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