18 KiB
18 KiB
Drug Targets and Pathways
Overview
DrugBank provides comprehensive information about drug-protein interactions including targets, enzymes, transporters, and carriers. This data is essential for understanding drug mechanisms, identifying repurposing opportunities, and predicting off-target effects.
Protein Interaction Categories
Target Proteins
Primary proteins that drugs bind to produce therapeutic effects:
- Receptors: G-protein coupled receptors, nuclear receptors, ion channels
- Enzymes: Kinases, proteases, phosphatases
- Transporters: Used as targets (not just for ADME)
- Other: Structural proteins, DNA/RNA
Metabolic Enzymes
Enzymes involved in drug metabolism:
- Cytochrome P450 enzymes: CYP3A4, CYP2D6, CYP2C9, etc.
- Phase II enzymes: UGTs, SULTs, GSTs
- Esterases and peptidases
Transporters
Proteins involved in drug transport across membranes:
- Uptake transporters: OATPs, OCTs
- Efflux transporters: P-glycoprotein, BCRP, MRPs
- Other: SLC and ABC transporter families
Carriers
Plasma proteins that bind and transport drugs:
- Albumin: Major drug carrier in blood
- Alpha-1-acid glycoprotein
- Lipoproteins
- Specific binding proteins: SHBG, CBG, etc.
XML Data Structure
Target Element Structure
<targets>
<target>
<id>BE0000001</id>
<name>Prothrombin</name>
<organism>Humans</organism>
<actions>
<action>inhibitor</action>
</actions>
<known-action>yes</known-action>
<polypeptide id="P00734" source="Swiss-Prot">
<name>Prothrombin</name>
<general-function>Serine-type endopeptidase activity</general-function>
<specific-function>Thrombin plays a role in...</specific-function>
<gene-name>F2</gene-name>
<organism>Homo sapiens</organism>
<external-identifiers>
<external-identifier>
<resource>UniProtKB</resource>
<identifier>P00734</identifier>
</external-identifier>
</external-identifiers>
<amino-acid-sequence>MAHVRGLQLP...</amino-acid-sequence>
<pfams>...</pfams>
<go-classifiers>...</go-classifiers>
</polypeptide>
</target>
</targets>
Extract Target Information
Get Drug Targets
from drugbank_downloader import get_drugbank_root
def get_drug_targets(drugbank_id):
"""Extract all targets for a drug"""
root = get_drugbank_root()
ns = {'db': 'http://www.drugbank.ca'}
for drug in root.findall('db:drug', ns):
primary_id = drug.find('db:drugbank-id[@primary="true"]', ns)
if primary_id is not None and primary_id.text == drugbank_id:
targets = []
targets_elem = drug.find('db:targets', ns)
if targets_elem is not None:
for target in targets_elem.findall('db:target', ns):
target_data = extract_target_details(target, ns)
targets.append(target_data)
return targets
return []
def extract_target_details(target, ns):
"""Extract detailed target information"""
target_data = {
'id': target.find('db:id', ns).text,
'name': target.find('db:name', ns).text,
'organism': target.find('db:organism', ns).text,
'known_action': target.find('db:known-action', ns).text,
}
# Extract actions
actions_elem = target.find('db:actions', ns)
if actions_elem is not None:
actions = [action.text for action in actions_elem.findall('db:action', ns)]
target_data['actions'] = actions
# Extract polypeptide info
polypeptide = target.find('db:polypeptide', ns)
if polypeptide is not None:
target_data['uniprot_id'] = polypeptide.get('id')
target_data['gene_name'] = get_text_safe(polypeptide.find('db:gene-name', ns))
target_data['general_function'] = get_text_safe(polypeptide.find('db:general-function', ns))
target_data['specific_function'] = get_text_safe(polypeptide.find('db:specific-function', ns))
return target_data
def get_text_safe(element):
return element.text if element is not None else None
Get All Protein Interactions
def get_all_protein_interactions(drugbank_id):
"""Get targets, enzymes, transporters, and carriers for a drug"""
root = get_drugbank_root()
ns = {'db': 'http://www.drugbank.ca'}
for drug in root.findall('db:drug', ns):
primary_id = drug.find('db:drugbank-id[@primary="true"]', ns)
if primary_id is not None and primary_id.text == drugbank_id:
interactions = {
'targets': extract_protein_list(drug.find('db:targets', ns), ns),
'enzymes': extract_protein_list(drug.find('db:enzymes', ns), ns),
'transporters': extract_protein_list(drug.find('db:transporters', ns), ns),
'carriers': extract_protein_list(drug.find('db:carriers', ns), ns),
}
return interactions
return None
def extract_protein_list(parent_elem, ns):
"""Extract list of proteins from parent element"""
if parent_elem is None:
return []
proteins = []
# Same structure for targets, enzymes, transporters, carriers
for protein_elem in parent_elem:
protein_data = extract_target_details(protein_elem, ns)
proteins.append(protein_data)
return proteins
# Usage
interactions = get_all_protein_interactions('DB00001')
print(f"Targets: {len(interactions['targets'])}")
print(f"Enzymes: {len(interactions['enzymes'])}")
print(f"Transporters: {len(interactions['transporters'])}")
print(f"Carriers: {len(interactions['carriers'])}")
Build Target-Drug Networks
Create Target-Drug Matrix
import pandas as pd
def build_drug_target_matrix():
"""Build matrix of drugs vs targets"""
root = get_drugbank_root()
ns = {'db': 'http://www.drugbank.ca'}
drug_target_pairs = []
for drug in root.findall('db:drug', ns):
drug_id = drug.find('db:drugbank-id[@primary="true"]', ns).text
drug_name = drug.find('db:name', ns).text
targets_elem = drug.find('db:targets', ns)
if targets_elem is not None:
for target in targets_elem.findall('db:target', ns):
target_id = target.find('db:id', ns).text
target_name = target.find('db:name', ns).text
# Get UniProt ID if available
polypeptide = target.find('db:polypeptide', ns)
uniprot_id = polypeptide.get('id') if polypeptide is not None else None
drug_target_pairs.append({
'drug_id': drug_id,
'drug_name': drug_name,
'target_id': target_id,
'target_name': target_name,
'uniprot_id': uniprot_id
})
df = pd.DataFrame(drug_target_pairs)
return df
# Usage
dt_matrix = build_drug_target_matrix()
dt_matrix.to_csv('drug_target_matrix.csv', index=False)
Find Drugs Targeting Specific Protein
def find_drugs_for_target(target_name):
"""Find all drugs that target a specific protein"""
root = get_drugbank_root()
ns = {'db': 'http://www.drugbank.ca'}
drugs_for_target = []
target_name_lower = target_name.lower()
for drug in root.findall('db:drug', ns):
drug_id = drug.find('db:drugbank-id[@primary="true"]', ns).text
drug_name = drug.find('db:name', ns).text
targets_elem = drug.find('db:targets', ns)
if targets_elem is not None:
for target in targets_elem.findall('db:target', ns):
tgt_name = target.find('db:name', ns).text
if target_name_lower in tgt_name.lower():
drugs_for_target.append({
'drug_id': drug_id,
'drug_name': drug_name,
'target_name': tgt_name
})
break # Found match, move to next drug
return drugs_for_target
# Example: Find drugs targeting kinases
kinase_drugs = find_drugs_for_target('kinase')
print(f"Found {len(kinase_drugs)} drugs targeting kinases")
Find Drugs with Shared Targets
def find_shared_targets(drug1_id, drug2_id):
"""Find common targets between two drugs"""
targets1 = get_drug_targets(drug1_id)
targets2 = get_drug_targets(drug2_id)
# Compare by UniProt ID if available, otherwise by name
targets1_ids = set()
for t in targets1:
if t.get('uniprot_id'):
targets1_ids.add(t['uniprot_id'])
else:
targets1_ids.add(t['name'])
targets2_ids = set()
for t in targets2:
if t.get('uniprot_id'):
targets2_ids.add(t['uniprot_id'])
else:
targets2_ids.add(t['name'])
shared = targets1_ids.intersection(targets2_ids)
return list(shared)
# Usage for drug repurposing
shared = find_shared_targets('DB00001', 'DB00002')
print(f"Shared targets: {shared}")
Pathway Analysis
Extract Pathway Information
def get_drug_pathways(drugbank_id):
"""Extract pathway information for a drug"""
root = get_drugbank_root()
ns = {'db': 'http://www.drugbank.ca'}
for drug in root.findall('db:drug', ns):
primary_id = drug.find('db:drugbank-id[@primary="true"]', ns)
if primary_id is not None and primary_id.text == drugbank_id:
pathways = []
pathways_elem = drug.find('db:pathways', ns)
if pathways_elem is not None:
for pathway in pathways_elem.findall('db:pathway', ns):
pathway_data = {
'smpdb_id': pathway.find('db:smpdb-id', ns).text,
'name': pathway.find('db:name', ns).text,
'category': pathway.find('db:category', ns).text,
}
# Extract drugs in pathway
drugs_elem = pathway.find('db:drugs', ns)
if drugs_elem is not None:
pathway_drugs = []
for drug_elem in drugs_elem.findall('db:drug', ns):
pathway_drugs.append(drug_elem.find('db:drugbank-id', ns).text)
pathway_data['drugs'] = pathway_drugs
# Extract enzymes in pathway
enzymes_elem = pathway.find('db:enzymes', ns)
if enzymes_elem is not None:
pathway_enzymes = []
for enzyme in enzymes_elem.findall('db:uniprot-id', ns):
pathway_enzymes.append(enzyme.text)
pathway_data['enzymes'] = pathway_enzymes
pathways.append(pathway_data)
return pathways
return []
Build Pathway Network
def build_pathway_drug_network():
"""Build network of pathways and drugs"""
root = get_drugbank_root()
ns = {'db': 'http://www.drugbank.ca'}
pathway_network = {}
for drug in root.findall('db:drug', ns):
drug_id = drug.find('db:drugbank-id[@primary="true"]', ns).text
pathways_elem = drug.find('db:pathways', ns)
if pathways_elem is not None:
for pathway in pathways_elem.findall('db:pathway', ns):
pathway_id = pathway.find('db:smpdb-id', ns).text
pathway_name = pathway.find('db:name', ns).text
if pathway_id not in pathway_network:
pathway_network[pathway_id] = {
'name': pathway_name,
'drugs': []
}
pathway_network[pathway_id]['drugs'].append(drug_id)
return pathway_network
Target-Based Drug Repurposing
Find Drugs with Similar Target Profiles
def find_similar_target_profiles(drugbank_id, min_shared_targets=2):
"""Find drugs with similar target profiles for repurposing"""
reference_targets = get_drug_targets(drugbank_id)
reference_target_ids = set(t.get('uniprot_id') or t['name'] for t in reference_targets)
root = get_drugbank_root()
ns = {'db': 'http://www.drugbank.ca'}
similar_drugs = []
for drug in root.findall('db:drug', ns):
drug_id = drug.find('db:drugbank-id[@primary="true"]', ns).text
if drug_id == drugbank_id:
continue
drug_targets = get_drug_targets(drug_id)
drug_target_ids = set(t.get('uniprot_id') or t['name'] for t in drug_targets)
shared = reference_target_ids.intersection(drug_target_ids)
if len(shared) >= min_shared_targets:
drug_name = drug.find('db:name', ns).text
indication = get_text_safe(drug.find('db:indication', ns))
similar_drugs.append({
'drug_id': drug_id,
'drug_name': drug_name,
'shared_targets': len(shared),
'total_targets': len(drug_target_ids),
'overlap_ratio': len(shared) / len(drug_target_ids) if drug_target_ids else 0,
'indication': indication,
'shared_target_names': list(shared)
})
# Sort by overlap ratio
similar_drugs.sort(key=lambda x: x['overlap_ratio'], reverse=True)
return similar_drugs
# Example: Find repurposing candidates
candidates = find_similar_target_profiles('DB00001', min_shared_targets=2)
for drug in candidates[:5]:
print(f"{drug['drug_name']}: {drug['shared_targets']} shared targets")
Polypharmacology Analysis
def analyze_polypharmacology(drugbank_id):
"""Analyze on-target and off-target effects"""
targets = get_drug_targets(drugbank_id)
analysis = {
'total_targets': len(targets),
'known_action_targets': [],
'unknown_action_targets': [],
'target_classes': {},
'organisms': {}
}
for target in targets:
if target.get('known_action') == 'yes':
analysis['known_action_targets'].append(target)
else:
analysis['unknown_action_targets'].append(target)
# Count by organism
organism = target.get('organism', 'Unknown')
analysis['organisms'][organism] = analysis['organisms'].get(organism, 0) + 1
return analysis
# Usage
poly_analysis = analyze_polypharmacology('DB00001')
print(f"Total targets: {poly_analysis['total_targets']}")
print(f"Known action: {len(poly_analysis['known_action_targets'])}")
print(f"Unknown action: {len(poly_analysis['unknown_action_targets'])}")
Enzyme and Transporter Analysis
CYP450 Interaction Analysis
def analyze_cyp450_metabolism(drugbank_id):
"""Analyze CYP450 enzyme involvement"""
interactions = get_all_protein_interactions(drugbank_id)
enzymes = interactions['enzymes']
cyp_enzymes = []
for enzyme in enzymes:
gene_name = enzyme.get('gene_name', '')
if gene_name and gene_name.startswith('CYP'):
cyp_enzymes.append({
'gene': gene_name,
'name': enzyme['name'],
'actions': enzyme.get('actions', [])
})
return cyp_enzymes
# Check CYP involvement
cyp_data = analyze_cyp450_metabolism('DB00001')
for cyp in cyp_data:
print(f"{cyp['gene']}: {cyp['actions']}")
Transporter Substrate Analysis
def analyze_transporter_substrates(drugbank_id):
"""Identify transporter involvement for ADME"""
interactions = get_all_protein_interactions(drugbank_id)
transporters = interactions['transporters']
transporter_info = {
'efflux': [],
'uptake': [],
'other': []
}
for transporter in transporters:
name = transporter['name'].lower()
gene = transporter.get('gene_name', '').upper()
if 'p-glycoprotein' in name or gene == 'ABCB1':
transporter_info['efflux'].append(transporter)
elif 'oatp' in name or 'slco' in gene.lower():
transporter_info['uptake'].append(transporter)
else:
transporter_info['other'].append(transporter)
return transporter_info
GO Term and Protein Function Analysis
Extract GO Terms
def get_target_go_terms(drugbank_id):
"""Extract Gene Ontology terms for drug targets"""
root = get_drugbank_root()
ns = {'db': 'http://www.drugbank.ca'}
for drug in root.findall('db:drug', ns):
primary_id = drug.find('db:drugbank-id[@primary="true"]', ns)
if primary_id is not None and primary_id.text == drugbank_id:
go_terms = []
targets_elem = drug.find('db:targets', ns)
if targets_elem is not None:
for target in targets_elem.findall('db:target', ns):
polypeptide = target.find('db:polypeptide', ns)
if polypeptide is not None:
go_classifiers = polypeptide.find('db:go-classifiers', ns)
if go_classifiers is not None:
for go_class in go_classifiers.findall('db:go-classifier', ns):
go_term = {
'category': go_class.find('db:category', ns).text,
'description': go_class.find('db:description', ns).text,
}
go_terms.append(go_term)
return go_terms
return []
Best Practices
- UniProt Cross-Reference: Use UniProt IDs for accurate protein matching across databases
- Action Classification: Pay attention to action types (inhibitor, agonist, antagonist, etc.)
- Known vs Unknown: Distinguish between validated targets and predicted/unknown interactions
- Organism Specificity: Consider organism when analyzing target data
- Polypharmacology: Account for multiple targets when predicting drug effects
- Pathway Context: Use pathway data to understand systemic effects
- CYP450 Profiling: Essential for predicting drug-drug interactions
- Transporter Analysis: Critical for understanding bioavailability and tissue distribution