152 lines
5.4 KiB
Python
152 lines
5.4 KiB
Python
#!/usr/bin/env python3
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"""
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分析AutoDock Vina对接结果脚本
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用法: python analyze_results.py poses_directory output_csv
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"""
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import os
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import sys
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import pandas as pd
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import re
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from pathlib import Path
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def parse_vina_output(pdbqt_file):
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"""解析Vina输出文件，提取能量信息"""
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results = []
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try:
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with open(pdbqt_file, 'r') as f:
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lines = f.readlines()
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ligand_name = Path(pdbqt_file).stem.replace('_out', '')
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model_num = 0
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for line in lines:
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if line.startswith('REMARK VINA RESULT:'):
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# 解析能量信息
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# 格式: REMARK VINA RESULT: -8.5 0.000 0.000
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parts = line.strip().split()
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if len(parts) >= 4:
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binding_energy = float(parts[3])
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rmsd_lb = float(parts[4]) if len(parts) > 4 else 0.0
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rmsd_ub = float(parts[5]) if len(parts) > 5 else 0.0
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model_num += 1
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results.append({
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'ligand_name': ligand_name,
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'model': model_num,
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'binding_energy': binding_energy,
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'rmsd_lb': rmsd_lb,
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'rmsd_ub': rmsd_ub,
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'file_path': pdbqt_file
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})
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except Exception as e:
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print(f"解析文件失败 {pdbqt_file}: {e}")
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return results
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def analyze_poses_directory(poses_dir):
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"""分析整个poses目录"""
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all_results = []
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poses_path = Path(poses_dir)
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if not poses_path.exists():
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print(f"错误: 目录不存在 {poses_dir}")
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return []
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# 查找所有_out.pdbqt文件
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pdbqt_files = list(poses_path.glob("*_out.pdbqt"))
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print(f"找到 {len(pdbqt_files)} 个结果文件")
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for pdbqt_file in pdbqt_files:
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results = parse_vina_output(str(pdbqt_file))
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all_results.extend(results)
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return all_results
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def generate_summary_stats(df):
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"""生成汇总统计信息"""
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if df.empty:
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return {}
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# 获取每个配体的最佳结合能
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best_poses = df.loc[df.groupby('ligand_name')['binding_energy'].idxmin()]
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stats = {
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'total_ligands': df['ligand_name'].nunique(),
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'total_poses': len(df),
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'best_binding_energy': df['binding_energy'].min(),
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'worst_binding_energy': df['binding_energy'].max(),
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'mean_binding_energy': df['binding_energy'].mean(),
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'median_binding_energy': df['binding_energy'].median(),
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'std_binding_energy': df['binding_energy'].std(),
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'ligands_better_than_minus_7': (best_poses['binding_energy'] < -7.0).sum(),
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'ligands_better_than_minus_8': (best_poses['binding_energy'] < -8.0).sum(),
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'ligands_better_than_minus_9': (best_poses['binding_energy'] < -9.0).sum(),
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}
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return stats
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def main():
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if len(sys.argv) != 3:
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print("用法: python analyze_results.py <poses目录> <输出CSV文件>")
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sys.exit(1)
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poses_dir = sys.argv[1]
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output_csv = sys.argv[2]
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print("开始分析对接结果...")
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# 分析所有结果
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results = analyze_poses_directory(poses_dir)
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if not results:
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print("没有找到有效的结果文件")
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sys.exit(1)
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# 转换为DataFrame
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df = pd.DataFrame(results)
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# 保存详细结果
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df.to_csv(output_csv, index=False)
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print(f"详细结果已保存到: {output_csv}")
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# 生成汇总统计
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stats = generate_summary_stats(df)
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# 打印汇总信息
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print("\n=== 对接结果汇总 ===")
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print(f"总配体数量: {stats['total_ligands']}")
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print(f"总构象数量: {stats['total_poses']}")
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print(f"最佳结合能: {stats['best_binding_energy']:.2f} kcal/mol")
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print(f"最差结合能: {stats['worst_binding_energy']:.2f} kcal/mol")
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print(f"平均结合能: {stats['mean_binding_energy']:.2f} kcal/mol")
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print(f"中位结合能: {stats['median_binding_energy']:.2f} kcal/mol")
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print(f"标准差: {stats['std_binding_energy']:.2f} kcal/mol")
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print(f"结合能 < -7.0 的配体: {stats['ligands_better_than_minus_7']}")
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print(f"结合能 < -8.0 的配体: {stats['ligands_better_than_minus_8']}")
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print(f"结合能 < -9.0 的配体: {stats['ligands_better_than_minus_9']}")
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# 显示前10个最佳结果
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best_results = df.loc[df.groupby('ligand_name')['binding_energy'].idxmin()]
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best_results = best_results.sort_values('binding_energy').head(10)
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print("\n=== 前10个最佳结果 ===")
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for _, row in best_results.iterrows():
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print(f"{row['ligand_name']}: {row['binding_energy']:.2f} kcal/mol")
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# 保存汇总统计
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summary_file = output_csv.replace('.csv', '_summary.txt')
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with open(summary_file, 'w') as f:
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f.write("AutoDock Vina 对接结果汇总\n")
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f.write("=" * 30 + "\n\n")
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for key, value in stats.items():
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f.write(f"{key}: {value}\n")
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print(f"\n汇总统计已保存到: {summary_file}")
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if __name__ == "__main__":
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main()
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