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Add BRENDA database skill for enzyme research and analysis
- Add comprehensive BRENDA database skill with API integration
- Include enzyme data retrieval, pathway analysis, and visualization
- Support for enzyme queries, kinetic parameters, and taxonomy data
- Add visualization scripts for enzyme pathways and kinetics
This commit is contained in:
Jinxiang Xie
844
scientific-skills/brenda-database/scripts/brenda_queries.py
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844
scientific-skills/brenda-database/scripts/brenda_queries.py
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"""
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BRENDA Database Query Utilities
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This module provides high-level functions for querying and analyzing
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enzyme data from the BRENDA database using the SOAP API.
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Key features:
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- Parse BRENDA response data entries
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- Search for enzymes by substrate/product
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- Compare enzyme properties across organisms
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- Retrieve kinetic parameters and environmental conditions
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- Analyze substrate specificity and inhibition
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- Support for enzyme engineering and pathway design
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- Export data in various formats
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Installation:
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uv pip install zeep requests pandas
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Usage:
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from scripts.brenda_queries import search_enzymes_by_substrate, compare_across_organisms
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enzymes = search_enzymes_by_substrate("glucose", limit=20)
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comparison = compare_across_organisms("1.1.1.1", ["E. coli", "S. cerevisiae"])
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"""
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import re
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import time
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import json
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import csv
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from typing import List, Dict, Any, Optional, Tuple
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from pathlib import Path
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try:
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from zeep import Client, Settings
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from zeep.exceptions import Fault, TransportError
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ZEEP_AVAILABLE = True
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except ImportError:
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print("Warning: zeep not installed. Install with: uv pip install zeep")
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ZEEP_AVAILABLE = False
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try:
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import requests
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REQUESTS_AVAILABLE = True
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except ImportError:
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print("Warning: requests not installed. Install with: uv pip install requests")
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REQUESTS_AVAILABLE = False
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try:
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import pandas as pd
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PANDAS_AVAILABLE = True
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except ImportError:
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print("Warning: pandas not installed. Install with: uv pip install pandas")
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PANDAS_AVAILABLE = False
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# Import the brenda_client from the project root
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import sys
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sys.path.append(str(Path(__file__).parent.parent.parent.parent))
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try:
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from brenda_client import get_km_values, get_reactions, call_brenda
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BRENDA_CLIENT_AVAILABLE = True
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except ImportError:
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print("Warning: brenda_client not available")
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BRENDA_CLIENT_AVAILABLE = False
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def validate_dependencies():
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"""Validate that required dependencies are installed."""
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missing = []
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if not ZEEP_AVAILABLE:
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missing.append("zeep")
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if not REQUESTS_AVAILABLE:
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missing.append("requests")
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if not BRENDA_CLIENT_AVAILABLE:
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missing.append("brenda_client")
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if missing:
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raise ImportError(f"Missing required dependencies: {', '.join(missing)}")
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def parse_km_entry(entry: str) -> Dict[str, Any]:
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"""Parse a BRENDA Km value entry into structured data."""
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if not entry or not isinstance(entry, str):
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return {}
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parsed = {}
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parts = entry.split('#')
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for part in parts:
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if '*' in part:
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key, value = part.split('*', 1)
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parsed[key.strip()] = value.strip()
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# Extract numeric values from kmValue
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if 'kmValue' in parsed:
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km_value = parsed['kmValue']
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# Extract first numeric value (in mM typically)
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numeric_match = re.search(r'(\d+\.?\d*)', km_value)
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if numeric_match:
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parsed['km_value_numeric'] = float(numeric_match.group(1))
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# Extract pH from commentary
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if 'commentary' in parsed:
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commentary = parsed['commentary']
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ph_match = re.search(r'pH\s*([0-9.]+)', commentary)
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if ph_match:
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parsed['ph'] = float(ph_match.group(1))
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temp_match = re.search(r'(\d+)\s*°?C', commentary)
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if temp_match:
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parsed['temperature'] = float(temp_match.group(1))
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return parsed
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def parse_reaction_entry(entry: str) -> Dict[str, Any]:
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"""Parse a BRENDA reaction entry into structured data."""
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if not entry or not isinstance(entry, str):
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return {}
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parsed = {}
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parts = entry.split('#')
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for part in parts:
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if '*' in part:
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key, value = part.split('*', 1)
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parsed[key.strip()] = value.strip()
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# Parse reaction equation
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if 'reaction' in parsed:
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reaction = parsed['reaction']
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# Extract reactants and products
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if '<=>' in reaction:
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reactants, products = reaction.split('<=>', 1)
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elif '->' in reaction:
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reactants, products = reaction.split('->', 1)
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elif '=' in reaction:
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reactants, products = reaction.split('=', 1)
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else:
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reactants, products = reaction, ''
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parsed['reactants'] = [r.strip() for r in reactants.split('+')]
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parsed['products'] = [p.strip() for p in products.split('+')]
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return parsed
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def extract_organism_data(entry: str) -> Dict[str, Any]:
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"""Extract organism-specific information from BRENDA entry."""
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parsed = parse_km_entry(entry) if 'kmValue' in entry else parse_reaction_entry(entry)
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if 'organism' in parsed:
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return {
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'organism': parsed['organism'],
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'ec_number': parsed.get('ecNumber', ''),
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'substrate': parsed.get('substrate', ''),
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'km_value': parsed.get('kmValue', ''),
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'km_numeric': parsed.get('km_value_numeric', None),
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'ph': parsed.get('ph', None),
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'temperature': parsed.get('temperature', None),
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'commentary': parsed.get('commentary', ''),
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'literature': parsed.get('literature', '')
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}
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return {}
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def search_enzymes_by_substrate(substrate: str, limit: int = 50) -> List[Dict[str, Any]]:
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"""Search for enzymes that act on a specific substrate."""
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validate_dependencies()
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enzymes = []
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# Search for Km values with the substrate
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try:
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km_data = get_km_values("*", substrate=substrate)
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time.sleep(0.5) # Rate limiting
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for entry in km_data[:limit]:
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parsed = parse_km_entry(entry)
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if parsed:
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enzymes.append({
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'ec_number': parsed.get('ecNumber', ''),
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'organism': parsed.get('organism', ''),
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'substrate': parsed.get('substrate', ''),
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'km_value': parsed.get('kmValue', ''),
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'km_numeric': parsed.get('km_value_numeric', None),
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'commentary': parsed.get('commentary', '')
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})
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except Exception as e:
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print(f"Error searching enzymes by substrate: {e}")
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# Remove duplicates based on EC number and organism
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unique_enzymes = []
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seen = set()
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for enzyme in enzymes:
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key = (enzyme['ec_number'], enzyme['organism'])
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if key not in seen:
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seen.add(key)
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unique_enzymes.append(enzyme)
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return unique_enzymes[:limit]
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def search_enzymes_by_product(product: str, limit: int = 50) -> List[Dict[str, Any]]:
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"""Search for enzymes that produce a specific product."""
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validate_dependencies()
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enzymes = []
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# Search for reactions containing the product
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try:
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# This is a simplified approach - in practice you might need
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# more sophisticated pattern matching for products
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reactions = get_reactions("*", reaction=f"*{product}*")
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time.sleep(0.5) # Rate limiting
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for entry in reactions[:limit]:
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parsed = parse_reaction_entry(entry)
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if parsed and 'products' in parsed:
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# Check if our target product is in the products list
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if any(product.lower() in prod.lower() for prod in parsed['products']):
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enzymes.append({
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'ec_number': parsed.get('ecNumber', ''),
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'organism': parsed.get('organism', ''),
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'reaction': parsed.get('reaction', ''),
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'reactants': parsed.get('reactants', []),
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'products': parsed.get('products', []),
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'commentary': parsed.get('commentary', '')
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})
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except Exception as e:
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print(f"Error searching enzymes by product: {e}")
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return enzymes[:limit]
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def compare_across_organisms(ec_number: str, organisms: List[str]) -> List[Dict[str, Any]]:
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"""Compare enzyme properties across different organisms."""
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validate_dependencies()
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comparison = []
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for organism in organisms:
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try:
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# Get Km data for this organism
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km_data = get_km_values(ec_number, organism=organism)
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time.sleep(0.5) # Rate limiting
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if km_data:
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# Calculate statistics
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numeric_kms = []
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phs = []
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temperatures = []
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for entry in km_data:
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parsed = parse_km_entry(entry)
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if 'km_value_numeric' in parsed:
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numeric_kms.append(parsed['km_value_numeric'])
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if 'ph' in parsed:
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phs.append(parsed['ph'])
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if 'temperature' in parsed:
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temperatures.append(parsed['temperature'])
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org_data = {
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'organism': organism,
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'ec_number': ec_number,
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'data_points': len(km_data),
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'average_km': sum(numeric_kms) / len(numeric_kms) if numeric_kms else None,
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'min_km': min(numeric_kms) if numeric_kms else None,
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'max_km': max(numeric_kms) if numeric_kms else None,
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'optimal_ph': sum(phs) / len(phs) if phs else None,
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'optimal_temperature': sum(temperatures) / len(temperatures) if temperatures else None,
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'temperature_range': (min(temperatures), max(temperatures)) if temperatures else None
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}
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comparison.append(org_data)
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else:
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comparison.append({
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'organism': organism,
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'ec_number': ec_number,
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'data_points': 0,
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'note': 'No data found'
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})
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except Exception as e:
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print(f"Error comparing organism {organism}: {e}")
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comparison.append({
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'organism': organism,
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'ec_number': ec_number,
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'error': str(e)
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})
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return comparison
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def get_organisms_for_enzyme(ec_number: str) -> List[str]:
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"""Get list of organisms that have data for a specific enzyme."""
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validate_dependencies()
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try:
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km_data = get_km_values(ec_number)
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time.sleep(0.5) # Rate limiting
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organisms = set()
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for entry in km_data:
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parsed = parse_km_entry(entry)
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if 'organism' in parsed:
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organisms.add(parsed['organism'])
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return sorted(list(organisms))
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except Exception as e:
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print(f"Error getting organisms for enzyme {ec_number}: {e}")
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return []
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def get_environmental_parameters(ec_number: str) -> Dict[str, Any]:
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"""Get environmental parameters (pH, temperature) for an enzyme."""
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validate_dependencies()
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try:
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km_data = get_km_values(ec_number)
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time.sleep(0.5) # Rate limiting
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phs = []
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temperatures = []
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ph_stabilities = []
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temp_stabilities = []
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for entry in km_data:
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parsed = parse_km_entry(entry)
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if 'ph' in parsed:
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phs.append(parsed['ph'])
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if 'temperature' in parsed:
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temperatures.append(parsed['temperature'])
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# Check commentary for stability information
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commentary = parsed.get('commentary', '').lower()
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if 'stable' in commentary and 'ph' in commentary:
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# Extract pH stability range
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ph_range_match = re.search(r'ph\s*([\d.]+)\s*[-–]\s*([\d.]+)', commentary)
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if ph_range_match:
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ph_stabilities.append((float(ph_range_match.group(1)), float(ph_range_match.group(2))))
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if 'stable' in commentary and ('temp' in commentary or '°c' in commentary):
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# Extract temperature stability
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temp_match = re.search(r'(\d+)\s*[-–]\s*(\d+)\s*°?c', commentary)
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if temp_match:
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temp_stabilities.append((int(temp_match.group(1)), int(temp_match.group(2))))
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params = {
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'ec_number': ec_number,
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'data_points': len(km_data),
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'ph_range': (min(phs), max(phs)) if phs else None,
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'optimal_ph': sum(phs) / len(phs) if phs else None,
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'optimal_temperature': sum(temperatures) / len(temperatures) if temperatures else None,
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'temperature_range': (min(temperatures), max(temperatures)) if temperatures else None,
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'stability_ph': ph_stabilities[0] if ph_stabilities else None,
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'temperature_stability': temp_stabilities[0] if temp_stabilities else None
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}
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return params
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except Exception as e:
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print(f"Error getting environmental parameters for {ec_number}: {e}")
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return {'ec_number': ec_number, 'error': str(e)}
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def get_cofactor_requirements(ec_number: str) -> List[Dict[str, Any]]:
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"""Get cofactor requirements for an enzyme from reaction data."""
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validate_dependencies()
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cofactors = []
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try:
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reactions = get_reactions(ec_number)
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time.sleep(0.5) # Rate limiting
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for entry in reactions:
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parsed = parse_reaction_entry(entry)
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if parsed and 'reactants' in parsed:
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# Look for common cofactors in reactants
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common_cofactors = [
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'NAD+', 'NADH', 'NADP+', 'NADPH',
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'ATP', 'ADP', 'AMP',
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'FAD', 'FADH2',
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'CoA', 'acetyl-CoA',
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'pyridoxal phosphate', 'PLP',
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'biotin',
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'heme', 'iron-sulfur'
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]
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for reactant in parsed['reactants']:
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for cofactor in common_cofactors:
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if cofactor.lower() in reactant.lower():
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cofactors.append({
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'name': cofactor,
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'full_name': reactant,
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'type': 'oxidoreductase' if 'NAD' in cofactor else 'other',
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'organism': parsed.get('organism', ''),
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'ec_number': ec_number
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})
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except Exception as e:
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print(f"Error getting cofactor requirements for {ec_number}: {e}")
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# Remove duplicates
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unique_cofactors = []
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seen = set()
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for cofactor in cofactors:
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key = (cofactor['name'], cofactor['organism'])
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if key not in seen:
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seen.add(key)
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unique_cofactors.append(cofactor)
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return unique_cofactors
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def get_substrate_specificity(ec_number: str) -> List[Dict[str, Any]]:
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"""Get substrate specificity data for an enzyme."""
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validate_dependencies()
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specificity = []
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try:
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km_data = get_km_values(ec_number)
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time.sleep(0.5) # Rate limiting
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substrate_data = {}
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for entry in km_data:
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parsed = parse_km_entry(entry)
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if 'substrate' in parsed and 'km_value_numeric' in parsed:
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substrate = parsed['substrate']
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if substrate not in substrate_data:
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substrate_data[substrate] = {
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'name': substrate,
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'km_values': [],
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'organisms': set(),
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'vmax_values': [], # If available
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'kcat_values': [] # If available
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}
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substrate_data[substrate]['km_values'].append(parsed['km_value_numeric'])
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if 'organism' in parsed:
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substrate_data[substrate]['organisms'].add(parsed['organism'])
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# Calculate summary statistics
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for substrate, data in substrate_data.items():
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if data['km_values']:
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specificity.append({
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'name': substrate,
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'km': sum(data['km_values']) / len(data['km_values']),
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'min_km': min(data['km_values']),
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'max_km': max(data['km_values']),
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'data_points': len(data['km_values']),
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'organisms': list(data['organisms']),
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'vmax': sum(data['vmax_values']) / len(data['vmax_values']) if data['vmax_values'] else None,
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'kcat': sum(data['kcat_values']) / len(data['kcat_values']) if data['kcat_values'] else None,
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'kcat_km_ratio': None # Would need kcat data to calculate
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})
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# Sort by Km (lower is better affinity)
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specificity.sort(key=lambda x: x['km'] if x['km'] else float('inf'))
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except Exception as e:
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print(f"Error getting substrate specificity for {ec_number}: {e}")
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return specificity
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def compare_substrate_affinity(ec_number: str) -> List[Dict[str, Any]]:
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"""Compare substrate affinity for an enzyme."""
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return get_substrate_specificity(ec_number)
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def get_inhibitors(ec_number: str) -> List[Dict[str, Any]]:
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"""Get inhibitor information for an enzyme (from commentary)."""
|
||||
validate_dependencies()
|
||||
|
||||
inhibitors = []
|
||||
|
||||
try:
|
||||
km_data = get_km_values(ec_number)
|
||||
time.sleep(0.5) # Rate limiting
|
||||
|
||||
for entry in km_data:
|
||||
parsed = parse_km_entry(entry)
|
||||
commentary = parsed.get('commentary', '').lower()
|
||||
|
||||
# Look for inhibitor keywords
|
||||
inhibitor_keywords = ['inhibited', 'inhibition', 'blocked', 'prevented', 'reduced']
|
||||
if any(keyword in commentary for keyword in inhibitor_keywords):
|
||||
# Try to extract inhibitor names (this is approximate)
|
||||
# Common inhibitors
|
||||
common_inhibitors = [
|
||||
'iodoacetate', 'n-ethylmaleimide', 'p-chloromercuribenzoate',
|
||||
'heavy metals', 'mercury', 'copper', 'zinc',
|
||||
'cyanide', 'azide', 'carbon monoxide',
|
||||
'edta', 'egta'
|
||||
]
|
||||
|
||||
for inhibitor in common_inhibitors:
|
||||
if inhibitor in commentary:
|
||||
inhibitors.append({
|
||||
'name': inhibitor,
|
||||
'type': 'irreversible' if 'iodoacetate' in inhibitor or 'maleimide' in inhibitor else 'reversible',
|
||||
'organism': parsed.get('organism', ''),
|
||||
'ec_number': ec_number,
|
||||
'commentary': parsed.get('commentary', '')
|
||||
})
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error getting inhibitors for {ec_number}: {e}")
|
||||
|
||||
# Remove duplicates
|
||||
unique_inhibitors = []
|
||||
seen = set()
|
||||
for inhibitor in inhibitors:
|
||||
key = (inhibitor['name'], inhibitor['organism'])
|
||||
if key not in seen:
|
||||
seen.add(key)
|
||||
unique_inhibitors.append(inhibitor)
|
||||
|
||||
return unique_inhibitors
|
||||
|
||||
|
||||
def get_activators(ec_number: str) -> List[Dict[str, Any]]:
|
||||
"""Get activator information for an enzyme (from commentary)."""
|
||||
validate_dependencies()
|
||||
|
||||
activators = []
|
||||
|
||||
try:
|
||||
km_data = get_km_values(ec_number)
|
||||
time.sleep(0.5) # Rate limiting
|
||||
|
||||
for entry in km_data:
|
||||
parsed = parse_km_entry(entry)
|
||||
commentary = parsed.get('commentary', '').lower()
|
||||
|
||||
# Look for activator keywords
|
||||
activator_keywords = ['activated', 'stimulated', 'enhanced', 'increased']
|
||||
if any(keyword in commentary for keyword in activator_keywords):
|
||||
# Try to extract activator names (this is approximate)
|
||||
common_activators = [
|
||||
'mg2+', 'mn2+', 'ca2+', 'zn2+',
|
||||
'k+', 'na+',
|
||||
'phosphate', 'pyrophosphate',
|
||||
'dithiothreitol', 'dtt',
|
||||
'β-mercaptoethanol'
|
||||
]
|
||||
|
||||
for activator in common_activators:
|
||||
if activator in commentary:
|
||||
activators.append({
|
||||
'name': activator,
|
||||
'type': 'metal ion' if '+' in activator else 'reducing agent' if 'dtt' in activator.lower() or 'mercapto' in activator.lower() else 'other',
|
||||
'mechanism': 'allosteric' if 'allosteric' in commentary else 'cofactor' else 'unknown',
|
||||
'organism': parsed.get('organism', ''),
|
||||
'ec_number': ec_number,
|
||||
'commentary': parsed.get('commentary', '')
|
||||
})
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error getting activators for {ec_number}: {e}")
|
||||
|
||||
# Remove duplicates
|
||||
unique_activators = []
|
||||
seen = set()
|
||||
for activator in activators:
|
||||
key = (activator['name'], activator['organism'])
|
||||
if key not in seen:
|
||||
seen.add(key)
|
||||
unique_activators.append(activator)
|
||||
|
||||
return unique_activators
|
||||
|
||||
|
||||
def find_thermophilic_homologs(ec_number: str, min_temp: int = 50) -> List[Dict[str, Any]]:
|
||||
"""Find thermophilic homologs of an enzyme."""
|
||||
validate_dependencies()
|
||||
|
||||
thermophilic = []
|
||||
|
||||
try:
|
||||
organisms = get_organisms_for_enzyme(ec_number)
|
||||
|
||||
for organism in organisms:
|
||||
# Check if organism might be thermophilic based on name
|
||||
thermophilic_keywords = ['therm', 'hypertherm', 'pyro']
|
||||
if any(keyword in organism.lower() for keyword in thermophilic_keywords):
|
||||
# Get kinetic data to extract temperature information
|
||||
km_data = get_km_values(ec_number, organism=organism)
|
||||
time.sleep(0.2) # Rate limiting
|
||||
|
||||
temperatures = []
|
||||
kms = []
|
||||
|
||||
for entry in km_data:
|
||||
parsed = parse_km_entry(entry)
|
||||
if 'temperature' in parsed:
|
||||
temperatures.append(parsed['temperature'])
|
||||
if 'km_value_numeric' in parsed:
|
||||
kms.append(parsed['km_value_numeric'])
|
||||
|
||||
if temperatures and max(temperatures) >= min_temp:
|
||||
thermophilic.append({
|
||||
'organism': organism,
|
||||
'ec_number': ec_number,
|
||||
'optimal_temperature': max(temperatures),
|
||||
'temperature_range': (min(temperatures), max(temperatures)),
|
||||
'km': sum(kms) / len(kms) if kms else None,
|
||||
'data_points': len(km_data)
|
||||
})
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error finding thermophilic homologs for {ec_number}: {e}")
|
||||
|
||||
return thermophilic
|
||||
|
||||
|
||||
def find_ph_stable_variants(ec_number: str, min_ph: float = 8.0, max_ph: float = 6.0) -> List[Dict[str, Any]]:
|
||||
"""Find pH-stable variants of an enzyme."""
|
||||
validate_dependencies()
|
||||
|
||||
ph_stable = []
|
||||
|
||||
try:
|
||||
organisms = get_organisms_for_enzyme(ec_number)
|
||||
|
||||
for organism in organisms:
|
||||
km_data = get_km_values(ec_number, organism=organism)
|
||||
time.sleep(0.2) # Rate limiting
|
||||
|
||||
phs = []
|
||||
kms = []
|
||||
|
||||
for entry in km_data:
|
||||
parsed = parse_km_entry(entry)
|
||||
if 'ph' in parsed:
|
||||
phs.append(parsed['ph'])
|
||||
if 'km_value_numeric' in parsed:
|
||||
kms.append(parsed['km_value_numeric'])
|
||||
|
||||
if phs:
|
||||
ph_range = (min(phs), max(phs))
|
||||
is_alkaline_stable = min_ph and ph_range[0] >= min_ph
|
||||
is_acid_stable = max_ph and ph_range[1] <= max_ph
|
||||
|
||||
if is_alkaline_stable or is_acid_stable:
|
||||
ph_stable.append({
|
||||
'organism': organism,
|
||||
'ec_number': ec_number,
|
||||
'ph_range': ph_range,
|
||||
'optimal_ph': sum(phs) / len(phs),
|
||||
'km': sum(kms) / len(kms) if kms else None,
|
||||
'stability_type': 'alkaline' if is_alkaline_stable else 'acidic',
|
||||
'data_points': len(km_data)
|
||||
})
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error finding pH-stable variants for {ec_number}: {e}")
|
||||
|
||||
return ph_stable
|
||||
|
||||
|
||||
def get_modeling_parameters(ec_number: str, substrate: str = None) -> Dict[str, Any]:
|
||||
"""Get parameters suitable for kinetic modeling."""
|
||||
validate_dependencies()
|
||||
|
||||
try:
|
||||
if substrate:
|
||||
km_data = get_km_values(ec_number, substrate=substrate)
|
||||
else:
|
||||
km_data = get_km_values(ec_number)
|
||||
|
||||
time.sleep(0.5) # Rate limiting
|
||||
|
||||
if not km_data:
|
||||
return {'ec_number': ec_number, 'error': 'No kinetic data found'}
|
||||
|
||||
# Extract modeling parameters
|
||||
kms = []
|
||||
phs = []
|
||||
temperatures = []
|
||||
v_max_values = []
|
||||
kcat_values = []
|
||||
|
||||
for entry in km_data:
|
||||
parsed = parse_km_entry(entry)
|
||||
|
||||
if 'km_value_numeric' in parsed:
|
||||
kms.append(parsed['km_value_numeric'])
|
||||
if 'ph' in parsed:
|
||||
phs.append(parsed['ph'])
|
||||
if 'temperature' in parsed:
|
||||
temperatures.append(parsed['temperature'])
|
||||
|
||||
# Look for Vmax and kcat in commentary (rare in BRENDA)
|
||||
commentary = parsed.get('commentary', '').lower()
|
||||
vmax_match = re.search(r'vmax\s*=\s*([\d.]+)', commentary)
|
||||
if vmax_match:
|
||||
v_max_values.append(float(vmax_match.group(1)))
|
||||
|
||||
kcat_match = re.search(r'kcat\s*=\s*([\d.]+)', commentary)
|
||||
if kcat_match:
|
||||
kcat_values.append(float(kcat_match.group(1)))
|
||||
|
||||
modeling_data = {
|
||||
'ec_number': ec_number,
|
||||
'substrate': substrate if substrate else 'various',
|
||||
'km': sum(kms) / len(kms) if kms else None,
|
||||
'km_std': (sum((x - sum(kms)/len(kms))**2 for x in kms) / len(kms))**0.5 if kms else None,
|
||||
'vmax': sum(v_max_values) / len(v_max_values) if v_max_values else None,
|
||||
'kcat': sum(kcat_values) / len(kcat_values) if kcat_values else None,
|
||||
'optimal_ph': sum(phs) / len(phs) if phs else None,
|
||||
'optimal_temperature': sum(temperatures) / len(temperatures) if temperatures else None,
|
||||
'data_points': len(km_data),
|
||||
'temperature': sum(temperatures) / len(temperatures) if temperatures else 25.0, # Default to 25°C
|
||||
'ph': sum(phs) / len(phs) if phs else 7.0, # Default to pH 7.0
|
||||
'enzyme_conc': 1.0, # Default enzyme concentration (μM)
|
||||
'substrate_conc': None, # Would be set by user
|
||||
}
|
||||
|
||||
return modeling_data
|
||||
|
||||
except Exception as e:
|
||||
return {'ec_number': ec_number, 'error': str(e)}
|
||||
|
||||
|
||||
def export_kinetic_data(ec_number: str, format: str = 'csv', filename: str = None) -> str:
|
||||
"""Export kinetic data to file."""
|
||||
validate_dependencies()
|
||||
|
||||
if not filename:
|
||||
filename = f"brenda_kinetic_data_{ec_number.replace('.', '_')}.{format}"
|
||||
|
||||
try:
|
||||
# Get all kinetic data
|
||||
km_data = get_km_values(ec_number)
|
||||
time.sleep(0.5) # Rate limiting
|
||||
|
||||
if not km_data:
|
||||
print(f"No kinetic data found for EC {ec_number}")
|
||||
return filename
|
||||
|
||||
# Parse all entries
|
||||
parsed_data = []
|
||||
for entry in km_data:
|
||||
parsed = parse_km_entry(entry)
|
||||
if parsed:
|
||||
parsed_data.append(parsed)
|
||||
|
||||
# Export based on format
|
||||
if format.lower() == 'csv':
|
||||
if parsed_data:
|
||||
df = pd.DataFrame(parsed_data)
|
||||
df.to_csv(filename, index=False)
|
||||
else:
|
||||
with open(filename, 'w', newline='') as f:
|
||||
f.write('No data found')
|
||||
|
||||
elif format.lower() == 'json':
|
||||
with open(filename, 'w') as f:
|
||||
json.dump(parsed_data, f, indent=2, default=str)
|
||||
|
||||
elif format.lower() == 'excel':
|
||||
if parsed_data and PANDAS_AVAILABLE:
|
||||
df = pd.DataFrame(parsed_data)
|
||||
df.to_excel(filename, index=False)
|
||||
else:
|
||||
print("pandas required for Excel export")
|
||||
return filename
|
||||
|
||||
print(f"Exported {len(parsed_data)} entries to {filename}")
|
||||
return filename
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error exporting data: {e}")
|
||||
return filename
|
||||
|
||||
|
||||
def search_by_pattern(pattern: str, limit: int = 50) -> List[Dict[str, Any]]:
|
||||
"""Search enzymes using a reaction pattern or keyword."""
|
||||
validate_dependencies()
|
||||
|
||||
enzymes = []
|
||||
|
||||
try:
|
||||
# Search reactions containing the pattern
|
||||
reactions = get_reactions("*", reaction=f"*{pattern}*")
|
||||
time.sleep(0.5) # Rate limiting
|
||||
|
||||
for entry in reactions[:limit]:
|
||||
parsed = parse_reaction_entry(entry)
|
||||
if parsed:
|
||||
enzymes.append({
|
||||
'ec_number': parsed.get('ecNumber', ''),
|
||||
'organism': parsed.get('organism', ''),
|
||||
'reaction': parsed.get('reaction', ''),
|
||||
'reactants': parsed.get('reactants', []),
|
||||
'products': parsed.get('products', []),
|
||||
'commentary': parsed.get('commentary', '')
|
||||
})
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error searching by pattern '{pattern}': {e}")
|
||||
|
||||
return enzymes
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# Example usage
|
||||
print("BRENDA Database Query Examples")
|
||||
print("=" * 40)
|
||||
|
||||
try:
|
||||
# Example 1: Search enzymes by substrate
|
||||
print("\n1. Searching enzymes for 'glucose':")
|
||||
enzymes = search_enzymes_by_substrate("glucose", limit=5)
|
||||
for enzyme in enzymes:
|
||||
print(f" EC {enzyme['ec_number']}: {enzyme['organism']}")
|
||||
print(f" Km: {enzyme['km_value']}")
|
||||
|
||||
# Example 2: Compare across organisms
|
||||
print("\n2. Comparing alcohol dehydrogenase (1.1.1.1) across organisms:")
|
||||
organisms = ["Escherichia coli", "Saccharomyces cerevisiae", "Homo sapiens"]
|
||||
comparison = compare_across_organisms("1.1.1.1", organisms)
|
||||
for comp in comparison:
|
||||
if comp.get('data_points', 0) > 0:
|
||||
print(f" {comp['organism']}:")
|
||||
print(f" Avg Km: {comp.get('average_km', 'N/A')}")
|
||||
print(f" Optimal pH: {comp.get('optimal_ph', 'N/A')}")
|
||||
|
||||
# Example 3: Get environmental parameters
|
||||
print("\n3. Environmental parameters for 1.1.1.1:")
|
||||
params = get_environmental_parameters("1.1.1.1")
|
||||
if params.get('data_points', 0) > 0:
|
||||
print(f" pH range: {params.get('ph_range', 'N/A')}")
|
||||
print(f" Temperature range: {params.get('temperature_range', 'N/A')}")
|
||||
|
||||
except Exception as e:
|
||||
print(f"Example failed: {e}")
|
||||
Reference in New Issue
Block a user