload, timeout=30)
r.raise_for_status()
data = r.json()
print(f"KRAS-like hits: {data['total_count']}")
for hit in data["result_set"][:5]:
    print(f"  {hit['identifier']}  score={hit.get('score', 'n/a')}")

Module 3: Structure Similarity Search

Find structures with similar 3D geometry using BioZernike descriptors. Service is "structure"; pass the reference entry + assembly ID.

python
import requests
SEARCH = "https://search.rcsb.org/rcsbsearch/v2/query"

payload = {
    "query": {
        "type": "terminal", "service": "structure",
        "parameters": {
            "value": {"entry_id": "4HHB", "assembly_id": "1"},
            "operator": "strict_shape_match",  # or "relaxed_shape_match"
        },
    },
    "return_type": "polymer_entity",
    "request_options": {"paginate": {"rows": 10}},
}
r = requests.post(SEARCH, json=payload, timeout=30)
r.raise_for_status()
data = r.json()
print(f"Structurally similar to 4HHB: {data['total_count']}")
for hit in data["result_set"][:5]:
    print(f"  {hit['identifier']}  score={hit.get('score', 'n/a')}")

Module 4: Data Retrieval (GraphQL)

The GraphQL endpoint at data.rcsb.org/graphql is the canonical way to retrieve structured metadata for known PDB IDs. One request can pull fields across the full data hierarchy (entry → polymer_entity → assembly → chem_comp).

python
import requests
DATA = "https://data.rcsb.org/graphql"

# Entry-level metadata
gql = """{ entry(entry_id: "4HHB") {
  struct { title }
  exptl { method }
  rcsb_entry_info { resolution_combined deposited_atom_count polymer_entity_count nonpolymer_entity_count }
  rcsb_accession_info { deposit_date initial_release_date }
} }"""
r = requests.post(DATA, json={"query": gql}, timeout=30)
entry = r.json()["data"]["entry"]
print(f"Title       : {entry['struct']['title']}")
print(f"Method      : {entry['exptl'][0]['method']}")
print(f"Resolution  : {entry['rcsb_entry_info']['resolution_combined']} Å")
print(f"Atoms       : {entry['rcsb_entry_info']['deposited_atom_count']}")

python
# Polymer entity (sequence, organism, MW)
gql = """{ polymer_entity(entry_id: "4HHB", entity_id: "1") {
  entity_poly { pdbx_seq_one_letter_code }
  rcsb_polymer_entity { formula_weight }
  rcsb_entity_source_organism { scientific_name ncbi_taxonomy_id }
} }"""
r = requests.post(DATA, json={"query": gql}, timeout=30)
pe = r.json()["data"]["polymer_entity"]
print(f"Sequence (first 50): {pe['entity_poly']['pdbx_seq_one_letter_code'][:50]}")
print(f"Organism : {pe['rcsb_entity_source_organism'][0]['scientific_name']}")
print(f"MW       : {pe['rcsb_polymer_entity']['formula_weight']}")

python
# Batch: pull metadata for many entries in one request
gql = """{ entries(entry_ids: ["4HHB", "1A3N", "1HHB"]) {
  rcsb_id
  struct { title }
  exptl { method }
  rcsb_entry_info { resolution_combined }
} }"""
r = requests.post(DATA, json={"query": gql}, timeout=30)
for e in r.json()["data"]["entries"]:
    res = e["rcsb_entry_info"]["resolution_combined"]
    print(f"  {e['rcsb_id']}: {e['exptl'][0]['method']:<25} {res} Å  — {e['struct']['title'][:40]}")

Module 5: File Download

Coordinate files (mmCIF, PDB, FASTA, assembly variants) are served directly from files.rcsb.org.

python
import requests

def download_structure(pdb_id, fmt="cif", output_dir="."):
    """Download mmCIF / PDB / FASTA. URLs: .pdb, .cif, /fasta/entry/{ID}, .pdb1 (assembly)."""
    url = f"https://files.rcsb.org/download/{pdb_id}.{fmt}"
    r = requests.get(url, timeout=60)
    if r.status_code == 200:
        path = f"{output_dir}/{pdb_id}.{fmt}"
        # mmCIF / PDB are text; assemblies and biological units are also text
        with open(path, "w") as f:
            f.write(r.text)
        print(f"Downloaded {path}  ({len(r.text)/1024:.1f} KB)")
        return path
    print(f"HTTP {r.status_code} for {pdb_id}.{fmt}")
    return None

download_structure("4HHB", fmt="cif")
download_structure("4HHB", fmt="pdb")

python
# FASTA sequence for an entry
r = requests.get("https://www.rcsb.org/fasta/entry/4HHB", timeout=30)
r.raise_for_status()
print(r.text[:400])

Module 6: Query Composition (group + logical_operator)

Combine terminal queries with type: "group" and a logical_operator of "and" / "or". Nested groups give arbitrary boolean expressions; negation is via "node_id" references with "operator": "negate" on the group (rare — usually expressed as the inverse attribute filter).

python
import requests, datetime
SEARCH = "https://search.rcsb.org/rcsbsearch/v2/query"

# AND: high-resolution human structures
q_and = {
    "type": "group", "logical_operator": "and",
    "nodes": [
        {"type": "terminal", "service": "text",
         "parameters": {"attribute": "rcsb_entity_source_organism.scientific_name",
                        "operator": "exact_match", "value": "Homo sapiens"}},
        {"type": "terminal", "service": "text",
         "parameters": {"attribute": "rcsb_entry_info.resolution_combined",
                        "operator": "less", "value": 2.0}},
    ],
}

# OR: human or mouse
q_or = {
    "type": "group", "logical_operator": "or",
    "nodes": [
        {"type": "terminal", "service": "text",
         "parameters": {"attribute": "rcsb_entity_source_organism.scientific_name",
                        "operator": "exact_match", "value": "Homo sapiens"}},
        {"type": "terminal", "service": "text",
         "parameters": {"attribute": "rcsb_entity_source_organism.scientific_name",
                        "operator": "exact_match", "value": "Mus musculus"}},
    ],
}

# Combined: recent (last 30 days) + high-quality
one_month_ago = (datetime.date.today() - datetime.timedelta(days=30)).isoformat()
today = datetime.date.today().isoformat()
q_recent_hq = {
    "type": "group", "logical_operator": "and",
    "nodes": [
        {"type": "terminal", "service": "text",
         "parameters": {"attribute": "rcsb_entry_info.resolution_combined",
                        "operator": "less", "value": 2.0}},
        {"type": "terminal", "service": "text",
         "parameters": {"attribute": "refine.ls_R_factor_R_free",
                        "operator": "less", "value": 0.25}},
        {"type": "terminal", "service": "text",
         "parameters": {"attribute": "rcsb_accession_info.initial_release_date",
                        "operator": "range",
                        "value": {"from": one_month_ago, "to": today,
                                  "include_lower": True, "include_upper": True}}},
    ],
}

payload = {"query": q_recent_hq, "return_type": "entry",
           "request_options": {"paginate": {"rows": 5}}}
r = requests.post(SEARCH, json=payload, timeout=30)
print(f"Recent high-quality: {r.json()['total_count']} structures")

Module 7: Pagination + Batch with Rate Limiting

Search responses include total_count. Paginate with request_options.paginate.start and rows (max ~10000 per page in practice; 100–500 is a good batch size).

python
import requests, time
SEARCH = "https://search.rcsb.org/rcsbsearch/v2/query"

def search_all(query_node, return_type="entry", page=100, max_results=None, delay=0.3):
    """Paginate through every result; rate-limit between pages."""
    out, start = [], 0
    while True:
        payload = {"query": query_node, "return_type": return_type,
                   "request_options": {"paginate": {"start": start, "rows": page}}}
        r = requests.post(SEARCH, json=payload, timeout=30)
        r.raise_for_status()
        d = r.json()
        batch = [h["identifier"] for h in d.get("result_set", [])]
        if not batch:
            break
        out.extend(batch)
        if max_results and len(out) >= max_results:
            return out[:max_results]
        if len(batch) < page or len(out) >= d.get("total_count", 0):
            break
        start += page
        time.sleep(delay)
    return out

# Example: every insulin entry
ids = search_all(
    {"type": "terminal", "service": "full_text", "parameters": {"value": "insulin"}},
    max_results=300,
)
print(f"Insulin entries collected: {len(ids)}")

python
# Batch metadata fetch via GraphQL `entries(...)` to avoid one round-trip per ID
DATA = "https://data.rcsb.org/graphql"

def batch_metadata(pdb_ids, chunk=50):
    """Fetch (title, method, resolution) for many entries with one POST per chunk."""
    all_rows = []
    for i in range(0, len(pdb_ids), chunk):
        ids_arr = pdb_ids[i:i+chunk]
        ids_str = ", ".join(f'"{p}"' for p in ids_arr)
        gql = f"""{{ entries(entry_ids: [{ids_str}]) {{
            rcsb_id
            struct {{ title }}
            exptl {{ method }}
            rcsb_entry_info {{ resolution_combined }}
        }} }}"""
        r = requests.post(DATA, json={"query": gql}, timeout=60)
        r.raise_for_status()
        for e in r.json()["data"]["entries"]:
            res = e["rcsb_entry_info"]["resolution_combined"]
            all_rows.append({
                "pdb_id": e["rcsb_id"],
                "method": e["exptl"][0]["method"] if e["exptl"] else None,
                "resolution": res[0] if isinstance(res, list) and res else res,
                "title": e["struct"]["title"],
            })
    return all_rows

rows = batch_metadata(ids[:20])
for r in rows[:5]:
    print(f"  {r['pdb_id']}: {r['method']:<25} {r['resolution']} Å  — {r['title'][:50]}")

Key Concepts

Search Service Cheat Sheet

| Service | Use case | Required parameters | |---|---|---| | full_text | Free-text keyword across all indexed fields | value (string) | | text | Structured attribute filter | attribute, operator, value | | sequence | MMseqs2 sequence similarity | target ∈ {pdb_protein_sequence, pdb_dna_sequence, pdb_rna_sequence}, value (sequence), evalue_cutoff, identity_cutoff | | seqmotif | Pattern / regex / PROSITE motif | value (pattern), pattern_type ∈ {simple, prosite, regex} | | structure | 3D shape similarity (BioZernike) | value ({entry_id, assembly_id}), operator ∈ {strict_shape_match, relaxed_shape_match} | | strucmotif | 3D residue-arrangement motif | value (residue list), rmsd_cutoff | | chemical | Ligand similarity by SMILES/InChI | value, match_type ∈ {graph-exact, graph-relaxed, fingerprint-similarity, sub-structure-stereo-relaxed} |

AttributeQuery Operators (service: "text")

| Operator | Value shape | Example | |---|---|---| | exact_match | string | "Homo sapiens" | | contains_words / contains_phrase | string | "tyrosine kinase" | | equals / greater / less / greater_or_equal / less_or_equal | number | 2.0 | | range | {from, to, include_lower, include_upper} | {"from": 1.5, "to": 2.5, "include_lower": True, "include_upper": True} | | exists | (none) | — | | in | array | ["X-RAY DIFFRACTION", "ELECTRON MICROSCOPY"] |

Return Types

return_type controls the granularity of identifiers in result_set:

| return_type | Identifier shape | Example | |---|---|---| | entry | 4HHB | One per PDB ID | | polymer_entity | 4HHB_1 | One per polymer chain entity | | non_polymer_entity | 4HHB_2 | Ligands, cofactors | | assembly | 4HHB-1 | Biological unit | | polymer_instance | 4HHB.A | Individual chain coordinates | | mol_definition | HEM | Chemical component (PDB ligand code) |

Common Data API GraphQL Roots

| Root | Identifier shape | Returns | |---|---|---| | entry(entry_id: ...) | "4HHB" | Entry-level metadata | | entries(entry_ids: [...]) | array | Batch entry lookup | | polymer_entity(entry_id: ..., entity_id: ...) | "4HHB", "1" | Sequence + organism | | polymer_entity_instance(entry_id: ..., asym_id: ...) | "4HHB", "A" | Chain-level coords/metadata | | assembly(entry_id: ..., assembly_id: ...) | "4HHB", "1" | Biological assembly | | chem_comp(comp_id: ...) | "HEM" | Small molecule reference |

File Formats

| Format | URL pattern | Notes | |---|---|---| | mmCIF | https://files.rcsb.org/download/{id}.cif | Recommended; no atom-count limit | | PDB | https://files.rcsb.org/download/{id}.pdb | Legacy; 99,999 atom limit | | Assembly (mmCIF) | https://files.rcsb.org/download/{id}-assembly{N}.cif | Biological unit | | FASTA | https://www.rcsb.org/fasta/entry/{id} | Sequence only |

Common Workflows

Workflow 1: Drug Target Structure Set

Goal: Find high-resolution human EGFR structures with bound ligands.

python
import requests, time

SEARCH = "https://search.rcsb.org/rcsbsearch/v2/query"
DATA   = "https://data.rcsb.org/graphql"

payload = {
    "query": {
        "type": "group", "logical_operator": "and",
        "nodes": [
            {"type": "terminal", "service": "full_text",
             "parameters": {"value": "EGFR epidermal growth factor receptor"}},
            {"type": "terminal", "service": "text",
             "parameters": {"attribute": "rcsb_entity_source_organism.scientific_name",
                            "operator": "exact_match", "value": "Homo sapiens"}},
            {"type": "terminal", "service": "text",
             "parameters": {"attribute": "rcsb_entry_info.resolution_combined",
                            "operator": "less", "value": 2.5}},
        ],
    },
    "return_type": "entry",
    "request_options": {"paginate": {"rows": 50}},
}
r = requests.post(SEARCH, json=payload, timeout=30)
r.raise_for_status()
pdb_ids = [h["identifier"] for h in r.json()["result_set"]]
print(f"EGFR ≤2.5 Å human structures: {len(pdb_ids)}")

# Filter to entries with bound ligands via batch GraphQL
ids_str = ", ".join(f'"{p}"' for p in pdb_ids[:20])
gql = f"""{{ entries(entry_ids: [{ids_str}]) {{
    rcsb_id
    struct {{ title }}
    rcsb_entry_info {{ resolution_combined nonpolymer_entity_count }}
}} }}"""
r2 = requests.post(DATA, json={"query": gql}, timeout=60)
for e in r2.json()["data"]["entries"]:
    n_lig = e["rcsb_entry_info"]["nonpolymer_entity_count"] or 0
    if n_lig > 0:
        res = e["rcsb_entry_info"]["resolution_combined"]
        res_v = res[0] if isinstance(res, list) else res
        print(f"  {e['rcsb_id']}: {res_v} Å, ligands={n_lig}  — {e['struct']['title'][:60]}")
    time.sleep(0.05)

Workflow 2: Protein Family — Sequence-Similar Structures

Goal: Find all PDB structures with sequence similar to a query (KRAS), then summarize their resolution + experimental method.

python
import requests, time

SEARCH = "https://search.rcsb.org/rcsbsearch/v2/query"
DATA   = "https://data.rcsb.org/graphql"

kras_seq = ("MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVIDGETCLLDILDTAGQ"
            "EEYSAMRDQYMRTGEGFLCVFAINNTKSFEDIHHYREQIKRVKDSEDVPMVLVGNKCDLPS"
            "RTVDTKQAQDLARSYGIPFIETSAKTRQGVDDAFYTLVREIRKHKEKMSK")

payload = {
    "query": {
        "type": "terminal", "service": "sequence",
        "parameters": {"target": "pdb_protein_sequence", "value": kras_seq,
                       "evalue_cutoff": 1e-5, "identity_cutoff": 0.5},
    },
    "return_type": "polymer_entity",
    "request_options": {"paginate": {"rows": 20}},
}
r = requests.post(SEARCH, json=payload, timeout=30)
hits = r.json()["result_set"]
print(f"KRAS family hits: {len(hits)}")

# Unique PDB IDs from polymer_entity identifiers (e.g., "4OBE_1" -> "4OBE")
entry_ids = sorted({h["identifier"].split("_")[0] for h in hits})

# Batch metadata
ids_str = ", ".join(f'"{p}"' for p in entry_ids)
gql = f"""{{ entries(entry_ids: [{ids_str}]) {{
    rcsb_id
    struct {{ title }}
    exptl {{ method }}
    rcsb_entry_info {{ resolution_combined }}
}} }}"""
r2 = requests.post(DATA, json={"query": gql}, timeout=60)
for e in sorted(r2.json()["data"]["entries"],
                key=lambda x: (x["rcsb_entry_info"]["resolution_combined"] or [99])[0] if isinstance(x["rcsb_entry_info"]["resolution_combined"], list) else (x["rcsb_entry_info"]["resolution_combined"] or 99)):
    res = e["rcsb_entry_info"]["resolution_combined"]
    res_v = res[0] if isinstance(res, list) else res
    print(f"  {e['rcsb_id']}: {res_v} Å  {e['exptl'][0]['method']:<25} {e['struct']['title'][:50]}")

Workflow 3: Download + Parse with BioPython

Goal: Download mmCIF, then enumerate chains with BioPython.

python
import requests
from Bio.PDB import MMCIFParser

pdb_id = "4HHB"
r = requests.get(f"https://files.rcsb.org/download/{pdb_id}.cif", timeout=60)
r.raise_for_status()
with open(f"{pdb_id}.cif", "w") as f:
    f.write(r.text)

parser = MMCIFParser(QUIET=True)
structure = parser.get_structure(pdb_id, f"{pdb_id}.cif")
for model in structure:
    for chain in model:
        std_res = [r for r in chain if r.id[0] == " "]
        atoms = sum(len(list(r.get_atoms())) for r in std_res)
        print(f"Chain {chain.id}: {len(std_res)} residues, {atoms} atoms")

Key Parameters

| Parameter | Endpoint | Default | Range / Options | Effect | |-----------|----------|---------|-----------------|--------| | value | search sequence | required | protein/DNA/RNA sequence string | Query sequence for MMseqs2 | | evalue_cutoff | search sequence | 0.1 | 1e-10–10 | E-value threshold | | identity_cutoff | search sequence | 0.9 | 0.0–1.0 | Minimum identity fraction | | target | search sequence | "pdb_protein_sequence" | pdb_protein_sequence, pdb_dna_sequence, pdb_rna_sequence | Sequence type | | operator | search text (attribute) | required | see Attribute Operators | Comparison kind | | operator | search structure | strict_shape_match | strict_shape_match, relaxed_shape_match | 3D match stringency | | return_type | all search | entry | entry, polymer_entity, assembly, polymer_instance, mol_definition, … | Identifier granularity | | paginate.start / paginate.rows | request_options | 0 / 25 | up to ~10000 rows/page in practice | Pagination window | | GraphQL field entries(entry_ids: [...]) | data.rcsb.org/graphql | — | array of PDB IDs | Batch entry metadata |

Best Practices

1. Search → fetch: Use the Search API to get a list of IDs, then GraphQL entries(entry_ids: [...]) for batch metadata. Avoid one GraphQL request per ID.

1. Use full_text vs text deliberately: free-text keyword search needs "service": "full_text". Structured attribute filters need "service": "text". They are not interchangeable.

1. mmCIF over PDB format: PDB format is being phased out and has a 99,999 atom limit. Always download .cif for new code.

1. Set realistic paginate.rows: rows: 100 is a good default for batch work; the API may slow down beyond ~10000. Loop with paginate.start for full traversal.

1. Rate limit with time.sleep(0.2) in batch loops: No published hard cap, but the public infrastructure is shared. On HTTP 429, back off exponentially.

1. Inspect the payload before posting: print(json.dumps(payload, indent=2)) is the cheapest way to debug HTTP 400 errors.

1. entries(entry_ids: [...]) does not validate every ID: if one ID is wrong, the whole array returns null entries. Validate IDs separately if you can't trust the source.

Common Recipes

Recipe: Get FASTA for a PDB ID

python
import requests
r = requests.get("https://www.rcsb.org/fasta/entry/4HHB", timeout=30)
print(r.text)

Recipe: All chains in an entry

python
import requests
DATA = "https://data.rcsb.org/graphql"
gql = """{ entry(entry_id: "4HHB") {
  polymer_entities {
    rcsb_id
    rcsb_polymer_entity_container_identifiers { auth_asym_ids }
    entity_poly { rcsb_entity_polymer_type pdbx_seq_one_letter_code_can }
  }
} }"""
r = requests.post(DATA, json={"query": gql}, timeout=30)
for pe in r.json()["data"]["entry"]["polymer_entities"]:
    chains = pe["rcsb_polymer_entity_container_identifiers"]["auth_asym_ids"]
    seq    = pe["entity_poly"]["pdbx_seq_one_letter_code_can"][:50]
    print(f"  {pe['rcsb_id']} chains={chains}  type={pe['entity_poly']['rcsb_entity_polymer_type']}  seq={seq}…")

Recipe: List all ligands in an entry

python
import requests
DATA = "https://data.rcsb.org/graphql"
gql = """{ entry(entry_id: "1IEP") {
  nonpolymer_entities {
    rcsb_id
    nonpolymer_comp { chem_comp { id name formula } }
  }
} }"""
r = requests.post(DATA, json={"query": gql}, timeout=30)
for npe in r.json()["data"]["entry"]["nonpolymer_entities"]:
    cc = npe["nonpolymer_comp"]["chem_comp"]
    print(f"  {npe['rcsb_id']}: {cc['id']} ({cc['name']})  {cc['formula']}")

Recipe: Inspect available search attributes

The Search API exposes a JSON schema at https://search.rcsb.org/rcsbsearch/v2/metadata/schema. Use it to look up valid attribute paths.

python
import requests
r = requests.get("https://search.rcsb.org/rcsbsearch/v2/metadata/schema", timeout=30)
schema = r.json()
# Schema lists hundreds of attribute paths; sample a few
sample_paths = [k for k in schema if "resolution" in k.lower()][:5]
print(sample_paths)

Troubleshooting

| Problem | Cause | Solution | |---------|-------|----------| | HTTP 400 — Invalid request to the [ text ] service on a free-text query | Wrong service name | Use "service": "full_text" for keyword search; "service": "text" is for structured attribute filters | | HTTP 400 with cryptic schema message | Bad operator/value shape | Check the AttributeQuery Operators table; range needs the {from,to,include_lower,include_upper} dict | | Empty result_set | Filters too strict | Relax filters one at a time; verify attribute names via the schema endpoint | | HTTP 404 on entries(entry_ids: ["XYZW"]) | The entry doesn't exist | RCSB returns null rather than 404 inside the GraphQL response — check each data.entries[i] for null | | HTTP 429 Too Many Requests | Burst pace | Add time.sleep(0.3) between requests; exponential backoff on 429 | | HTTP 500 from search | Server-side glitch | Retry after 5–10 s; check status.rcsb.org | | Downloaded .pdb file truncated | >99,999 atoms (legacy format limit) | Download .cif instead | | GraphQL response has errors array | Field name typo or wrong root | Read the error message; the API is strict about field names — check the schema browser at <https://data.rcsb.org/index.html#graphql-api> |

Related Skills

• alphafold-database-access — AI-predicted structures; use when no experimental structure exists
• uniprot-protein-database — protein annotations, sequences, ID mapping (UniProt accession needed for AlphaFold)
• biopython-molecular-biology — parse downloaded PDB/mmCIF files, extract coordinates, compute distances
• autodock-vina-docking — downstream molecular docking using PDB structures as receptors
• rdkit-cheminformatics — analyze ligands extracted from PDB complexes

References

• RCSB PDB — main portal and web search
• Search API v2 docs — full JSON-payload reference and Swagger
• Data API GraphQL browser — schema explorer
• Search API attribute schema — JSON of every searchable attribute
• RCSB PDB Web APIs overview — index of all programmatic surfaces
• For SDK-based usage, see the rcsb-api PyPI package; this SKILL.md uses the underlying REST/GraphQL directly so no SDK install is needed.