多种毒素:一种基于序列的堆叠组合模型,用于多种类型的蛋白质毒素分类
Harshika Sharma1, Mayank Singh Thakur2, Avinash Barala2
1Department of Computational Biology, Indraprastha Institute of Information Technology Delhi (IIIT-Delhi), Okhla Phase III, New Delhi, 110020, India; Infosys Centre for Artificial Intelligence, Indraprastha Institute of Information Technology Delhi (IIIT-Delhi), Okhla Phase III, New Delhi, 110020, India; Center of Excellence in Healthcare, Indraprastha Institute of Information Technology Delhi (IIIT-Delhi), Okhla Phase III, New Delhi, 110020, India.
International journal of biological macromolecules
|September 9, 2025
概括
MultiTox根据它们的作用方式对毒素蛋白进行分类,使用集体堆叠框架. 这种方法提高了对神经毒素,细胞毒素,血液毒素和肠毒素的蛋白质结构,功能和生物活性的理解.
科学领域:
- 蛋白质组学和生物信息学
- 结构生物学 结构生物学
- 毒理学 毒理学 毒理学
背景情况:
- 了解毒素蛋白多样性对于破译宏分子行为和生物活性至关重要.
- 传统的毒性预测方法对不同毒素的作用模式的洞察力有限.
研究的目的:
- 开发一个先进的计算框架,MultiTox,根据其分子机制对毒素蛋白进行分类.
- 使用序列数据区分神经毒素,细胞毒素,血液毒素和肠毒素.
主要方法:
- 策划了24,756个蛋白质的数据集,提取了高维的ESM-2嵌入.
- 采用一个双层集体堆叠框架,集成LGBM,MLP,ET,KNN,QDA和XGBoost分类器.
- 使用SHAP进行模型解释,并使用InterProScan进行功能注释.
主要成果:
- 实现了高整体准确性 (91.07%),F1得分 (90.73%) 和MCC (91.61%).
- 证明了强大的类型精度,特别是对血液毒素 (98.80%) 和肠毒素 (97.02%).
- 识别了与结构图案,疏水性,溶剂可访问性,折叠性,定位性和宿主相互作用有关的特定类别特征.
结论:
- MultiTox提供了一种可扩展和可解释的毒素蛋白质分类方法,将序列数据与功能洞察联系起来.
- 该框架为研究蛋白质结构功能关系的研究人员提供了宝贵的工具,并开发了有针对性的干预措施.
- 一个公共的网络服务器可用于实时预测,促进更广泛的科学应用.
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