机器学习模型用于询问针对囊蛋白的全蛋白体共价结合性
Ruibin Liu1, Joseph Clayton1,2, Mingzhe Shen1
1Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland 21201, United States.
JACS Au
|April 26, 2024
概括
机器学习模型准确地识别了可结合的氨酸氨酸位点,用于向的共价药物设计. 这加快了发现速度,并通过预测蛋白质中的反应性位点来扩大可用药物的蛋白质组.
科学领域:
- 计算化学是一种计算化学.
- 结构生物学是结构生物学.
- 药物发现 药物发现
背景情况:
- 识别可共结合的部位对于有针对性的共抑制剂设计至关重要.
- 扩大可用药的蛋白质组需要新的位点识别方法.
研究的目的:
- 开发和验证机器学习模型,用于识别可共结合的氨酸氨酸位.
- 为共价药物发现创建一个全面的数据库和预测服务器.
主要方法:
- 在精心策划的LigCys3D数据库上训练基于树的模型和卷积神经网络 (CNN).
- 使用来自蛋白质数据库的10,000多个3D蛋白质结构.
- 使用未见的测试数据和AlphaFold2预测结构验证模型.
主要成果:
- 实现了 94% 和 93% 的接收机下面面积,分别为树模型和 CNN 的运行特征曲线.
- 通过使用预测结构,在复合联结囊蛋白中显示了90%以上的回忆.
- 在392个人体激酶中预测可结合的囊蛋白,包括特定的域位置.
结论:
- 机器学习模型有效地识别可结合的半氨酸,加速向的共价抑制剂设计.
- LigCys3D数据库和DeepCys网络服务器为药物发现社区提供了宝贵的资源.
- 这项工作推动了以机器学习为主导的基因组和结构数据的整合,用于下一代药物发现.
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