Version Compatibility

Reference examples tested with: BioPython 1.83+, pandas 2.2+

Before using code patterns, verify installed versions match. If versions differ:

• Python:

  pip show <package>

  then

  help(module.function)

  to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

UniProt Access

Query UniProt for protein sequences, functional annotations, and cross-references.

"Get protein information from UniProt" → Fetch sequences, GO terms, domains, and cross-references for a protein accession.

• Python:

  requests.get(f'https://rest.uniprot.org/uniprotkb/{acc}.json')

  (UniProt REST API)
• Python:

  ExPASy.get_sprot_raw(acc)

  +

  SwissProt.read()

  (BioPython)

UniProt REST API

Fetch Single Entry

import requests

def fetch_uniprot(accession, format='fasta'):
    '''Fetch UniProt entry. Formats: fasta, json, txt, xml, gff'''
    url = f'https://rest.uniprot.org/uniprotkb/{accession}.{format}'
    response = requests.get(url)
    response.raise_for_status()
    return response.text

sequence = fetch_uniprot('P53_HUMAN', 'fasta')
entry_json = fetch_uniprot('P04637', 'json')

Search UniProt

Goal: Find UniProt protein entries matching gene name, organism, or functional criteria.

Approach: Query the UniProt REST search endpoint with structured query syntax and parse the JSON results for accessions and protein descriptions.

def search_uniprot(query, format='json', size=25):
    '''Search UniProt with query syntax'''
    url = 'https://rest.uniprot.org/uniprotkb/search'
    params = {'query': query, 'format': format, 'size': size}
    response = requests.get(url, params=params)
    response.raise_for_status()
    return response.json() if format == 'json' else response.text

results = search_uniprot('gene:BRCA1 AND organism_id:9606')
for entry in results['results']:
    print(entry['primaryAccession'], entry['proteinDescription']['recommendedName']['fullName']['value'])

Query Syntax

gene:TP53

organism_id:9606

reviewed:true

length:[100 TO 500]

go:0006915

keyword:kinase

ec:2.7.1.1

database:pdb

Combine Queries

# Human kinases with structures
query = 'organism_id:9606 AND keyword:kinase AND database:pdb AND reviewed:true'
results = search_uniprot(query, size=100)

Batch Retrieval

Multiple Accessions

def batch_fetch(accessions, format='fasta'):
    '''Fetch multiple entries'''
    url = 'https://rest.uniprot.org/uniprotkb/accessions'
    params = {'accessions': ','.join(accessions), 'format': format}
    response = requests.get(url, params=params)
    return response.text

accessions = ['P04637', 'P53_HUMAN', 'Q9Y6K9']
sequences = batch_fetch(accessions)

Stream Large Results

def search_all(query, format='tsv', fields=None):
    '''Stream all results for large queries'''
    url = 'https://rest.uniprot.org/uniprotkb/stream'
    params = {'query': query, 'format': format}
    if fields:
        params['fields'] = ','.join(fields)
    response = requests.get(url, params=params, stream=True)
    return response.text

# Get all human proteins as TSV
all_human = search_all('organism_id:9606 AND reviewed:true',
                       fields=['accession', 'gene_names', 'protein_name'])

ID Mapping

Map Between Databases

Goal: Convert identifiers between databases (e.g., Ensembl gene IDs to UniProt accessions) in batch.

Approach: Submit an asynchronous ID mapping job to the UniProt API, poll for completion, then retrieve the mapped results.

import time

def map_ids(ids, from_db, to_db):
    '''Map IDs between databases'''
    url = 'https://rest.uniprot.org/idmapping/run'
    response = requests.post(url, data={'ids': ','.join(ids), 'from': from_db, 'to': to_db})
    job_id = response.json()['jobId']

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

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

# Ensembl gene IDs to UniProt
mapping = map_ids(['ENSG00000141510', 'ENSG00000171862'], 'Ensembl', 'UniProtKB')
for result in mapping['results']:
    print(result['from'], '->', result['to']['primaryAccession'])

Common Database Codes

UniProtKB

UniProtKB_AC-ID

Ensembl

RefSeq_Protein

PDB

GeneID

Gene_Name

Extract Specific Data

Parse JSON Entry

Goal: Extract structured annotations (GO terms, domains, PDB structures) from a UniProt JSON entry.

Approach: Fetch the entry in JSON format and navigate the nested structure to pull accession, gene name, sequence, and cross-reference lists filtered by database type.

import json

entry = json.loads(fetch_uniprot('P04637', 'json'))

accession = entry['primaryAccession']
gene_name = entry['genes'][0]['geneName']['value']
protein_name = entry['proteinDescription']['recommendedName']['fullName']['value']
sequence = entry['sequence']['value']
length = entry['sequence']['length']

# GO terms
go_terms = [ref for ref in entry.get('uniProtKBCrossReferences', [])
            if ref['database'] == 'GO']

# Domains (InterPro)
domains = [ref for ref in entry.get('uniProtKBCrossReferences', [])
           if ref['database'] == 'InterPro']

# PDB structures
pdb_refs = [ref for ref in entry.get('uniProtKBCrossReferences', [])
            if ref['database'] == 'PDB']

Get Specific Fields (TSV)

def get_fields(query, fields):
    '''Get specific fields as DataFrame'''
    import pandas as pd
    from io import StringIO

    url = 'https://rest.uniprot.org/uniprotkb/search'
    params = {'query': query, 'format': 'tsv', 'fields': ','.join(fields), 'size': 500}
    response = requests.get(url, params=params)
    return pd.read_csv(StringIO(response.text), sep='\t')

df = get_fields('organism_id:9606 AND keyword:kinase AND reviewed:true',
                ['accession', 'gene_names', 'protein_name', 'length', 'go_p'])

Available Fields

accession

gene_names

protein_name

organism_name

length

mass

go_p

go_c

go_f

xref_pdb

ft_domain

ft_binding

Biopython Integration

from Bio import SeqIO
from io import StringIO

fasta_text = fetch_uniprot('P04637', 'fasta')
record = SeqIO.read(StringIO(fasta_text), 'fasta')
print(record.id, len(record.seq))

Related Skills

• database-access/entrez-fetch - NCBI protein access
• database-access/blast-searches - BLAST against UniProt
• structural-biology/structure-io - Download PDB structures
• structural-biology/alphafold-predictions - AlphaFold structures