UniProt — Protein Database

Overview

UniProt is the most comprehensive protein sequence and functional annotation database, containing 250M+ entries. This skill covers programmatic access via the UniProt REST API for protein search, sequence retrieval, ID mapping, and annotation queries. Swiss-Prot entries are manually curated; TrEMBL entries are computationally predicted.

When to Use

• Searching for proteins by gene name, accession, organism, or function keywords
• Retrieving protein sequences in FASTA format for downstream analysis
• Mapping identifiers between databases (UniProt ↔ Ensembl, PDB, RefSeq, KEGG)
• Accessing protein annotations: GO terms, domains, post-translational modifications
• Batch retrieving multiple protein entries for comparative analysis
• Downloading reviewed (Swiss-Prot) protein datasets for a specific organism
• For unified access to 40+ databases, use bioservices instead
• For protein 3D structures, use alphafold-database or pdb-database

Prerequisites

pip install requests pandas

API Rate Limits: UniProt REST API has no strict rate limit but recommends adding

time.sleep(0.5)

Quick Start

import requests

# Search for human insulin proteins (reviewed/Swiss-Prot only)
url = "https://rest.uniprot.org/uniprotkb/search"
params = {"query": "insulin AND organism_id:9606 AND reviewed:true", "format": "tsv",
          "fields": "accession,gene_names,protein_name,length"}
response = requests.get(url, params=params)
print(response.text[:500])
# accession  gene_names  protein_name  length
# P01308     INS         Insulin       110

Core API

1. Protein Search

Search UniProt with structured queries combining Boolean operators and field-specific filters.

import requests
import time

BASE = "https://rest.uniprot.org/uniprotkb/search"

def search_uniprot(query, fields=None, format="json", size=25):
    """Search UniProt with query syntax."""
    params = {"query": query, "format": format, "size": size}
    if fields:
        params["fields"] = ",".join(fields)
    resp = requests.get(BASE, params=params)
    resp.raise_for_status()
    return resp.json() if format == "json" else resp.text

# Search by gene name
results = search_uniprot("gene:BRCA1 AND reviewed:true",
                         fields=["accession", "gene_names", "organism_name", "length"])
for entry in results["results"][:3]:
    print(f"{entry['primaryAccession']} | {entry.get('genes', [{}])[0].get('geneName', {}).get('value', 'N/A')} | {entry.get('organism', {}).get('scientificName', 'N/A')}")

Query syntax reference:

# Boolean operators
kinase AND organism_id:9606          # Human kinases
(diabetes OR insulin) AND reviewed:true
cancer NOT lung

# Field-specific
gene:BRCA1
accession:P12345
taxonomy_name:"Homo sapiens"
go:0005515                           # GO term: protein binding

# Range queries
length:[100 TO 500]
mass:[50000 TO 100000]

# Wildcards
gene:BRCA*

2. Protein Entry Retrieval

Retrieve individual protein entries by accession number.

import requests

def get_protein(accession, format="json"):
    """Retrieve a single protein entry."""
    url = f"https://rest.uniprot.org/uniprotkb/{accession}"
    resp = requests.get(url, headers={"Accept": f"application/{format}"})
    resp.raise_for_status()
    return resp.json() if format == "json" else resp.text

# Get human insulin
entry = get_protein("P01308")
print(f"Protein: {entry['proteinDescription']['recommendedName']['fullName']['value']}")
print(f"Gene: {entry['genes'][0]['geneName']['value']}")
print(f"Length: {entry['sequence']['length']} aa")
print(f"Sequence: {entry['sequence']['value'][:50]}...")

# Get FASTA directly
fasta = requests.get("https://rest.uniprot.org/uniprotkb/P01308.fasta").text
print(fasta[:200])

3. ID Mapping

Map identifiers between UniProt and other databases.

import requests
import time

def map_ids(ids, from_db, to_db):
    """Map identifiers between databases (async job)."""
    # Submit job
    resp = requests.post("https://rest.uniprot.org/idmapping/run",
                         data={"from": from_db, "to": to_db, "ids": ",".join(ids)})
    resp.raise_for_status()
    job_id = resp.json()["jobId"]

    # Poll for completion
    while True:
        status = requests.get(f"https://rest.uniprot.org/idmapping/status/{job_id}").json()
        if "results" in status or "failedIds" in status:
            break
        time.sleep(1)

    # Get results
    results = requests.get(f"https://rest.uniprot.org/idmapping/results/{job_id}").json()
    return results

# UniProt → PDB mapping
results = map_ids(["P01308", "P12345"], from_db="UniProtKB_AC-ID", to_db="PDB")
for r in results.get("results", []):
    print(f"{r['from']} → PDB: {r['to']}")

# UniProt → Ensembl mapping
results = map_ids(["P01308"], from_db="UniProtKB_AC-ID", to_db="Ensembl")
for r in results.get("results", []):
    print(f"{r['from']} → Ensembl: {r['to']}")

Common database codes:

UniProtKB_AC-ID

Ensembl

RefSeq_Protein

PDB

Gene_Name

GeneID

KEGG

4. Batch Retrieval and Streaming

Retrieve large datasets efficiently.

import requests
import time

def batch_retrieve(accessions, fields=None, format="tsv"):
    """Retrieve multiple proteins by accession."""
    query = " OR ".join(f"accession:{acc}" for acc in accessions)
    params = {"query": query, "format": format}
    if fields:
        params["fields"] = ",".join(fields)
    resp = requests.get("https://rest.uniprot.org/uniprotkb/search", params=params)
    resp.raise_for_status()
    return resp.text

# Batch retrieve
accessions = ["P01308", "P12345", "Q9Y6K9"]
tsv = batch_retrieve(accessions, fields=["accession", "gene_names", "protein_name", "length"])
print(tsv)

# Streaming for large queries (no pagination needed)
def stream_query(query, format="fasta"):
    """Stream large result sets."""
    url = f"https://rest.uniprot.org/uniprotkb/stream?query={query}&format={format}"
    resp = requests.get(url, stream=True)
    resp.raise_for_status()
    for chunk in resp.iter_content(chunk_size=8192, decode_unicode=True):
        yield chunk

# Stream all human kinases as FASTA
# for chunk in stream_query("kinase AND organism_id:9606 AND reviewed:true"):
#     print(chunk[:200])

5. Pagination and Cursor-Based Iteration

Handle large result sets with pagination using the

Link

import requests

def paginate_search(query, fields=None, page_size=500):
    """Iterate all pages of a UniProt search using cursor pagination."""
    params = {"query": query, "format": "tsv", "size": page_size}
    if fields:
        params["fields"] = ",".join(fields)
    url = "https://rest.uniprot.org/uniprotkb/search"
    rows = []
    header = None
    while url:
        resp = requests.get(url, params=params)
        resp.raise_for_status()
        params = {}  # cursor is embedded in the next URL
        lines = resp.text.strip().split("\n")
        if header is None:
            header = lines[0]
        rows.extend(lines[1:])
        # Follow Link header for next page
        link = resp.headers.get("Link", "")
        url = link.split("<")[1].split(">")[0] if "<" in link else None
    return header, rows

header, rows = paginate_search(
    "kinase AND organism_id:9606 AND reviewed:true",
    fields=["accession", "gene_names", "length"]
)
print(f"Retrieved {len(rows)} proteins")
print(header)
print("\n".join(rows[:3]))

6. Field Selection and Annotations

Customize which data fields to retrieve.

import requests
import pandas as pd
from io import StringIO

# Retrieve specific annotation fields
params = {
    "query": "gene:TP53 AND organism_id:9606 AND reviewed:true",
    "format": "tsv",
    "fields": "accession,gene_names,protein_name,go_p,go_f,go_c,cc_function,ft_domain",
}
resp = requests.get("https://rest.uniprot.org/uniprotkb/search", params=params)
df = pd.read_csv(StringIO(resp.text), sep="\t")
print(df.columns.tolist())
print(df.iloc[0])

Common field groups:

• Sequence:

  accession

  ,

  sequence

  ,

  length

  ,

  mass

• Names:

  gene_names

  ,

  protein_name

  ,

  organism_name

• GO:

  go_p

  (process),

  go_f

  (function),

  go_c

  (component)
• Features:

  ft_domain

  ,

  ft_binding

  ,

  ft_act_site

  ,

  ft_mod_res

• Comments:

  cc_function

  ,

  cc_interaction

  ,

  cc_subcellular_location

Key Parameters

query

/search

/stream

format

json

json

tsv

fasta

xml

gff

fields

/search

size

/search

from

to

/idmapping/run

reviewed:true

true

false

organism_id

Best Practices

1. Filter

   reviewed:true

   for curated data: Swiss-Prot entries are manually reviewed; TrEMBL entries are computationally predicted. Use Swiss-Prot for high-confidence annotations.

2. Use TSV format with

   fields

   for tabular analysis: Requesting only needed fields as TSV is faster and easier to parse than full JSON entries.

3. Use streaming for large downloads: The

   /stream

   endpoint returns all results without pagination, avoiding the need for multi-page iteration.

4. Add

   time.sleep(0.5)

   between batch requests: Respect API resources, especially when making many sequential requests.

5. Cache frequently accessed entries locally: UniProt updates monthly; cache results and re-fetch only when needed.

6. Anti-pattern — querying without

   organism_id

   : Broad queries like

   gene:INS

   return thousands of entries across all species. Always filter by organism for targeted results.

Common Recipes

Recipe: Download All Human Kinases as DataFrame

import requests
import pandas as pd
from io import StringIO

url = "https://rest.uniprot.org/uniprotkb/stream"
params = {
    "query": "ec:2.7.* AND organism_id:9606 AND reviewed:true",
    "format": "tsv",
    "fields": "accession,gene_names,protein_name,length,go_f",
}
resp = requests.get(url, params=params)
df = pd.read_csv(StringIO(resp.text), sep="\t")
print(f"Human kinases (Swiss-Prot): {len(df)}")
print(df.head())

Recipe: Extract GO Annotations for a Gene Set

import requests
import pandas as pd
from io import StringIO

gene_list = ["BRCA1", "BRCA2", "TP53", "ATM", "CHEK2"]
query = " OR ".join(f"gene:{g}" for g in gene_list)
query += " AND organism_id:9606 AND reviewed:true"

params = {
    "query": query,
    "format": "tsv",
    "fields": "accession,gene_names,go_p,go_f,go_c",
}
resp = requests.get("https://rest.uniprot.org/uniprotkb/search", params=params)
df = pd.read_csv(StringIO(resp.text), sep="\t")
print(df[["Accession", "Gene Names", "Gene Ontology (biological process)"]].head())

Recipe: Cross-Reference UniProt to PDB Structures

import requests
import time

accessions = ["P53_HUMAN", "P01308", "P00533"]  # TP53, Insulin, EGFR
resp = requests.post("https://rest.uniprot.org/idmapping/run",
                     data={"from": "UniProtKB_AC-ID", "to": "PDB", "ids": ",".join(accessions)})
job_id = resp.json()["jobId"]
time.sleep(2)
results = requests.get(f"https://rest.uniprot.org/idmapping/results/{job_id}").json()
for r in results.get("results", []):
    print(f"{r['from']} → PDB: {r['to']}")

Troubleshooting

400 Bad Request

AND organism_id:9606 AND reviewed:true

from

to

UniProtKB_AC-ID

UniProtKB

/stream

/search

429 Too Many Requests

time.sleep(0.5)

reviewed:true

Related Skills

• biopython-molecular-biology — parse FASTA sequences returned by UniProt; run BLAST with retrieved sequences
• alphafold-database — retrieve predicted 3D structures using UniProt accessions
• esm-protein-language-model — generate embeddings from UniProt protein sequences
• gget-genomic-databases — alternative interface for quick gene/protein lookups across databases

References

• UniProt REST API documentation — official API reference
• UniProt query syntax — field-specific search operators
• UniProt Consortium (2023) — "UniProt: the Universal Protein Knowledgebase in 2023" — Nucleic Acids Research