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, MD 21201, USA.
bioRxiv : the preprint server for biology
|September 4, 2023
概括
机器学习模型准确地识别了可共聚结合的部位,加速了针对性的共聚抑制剂设计. 这项工作通过预测新药发现的囊蛋白反应性来扩大可药性蛋白质组.
科学领域:
- 计算化学和结构生物学
- 药物发现和药物化学
- 生物信息学和机器学习
背景情况:
- 向的共价抑制剂为药物开发提供了一个有希望的途径,但确定合适的可结合部位仍然是一个挑战.
- 扩大可用药的蛋白质组需要新的方法来识别潜在的药物标.
- 机器学习 (ML) 方法有可能加速这些网站的识别.
研究的目的:
- 开发和验证可靠的机器学习模型,用于识别蛋白质中可共结合的位点.
- 为培训和测试ML模型创建一个有关联的半氨酸综合数据库.
- 为共价药物发现社区提供工具和资源.
主要方法:
- 策划了一种新型数据库 (LigCys3D),包含使用PDB结构的近800种蛋白质的1000多种联结型半氨酸.
- 在LigCys3D数据库上开发和训练基于树的模型和卷积神经网络 (CNN).
- 使用未见的测试数据和AlphaFold2预测结构的验证模型.
主要成果:
- 在未见数据上,树模型的AUC为94%,CNN的AUC为93%,实现了高性能.
- 在使用ML模型对预测结构进行回顾时,已证明超过90%的回忆率.
- 在包括PH和SH2域在内的392个人体激酶中确定并报告了可结合的预测性囊蛋白.
结论:
- 在精选数据上训练的ML模型可以可靠地识别共可结合的氨酸.
- 开发的工具 (LigCys3D数据库和DeepCys网络服务器) 将帮助研究人员发现共价药物.
- 这项工作代表了向下一代药物发现人类蛋白质组的ML驱动注释迈出的重要一步.
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