顶级CysteineDB:一个囊蛋白组范围的数据库,集成结构和化学蛋白质组学数据,用于预测囊蛋白结合性
Michele Bonus1, Julian Greb1, Jaimeen D Majmudar2
1Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf 40225 Düsseldorf, Germany.
Journal of molecular biology
|May 10, 2025
概括
TopCysteineDB集成了结构和化学蛋白质组学数据,以绘制氨酸结合性的地图. 本资源有助于设计向的共价抑制剂,通过为囊位提供统一的视图和预测模型.
科学领域:
- 生物化学 生化学
- 结构生物学 结构生物学
- 药物发现 药物发现 药物发现
背景情况:
- 向共价抑制剂在药物设计中至关重要,其中囊因其反应性而成为关键标.
- 现有的结构和化学蛋白质组学数据关于氨酸的结合性往往被断开,阻碍了全面的分析.
- 需要一个统一的资源来整合这些不同的数据类型,以改善药物开发.
研究的目的:
- 开发TopCysteineDB,一个全面的数据库,将蛋白质数据库 (PDB) 的结构信息与基于活动的蛋白质概况 (ABPP) 化学蛋白质组数据集成在一起.
- 系统地识别和分类氨酸的功能作用,包括共价变异,金属结合,辅因子结合和二硫化键.
- 创建TopCySPAL,一个机器学习模型,通过整合结构和蛋白质组学数据来预测氨酸结合性.
主要方法:
- 从PDB收集和分析了264,234种独特的氨酸的结构数据.
- 在人类蛋白质组中,整合了41,898种可检测的囊蛋白的化学蛋白质组学数据.
- 开发了一种自动化分类管道 (TopCovPDB) 与手动策划,以识别和分类囊功能.
- 训练了一种机器学习模型 (TopCySPAL),使用集成的结构特征和蛋白质组学数据.
主要成果:
- 确定了787种共价半氨酸,并对其他功能性作用进行了分类.
- 建立了结构和蛋白质组学数据之间的交叉引用,创造了对氨酸结合性的统一观点.
- 在氨酸结合性方面,TopCySPAL获得了高预测性能 (AUROC: 0.964,AUPRC: 0.914).
- 通过互动可视化和预测工具的Web界面 (TopCysteineDBApp) 可以访问TopCysteineDB和TopCySPAL.
结论:
- TopCysteineDB通过结合结构和化学蛋白质组数据,提供了前所未有的囊结合性综合视图.
- 开发的机器学习模型TopCySPAL准确地预测了氨酸的结合性,推进了向的共价抑制剂设计.
- 这一资源有助于系统地探索和预测囊基位反应,加速新疗法的开发.
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