Initial commit for brenda-database
This commit is contained in:
dfty
772
scripts/brenda_visualization.py
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772
scripts/brenda_visualization.py
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"""
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BRENDA Database Visualization Utilities
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This module provides visualization functions for BRENDA enzyme data,
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including kinetic parameters, environmental conditions, and pathway analysis.
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Key features:
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- Plot Km, kcat, and Vmax distributions
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- Compare enzyme properties across organisms
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- Visualize pH and temperature activity profiles
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- Plot substrate specificity and affinity data
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- Generate Michaelis-Menten curves
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- Create heatmaps and correlation plots
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- Support for pathway visualization
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Installation:
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uv pip install matplotlib seaborn pandas numpy
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Usage:
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from scripts.brenda_visualization import plot_kinetic_parameters, plot_michaelis_menten
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plot_kinetic_parameters("1.1.1.1")
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plot_michaelis_menten("1.1.1.1", substrate="ethanol")
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"""
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import math
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import numpy as np
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from typing import List, Dict, Any, Optional, Tuple
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import matplotlib.pyplot as plt
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import seaborn as sns
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from pathlib import Path
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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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try:
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from brenda_queries import (
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get_km_values, get_reactions, parse_km_entry, parse_reaction_entry,
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compare_across_organisms, get_environmental_parameters,
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get_substrate_specificity, get_modeling_parameters,
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search_enzymes_by_substrate, search_by_pattern
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)
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BRENDA_QUERIES_AVAILABLE = True
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except ImportError:
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print("Warning: brenda_queries not available")
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BRENDA_QUERIES_AVAILABLE = False
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# Set style for plots
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plt.style.use('default')
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sns.set_palette("husl")
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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 PANDAS_AVAILABLE:
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missing.append("pandas")
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if not BRENDA_QUERIES_AVAILABLE:
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missing.append("brenda_queries")
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if missing:
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raise ImportError(f"Missing required dependencies: {', '.join(missing)}")
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def plot_kinetic_parameters(ec_number: str, save_path: str = None, show_plot: bool = True) -> str:
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"""Plot kinetic parameter distributions for an enzyme."""
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validate_dependencies()
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try:
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# Get Km data
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km_data = get_km_values(ec_number)
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if not km_data:
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print(f"No kinetic data found for EC {ec_number}")
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return save_path
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# Parse data
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parsed_entries = []
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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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parsed_entries.append(parsed)
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if not parsed_entries:
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print(f"No numeric Km data found for EC {ec_number}")
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return save_path
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# Create figure with subplots
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fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(15, 12))
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fig.suptitle(f'Kinetic Parameters for EC {ec_number}', fontsize=16, fontweight='bold')
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# Extract data
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km_values = [entry['km_value_numeric'] for entry in parsed_entries]
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organisms = [entry.get('organism', 'Unknown') for entry in parsed_entries]
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substrates = [entry.get('substrate', 'Unknown') for entry in parsed_entries]
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# Plot 1: Km distribution histogram
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ax1.hist(km_values, bins=30, alpha=0.7, edgecolor='black')
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ax1.set_xlabel('Km (mM)')
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ax1.set_ylabel('Frequency')
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ax1.set_title('Km Value Distribution')
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ax1.axvline(np.mean(km_values), color='red', linestyle='--', label=f'Mean: {np.mean(km_values):.2f}')
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ax1.axvline(np.median(km_values), color='blue', linestyle='--', label=f'Median: {np.median(km_values):.2f}')
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ax1.legend()
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# Plot 2: Km by organism (top 10)
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if PANDAS_AVAILABLE:
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df = pd.DataFrame({'Km': km_values, 'Organism': organisms})
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organism_means = df.groupby('Organism')['Km'].mean().sort_values(ascending=False).head(10)
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organism_means.plot(kind='bar', ax=ax2)
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ax2.set_ylabel('Mean Km (mM)')
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ax2.set_title('Mean Km by Organism (Top 10)')
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ax2.tick_params(axis='x', rotation=45)
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# Plot 3: Km by substrate (top 10)
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if PANDAS_AVAILABLE:
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df = pd.DataFrame({'Km': km_values, 'Substrate': substrates})
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substrate_means = df.groupby('Substrate')['Km'].mean().sort_values(ascending=False).head(10)
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substrate_means.plot(kind='bar', ax=ax3)
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ax3.set_ylabel('Mean Km (mM)')
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ax3.set_title('Mean Km by Substrate (Top 10)')
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ax3.tick_params(axis='x', rotation=45)
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# Plot 4: Box plot by organism (top 5)
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if PANDAS_AVAILABLE:
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top_organisms = df.groupby('Organism')['Km'].count().sort_values(ascending=False).head(5).index
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top_data = df[df['Organism'].isin(top_organisms)]
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sns.boxplot(data=top_data, x='Organism', y='Km', ax=ax4)
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ax4.set_ylabel('Km (mM)')
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ax4.set_title('Km Distribution by Organism (Top 5)')
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ax4.tick_params(axis='x', rotation=45)
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plt.tight_layout()
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# Save plot
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if save_path:
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plt.savefig(save_path, dpi=300, bbox_inches='tight')
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print(f"Kinetic parameters plot saved to {save_path}")
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if show_plot:
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plt.show()
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else:
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plt.close()
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return save_path or f"kinetic_parameters_{ec_number.replace('.', '_')}.png"
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except Exception as e:
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print(f"Error plotting kinetic parameters: {e}")
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return save_path
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def plot_organism_comparison(ec_number: str, organisms: List[str], save_path: str = None, show_plot: bool = True) -> str:
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"""Compare enzyme properties across multiple organisms."""
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validate_dependencies()
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try:
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# Get comparison data
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comparison = compare_across_organisms(ec_number, organisms)
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if not comparison:
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print(f"No comparison data found for EC {ec_number}")
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return save_path
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# Filter out entries with no data
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valid_data = [c for c in comparison if c.get('data_points', 0) > 0]
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if not valid_data:
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print(f"No valid data for organism comparison of EC {ec_number}")
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return save_path
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# Create figure
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fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(15, 12))
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fig.suptitle(f'Organism Comparison for EC {ec_number}', fontsize=16, fontweight='bold')
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# Extract data
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names = [c['organism'] for c in valid_data]
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avg_kms = [c.get('average_km', 0) for c in valid_data if c.get('average_km')]
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optimal_phs = [c.get('optimal_ph', 0) for c in valid_data if c.get('optimal_ph')]
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optimal_temps = [c.get('optimal_temperature', 0) for c in valid_data if c.get('optimal_temperature')]
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data_points = [c.get('data_points', 0) for c in valid_data]
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# Plot 1: Average Km comparison
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if avg_kms:
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ax1.bar(names, avg_kms)
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ax1.set_ylabel('Average Km (mM)')
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ax1.set_title('Average Km Comparison')
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ax1.tick_params(axis='x', rotation=45)
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# Plot 2: Optimal pH comparison
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if optimal_phs:
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ax2.bar(names, optimal_phs)
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ax2.set_ylabel('Optimal pH')
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ax2.set_title('Optimal pH Comparison')
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ax2.tick_params(axis='x', rotation=45)
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# Plot 3: Optimal temperature comparison
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if optimal_temps:
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ax3.bar(names, optimal_temps)
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ax3.set_ylabel('Optimal Temperature (°C)')
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ax3.set_title('Optimal Temperature Comparison')
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ax3.tick_params(axis='x', rotation=45)
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# Plot 4: Data points comparison
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ax4.bar(names, data_points)
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ax4.set_ylabel('Number of Data Points')
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ax4.set_title('Available Data Points')
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ax4.tick_params(axis='x', rotation=45)
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plt.tight_layout()
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# Save plot
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if save_path:
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plt.savefig(save_path, dpi=300, bbox_inches='tight')
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print(f"Organism comparison plot saved to {save_path}")
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if show_plot:
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plt.show()
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else:
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plt.close()
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return save_path or f"organism_comparison_{ec_number.replace('.', '_')}.png"
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except Exception as e:
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print(f"Error plotting organism comparison: {e}")
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return save_path
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def plot_pH_profiles(ec_number: str, save_path: str = None, show_plot: bool = True) -> str:
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"""Plot pH activity profiles for an enzyme."""
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validate_dependencies()
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try:
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# Get kinetic data
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km_data = get_km_values(ec_number)
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if not km_data:
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print(f"No pH data found for EC {ec_number}")
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return save_path
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# Parse data and extract pH information
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ph_kms = []
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ph_organisms = []
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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 and 'km_value_numeric' in parsed:
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ph_kms.append((parsed['ph'], parsed['km_value_numeric']))
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ph_organisms.append(parsed.get('organism', 'Unknown'))
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if not ph_kms:
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print(f"No pH-Km data found for EC {ec_number}")
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return save_path
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# Create figure
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fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))
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fig.suptitle(f'pH Activity Profiles for EC {ec_number}', fontsize=16, fontweight='bold')
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# Extract data
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ph_values = [item[0] for item in ph_kms]
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km_values = [item[1] for item in ph_kms]
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# Plot 1: pH vs Km scatter plot
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scatter = ax1.scatter(ph_values, km_values, alpha=0.6, s=50)
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ax1.set_xlabel('pH')
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ax1.set_ylabel('Km (mM)')
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ax1.set_title('pH vs Km Values')
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ax1.grid(True, alpha=0.3)
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# Add trend line
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if len(ph_values) > 2:
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z = np.polyfit(ph_values, km_values, 1)
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p = np.poly1d(z)
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ax1.plot(ph_values, p(ph_values), "r--", alpha=0.8, label=f'Trend: y={z[0]:.3f}x+{z[1]:.3f}')
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ax1.legend()
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# Plot 2: pH distribution histogram
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ax2.hist(ph_values, bins=20, alpha=0.7, edgecolor='black')
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ax2.set_xlabel('pH')
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ax2.set_ylabel('Frequency')
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ax2.set_title('pH Distribution')
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ax2.axvline(np.mean(ph_values), color='red', linestyle='--', label=f'Mean: {np.mean(ph_values):.2f}')
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ax2.axvline(np.median(ph_values), color='blue', linestyle='--', label=f'Median: {np.median(ph_values):.2f}')
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ax2.legend()
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plt.tight_layout()
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# Save plot
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if save_path:
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plt.savefig(save_path, dpi=300, bbox_inches='tight')
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print(f"pH profile plot saved to {save_path}")
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if show_plot:
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plt.show()
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else:
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plt.close()
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return save_path or f"ph_profile_{ec_number.replace('.', '_')}.png"
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except Exception as e:
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print(f"Error plotting pH profiles: {e}")
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return save_path
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def plot_temperature_profiles(ec_number: str, save_path: str = None, show_plot: bool = True) -> str:
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"""Plot temperature activity profiles for an enzyme."""
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validate_dependencies()
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try:
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# Get kinetic data
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km_data = get_km_values(ec_number)
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if not km_data:
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print(f"No temperature data found for EC {ec_number}")
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return save_path
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# Parse data and extract temperature information
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temp_kms = []
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temp_organisms = []
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for entry in km_data:
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parsed = parse_km_entry(entry)
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if 'temperature' in parsed and 'km_value_numeric' in parsed:
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temp_kms.append((parsed['temperature'], parsed['km_value_numeric']))
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temp_organisms.append(parsed.get('organism', 'Unknown'))
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if not temp_kms:
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print(f"No temperature-Km data found for EC {ec_number}")
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return save_path
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# Create figure
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fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))
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fig.suptitle(f'Temperature Activity Profiles for EC {ec_number}', fontsize=16, fontweight='bold')
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# Extract data
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temp_values = [item[0] for item in temp_kms]
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km_values = [item[1] for item in temp_kms]
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# Plot 1: Temperature vs Km scatter plot
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scatter = ax1.scatter(temp_values, km_values, alpha=0.6, s=50)
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ax1.set_xlabel('Temperature (°C)')
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ax1.set_ylabel('Km (mM)')
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ax1.set_title('Temperature vs Km Values')
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ax1.grid(True, alpha=0.3)
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# Add trend line
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if len(temp_values) > 2:
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z = np.polyfit(temp_values, km_values, 2) # Quadratic fit for temperature optima
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p = np.poly1d(z)
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x_smooth = np.linspace(min(temp_values), max(temp_values), 100)
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ax1.plot(x_smooth, p(x_smooth), "r--", alpha=0.8, label='Polynomial fit')
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# Find optimum temperature
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optimum_idx = np.argmin(p(x_smooth))
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optimum_temp = x_smooth[optimum_idx]
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ax1.axvline(optimum_temp, color='green', linestyle=':', label=f'Optimal: {optimum_temp:.1f}°C')
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ax1.legend()
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# Plot 2: Temperature distribution histogram
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ax2.hist(temp_values, bins=20, alpha=0.7, edgecolor='black')
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ax2.set_xlabel('Temperature (°C)')
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ax2.set_ylabel('Frequency')
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ax2.set_title('Temperature Distribution')
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ax2.axvline(np.mean(temp_values), color='red', linestyle='--', label=f'Mean: {np.mean(temp_values):.1f}°C')
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ax2.axvline(np.median(temp_values), color='blue', linestyle='--', label=f'Median: {np.median(temp_values):.1f}°C')
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ax2.legend()
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plt.tight_layout()
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# Save plot
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if save_path:
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plt.savefig(save_path, dpi=300, bbox_inches='tight')
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print(f"Temperature profile plot saved to {save_path}")
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if show_plot:
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plt.show()
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else:
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plt.close()
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return save_path or f"temperature_profile_{ec_number.replace('.', '_')}.png"
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except Exception as e:
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print(f"Error plotting temperature profiles: {e}")
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return save_path
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||||
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||||
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def plot_substrate_specificity(ec_number: str, save_path: str = None, show_plot: bool = True) -> str:
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"""Plot substrate specificity and affinity for an enzyme."""
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validate_dependencies()
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||||
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try:
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# Get substrate specificity data
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specificity = get_substrate_specificity(ec_number)
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if not specificity:
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print(f"No substrate specificity data found for EC {ec_number}")
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return save_path
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||||
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||||
# Create figure
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||||
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(15, 12))
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||||
fig.suptitle(f'Substrate Specificity for EC {ec_number}', fontsize=16, fontweight='bold')
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||||
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||||
# Extract data
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substrates = [s['name'] for s in specificity]
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kms = [s['km'] for s in specificity if s.get('km')]
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data_points = [s['data_points'] for s in specificity]
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# Get top substrates for plotting
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if PANDAS_AVAILABLE and kms:
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df = pd.DataFrame({'Substrate': substrates, 'Km': kms, 'DataPoints': data_points})
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top_substrates = df.nlargest(15, 'DataPoints') # Top 15 by data points
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||||
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||||
# Plot 1: Km values for top substrates (sorted by affinity)
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top_sorted = top_substrates.sort_values('Km')
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ax1.barh(range(len(top_sorted)), top_sorted['Km'])
|
||||
ax1.set_yticks(range(len(top_sorted)))
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||||
ax1.set_yticklabels([s[:30] + '...' if len(s) > 30 else s for s in top_sorted['Substrate']])
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||||
ax1.set_xlabel('Km (mM)')
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||||
ax1.set_title('Substrate Affinity (Lower Km = Higher Affinity)')
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||||
ax1.invert_yaxis() # Best affinity at top
|
||||
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||||
# Plot 2: Data points by substrate
|
||||
ax2.barh(range(len(top_sorted)), top_sorted['DataPoints'])
|
||||
ax2.set_yticks(range(len(top_sorted)))
|
||||
ax2.set_yticklabels([s[:30] + '...' if len(s) > 30 else s for s in top_sorted['Substrate']])
|
||||
ax2.set_xlabel('Number of Data Points')
|
||||
ax2.set_title('Data Availability by Substrate')
|
||||
ax2.invert_yaxis()
|
||||
|
||||
# Plot 3: Km distribution
|
||||
ax3.hist(kms, bins=20, alpha=0.7, edgecolor='black')
|
||||
ax3.set_xlabel('Km (mM)')
|
||||
ax3.set_ylabel('Frequency')
|
||||
ax3.set_title('Km Value Distribution')
|
||||
ax3.axvline(np.mean(kms), color='red', linestyle='--', label=f'Mean: {np.mean(kms):.2f}')
|
||||
ax3.axvline(np.median(kms), color='blue', linestyle='--', label=f'Median: {np.median(kms):.2f}')
|
||||
ax3.legend()
|
||||
|
||||
# Plot 4: Km vs Data Points scatter
|
||||
ax4.scatter(df['DataPoints'], df['Km'], alpha=0.6)
|
||||
ax4.set_xlabel('Number of Data Points')
|
||||
ax4.set_ylabel('Km (mM)')
|
||||
ax4.set_title('Km vs Data Points')
|
||||
ax4.grid(True, alpha=0.3)
|
||||
|
||||
plt.tight_layout()
|
||||
|
||||
# Save plot
|
||||
if save_path:
|
||||
plt.savefig(save_path, dpi=300, bbox_inches='tight')
|
||||
print(f"Substrate specificity plot saved to {save_path}")
|
||||
|
||||
if show_plot:
|
||||
plt.show()
|
||||
else:
|
||||
plt.close()
|
||||
|
||||
return save_path or f"substrate_specificity_{ec_number.replace('.', '_')}.png"
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error plotting substrate specificity: {e}")
|
||||
return save_path
|
||||
|
||||
|
||||
def plot_michaelis_menten(ec_number: str, substrate: str = None, save_path: str = None, show_plot: bool = True) -> str:
|
||||
"""Generate Michaelis-Menten curves for an enzyme."""
|
||||
validate_dependencies()
|
||||
|
||||
try:
|
||||
# Get modeling parameters
|
||||
model_data = get_modeling_parameters(ec_number, substrate)
|
||||
|
||||
if not model_data or model_data.get('error'):
|
||||
print(f"No modeling data found for EC {ec_number}")
|
||||
return save_path
|
||||
|
||||
km = model_data.get('km')
|
||||
vmax = model_data.get('vmax')
|
||||
kcat = model_data.get('kcat')
|
||||
enzyme_conc = model_data.get('enzyme_conc', 1.0)
|
||||
|
||||
if not km:
|
||||
print(f"No Km data available for plotting")
|
||||
return save_path
|
||||
|
||||
# Create figure
|
||||
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))
|
||||
fig.suptitle(f'Michaelis-Menten Kinetics for EC {ec_number}' + (f' - {substrate}' if substrate else ''),
|
||||
fontsize=16, fontweight='bold')
|
||||
|
||||
# Generate substrate concentration range
|
||||
substrate_range = np.linspace(0, km * 5, 1000)
|
||||
|
||||
# Calculate reaction rates
|
||||
if vmax:
|
||||
# Use actual Vmax if available
|
||||
rates = (vmax * substrate_range) / (km + substrate_range)
|
||||
elif kcat and enzyme_conc:
|
||||
# Calculate Vmax from kcat and enzyme concentration
|
||||
vmax_calc = kcat * enzyme_conc
|
||||
rates = (vmax_calc * substrate_range) / (km + substrate_range)
|
||||
else:
|
||||
# Use normalized Vmax = 1.0
|
||||
rates = substrate_range / (km + substrate_range)
|
||||
|
||||
# Plot 1: Michaelis-Menten curve
|
||||
ax1.plot(substrate_range, rates, 'b-', linewidth=2, label='Michaelis-Menten')
|
||||
ax1.axhline(y=rates[-1] * 0.5, color='r', linestyle='--', alpha=0.7, label='0.5 × Vmax')
|
||||
ax1.axvline(x=km, color='g', linestyle='--', alpha=0.7, label=f'Km = {km:.2f}')
|
||||
ax1.set_xlabel('Substrate Concentration (mM)')
|
||||
ax1.set_ylabel('Reaction Rate')
|
||||
ax1.set_title('Michaelis-Menten Curve')
|
||||
ax1.legend()
|
||||
ax1.grid(True, alpha=0.3)
|
||||
|
||||
# Add annotation for Km
|
||||
km_rate = (substrate_range[km == min(substrate_range, key=lambda x: abs(x-km))] *
|
||||
(vmax if vmax else kcat * enzyme_conc if kcat else 1.0)) / (km +
|
||||
substrate_range[km == min(substrate_range, key=lambda x: abs(x-km))])
|
||||
ax1.plot(km, km_rate, 'ro', markersize=8)
|
||||
|
||||
# Plot 2: Lineweaver-Burk plot (double reciprocal)
|
||||
substrate_range_nonzero = substrate_range[substrate_range > 0]
|
||||
rates_nonzero = rates[substrate_range > 0]
|
||||
|
||||
reciprocal_substrate = 1 / substrate_range_nonzero
|
||||
reciprocal_rate = 1 / rates_nonzero
|
||||
|
||||
ax2.scatter(reciprocal_substrate, reciprocal_rate, alpha=0.6, s=10)
|
||||
|
||||
# Fit linear regression
|
||||
z = np.polyfit(reciprocal_substrate, reciprocal_rate, 1)
|
||||
p = np.poly1d(z)
|
||||
x_fit = np.linspace(min(reciprocal_substrate), max(reciprocal_substrate), 100)
|
||||
ax2.plot(x_fit, p(x_fit), 'r-', linewidth=2, label=f'1/Vmax = {z[1]:.3f}')
|
||||
|
||||
ax2.set_xlabel('1/[Substrate] (1/mM)')
|
||||
ax2.set_ylabel('1/Rate')
|
||||
ax2.set_title('Lineweaver-Burk Plot')
|
||||
ax2.legend()
|
||||
ax2.grid(True, alpha=0.3)
|
||||
|
||||
# Add parameter information
|
||||
info_text = f"Km = {km:.3f} mM"
|
||||
if vmax:
|
||||
info_text += f"\nVmax = {vmax:.3f}"
|
||||
if kcat:
|
||||
info_text += f"\nkcat = {kcat:.3f} s⁻¹"
|
||||
if enzyme_conc:
|
||||
info_text += f"\n[Enzyme] = {enzyme_conc:.3f} μM"
|
||||
|
||||
fig.text(0.02, 0.98, info_text, transform=fig.transFigure,
|
||||
fontsize=10, verticalalignment='top',
|
||||
bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.8))
|
||||
|
||||
plt.tight_layout()
|
||||
|
||||
# Save plot
|
||||
if save_path:
|
||||
plt.savefig(save_path, dpi=300, bbox_inches='tight')
|
||||
print(f"Michaelis-Menten plot saved to {save_path}")
|
||||
|
||||
if show_plot:
|
||||
plt.show()
|
||||
else:
|
||||
plt.close()
|
||||
|
||||
return save_path or f"michaelis_menten_{ec_number.replace('.', '_')}_{substrate or 'all'}.png"
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error plotting Michaelis-Menten: {e}")
|
||||
return save_path
|
||||
|
||||
|
||||
def create_heatmap_data(ec_number: str, parameters: List[str] = None) -> Dict[str, Any]:
|
||||
"""Create data for heatmap visualization."""
|
||||
validate_dependencies()
|
||||
|
||||
try:
|
||||
# Get comparison data across organisms
|
||||
organisms = ["Escherichia coli", "Saccharomyces cerevisiae", "Bacillus subtilis",
|
||||
"Homo sapiens", "Mus musculus", "Rattus norvegicus"]
|
||||
comparison = compare_across_organisms(ec_number, organisms)
|
||||
|
||||
if not comparison:
|
||||
return None
|
||||
|
||||
# Create heatmap data
|
||||
heatmap_data = {
|
||||
'organisms': [],
|
||||
'average_km': [],
|
||||
'optimal_ph': [],
|
||||
'optimal_temperature': [],
|
||||
'data_points': []
|
||||
}
|
||||
|
||||
for comp in comparison:
|
||||
if comp.get('data_points', 0) > 0:
|
||||
heatmap_data['organisms'].append(comp['organism'])
|
||||
heatmap_data['average_km'].append(comp.get('average_km', 0))
|
||||
heatmap_data['optimal_ph'].append(comp.get('optimal_ph', 0))
|
||||
heatmap_data['optimal_temperature'].append(comp.get('optimal_temperature', 0))
|
||||
heatmap_data['data_points'].append(comp.get('data_points', 0))
|
||||
|
||||
return heatmap_data
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error creating heatmap data: {e}")
|
||||
return None
|
||||
|
||||
|
||||
def plot_heatmap(ec_number: str, save_path: str = None, show_plot: bool = True) -> str:
|
||||
"""Create heatmap visualization of enzyme properties."""
|
||||
validate_dependencies()
|
||||
|
||||
try:
|
||||
heatmap_data = create_heatmap_data(ec_number)
|
||||
|
||||
if not heatmap_data or not heatmap_data['organisms']:
|
||||
print(f"No heatmap data available for EC {ec_number}")
|
||||
return save_path
|
||||
|
||||
if not PANDAS_AVAILABLE:
|
||||
print("pandas required for heatmap plotting")
|
||||
return save_path
|
||||
|
||||
# Create DataFrame for heatmap
|
||||
df = pd.DataFrame({
|
||||
'Organism': heatmap_data['organisms'],
|
||||
'Avg Km (mM)': heatmap_data['average_km'],
|
||||
'Optimal pH': heatmap_data['optimal_ph'],
|
||||
'Optimal Temp (°C)': heatmap_data['optimal_temperature'],
|
||||
'Data Points': heatmap_data['data_points']
|
||||
})
|
||||
|
||||
# Normalize data for better visualization
|
||||
df_normalized = df.copy()
|
||||
for col in ['Avg Km (mM)', 'Optimal pH', 'Optimal Temp (°C)', 'Data Points']:
|
||||
if col in df.columns:
|
||||
df_normalized[col] = (df[col] - df[col].min()) / (df[col].max() - df[col].min())
|
||||
|
||||
# Create figure
|
||||
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 8))
|
||||
fig.suptitle(f'Enzyme Properties Heatmap for EC {ec_number}', fontsize=16, fontweight='bold')
|
||||
|
||||
# Plot 1: Raw data heatmap
|
||||
heatmap_data_raw = df.set_index('Organism')[['Avg Km (mM)', 'Optimal pH', 'Optimal Temp (°C)', 'Data Points']].T
|
||||
sns.heatmap(heatmap_data_raw, annot=True, fmt='.2f', cmap='viridis', ax=ax1)
|
||||
ax1.set_title('Raw Values')
|
||||
|
||||
# Plot 2: Normalized data heatmap
|
||||
heatmap_data_norm = df_normalized.set_index('Organism')[['Avg Km (mM)', 'Optimal pH', 'Optimal Temp (°C)', 'Data Points']].T
|
||||
sns.heatmap(heatmap_data_norm, annot=True, fmt='.2f', cmap='viridis', ax=ax2)
|
||||
ax2.set_title('Normalized Values (0-1)')
|
||||
|
||||
plt.tight_layout()
|
||||
|
||||
# Save plot
|
||||
if save_path:
|
||||
plt.savefig(save_path, dpi=300, bbox_inches='tight')
|
||||
print(f"Heatmap plot saved to {save_path}")
|
||||
|
||||
if show_plot:
|
||||
plt.show()
|
||||
else:
|
||||
plt.close()
|
||||
|
||||
return save_path or f"heatmap_{ec_number.replace('.', '_')}.png"
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error plotting heatmap: {e}")
|
||||
return save_path
|
||||
|
||||
|
||||
def generate_summary_plots(ec_number: str, save_dir: str = None) -> List[str]:
|
||||
"""Generate a comprehensive set of plots for an enzyme."""
|
||||
validate_dependencies()
|
||||
|
||||
if save_dir is None:
|
||||
save_dir = f"enzyme_plots_{ec_number.replace('.', '_')}"
|
||||
|
||||
# Create save directory
|
||||
Path(save_dir).mkdir(exist_ok=True)
|
||||
|
||||
generated_files = []
|
||||
|
||||
# Generate all plot types
|
||||
plot_functions = [
|
||||
('kinetic_parameters', plot_kinetic_parameters),
|
||||
('ph_profiles', plot_pH_profiles),
|
||||
('temperature_profiles', plot_temperature_profiles),
|
||||
('substrate_specificity', plot_substrate_specificity),
|
||||
('heatmap', plot_heatmap),
|
||||
]
|
||||
|
||||
for plot_name, plot_func in plot_functions:
|
||||
try:
|
||||
save_path = f"{save_dir}/{plot_name}_{ec_number.replace('.', '_')}.png"
|
||||
result_path = plot_func(ec_number, save_path=save_path, show_plot=False)
|
||||
if result_path:
|
||||
generated_files.append(result_path)
|
||||
print(f"Generated {plot_name} plot")
|
||||
else:
|
||||
print(f"Failed to generate {plot_name} plot")
|
||||
except Exception as e:
|
||||
print(f"Error generating {plot_name} plot: {e}")
|
||||
|
||||
# Generate organism comparison for common model organisms
|
||||
model_organisms = ["Escherichia coli", "Saccharomyces cerevisiae", "Homo sapiens"]
|
||||
try:
|
||||
save_path = f"{save_dir}/organism_comparison_{ec_number.replace('.', '_')}.png"
|
||||
result_path = plot_organism_comparison(ec_number, model_organisms, save_path=save_path, show_plot=False)
|
||||
if result_path:
|
||||
generated_files.append(result_path)
|
||||
print("Generated organism comparison plot")
|
||||
except Exception as e:
|
||||
print(f"Error generating organism comparison plot: {e}")
|
||||
|
||||
# Generate Michaelis-Menten plot for most common substrate
|
||||
try:
|
||||
specificity = get_substrate_specificity(ec_number)
|
||||
if specificity:
|
||||
most_common = max(specificity, key=lambda x: x.get('data_points', 0))
|
||||
substrate_name = most_common['name'].split()[0] # Take first word
|
||||
save_path = f"{save_dir}/michaelis_menten_{ec_number.replace('.', '_')}_{substrate_name}.png"
|
||||
result_path = plot_michaelis_menten(ec_number, substrate_name, save_path=save_path, show_plot=False)
|
||||
if result_path:
|
||||
generated_files.append(result_path)
|
||||
print(f"Generated Michaelis-Menten plot for {substrate_name}")
|
||||
except Exception as e:
|
||||
print(f"Error generating Michaelis-Menten plot: {e}")
|
||||
|
||||
print(f"\nGenerated {len(generated_files)} plots in directory: {save_dir}")
|
||||
return generated_files
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# Example usage
|
||||
print("BRENDA Visualization Examples")
|
||||
print("=" * 40)
|
||||
|
||||
try:
|
||||
ec_number = "1.1.1.1" # Alcohol dehydrogenase
|
||||
|
||||
print(f"\n1. Generating kinetic parameters plot for EC {ec_number}")
|
||||
plot_kinetic_parameters(ec_number, show_plot=False)
|
||||
|
||||
print(f"\n2. Generating pH profile plot for EC {ec_number}")
|
||||
plot_pH_profiles(ec_number, show_plot=False)
|
||||
|
||||
print(f"\n3. Generating substrate specificity plot for EC {ec_number}")
|
||||
plot_substrate_specificity(ec_number, show_plot=False)
|
||||
|
||||
print(f"\n4. Generating Michaelis-Menten plot for EC {ec_number}")
|
||||
plot_michaelis_menten(ec_number, substrate="ethanol", show_plot=False)
|
||||
|
||||
print(f"\n5. Generating organism comparison plot for EC {ec_number}")
|
||||
organisms = ["Escherichia coli", "Saccharomyces cerevisiae", "Homo sapiens"]
|
||||
plot_organism_comparison(ec_number, organisms, show_plot=False)
|
||||
|
||||
print(f"\n6. Generating comprehensive summary plots for EC {ec_number}")
|
||||
summary_files = generate_summary_plots(ec_number, show_plot=False)
|
||||
print(f"Generated {len(summary_files)} summary plots")
|
||||
|
||||
except Exception as e:
|
||||
print(f"Example failed: {e}")
|
||||
Reference in New Issue
Block a user