对于复杂的生物系统而言,人工智能赋能扰动蛋白质组学
Liujia Qian1, Rui Sun1, Ruedi Aebersold2
1School of Medicine, Westlake University, Hangzhou, Zhejiang Province, China; Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China; Research Center for Industries of the Future, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China.
Cell genomics
|November 2, 2024
概括
扰动蛋白质组学通过测量扰动后的蛋白质变化,提供了一种全面的方法来理解生物系统. 这种方法与计算建模相结合,增强了系统生物学应用的预测能力.
科学领域:
- 蛋白质组学是指蛋白质组学.
- 系统生物学 系统生物学
- 计算生物学 计算生物学
背景情况:
- 有限的蛋白质水平扰动数据阻碍了系统生物学的采用.
- 扰动蛋白质组学通过整合各种测量来解决这个差距.
研究的目的:
- 介绍扰动蛋白质组学的逻辑,基本性和实用性.
- 提出一个通用的扰动,测量,建模到预测 (PMMP) 管道.
- 突出扰动蛋白质组学在促进生物学理解和预测建模方面的作用.
主要方法:
- 通过各种因素 (生物,化学,物理) 扰乱生物系统.
- 测量蛋白质变化 (表达,周转,PTM,相互作用,定位) 和表型数据.
- 应用计算模型 (机器学习,深度学习) 进行响应预测和功能识别.
主要成果:
- 开发一个通用的PMMP管道.
- 从大规模扰动蛋白质原子数据构建基础模型的潜力.
- 区分人工和自然扰乱系统的建模.
结论:
- 扰乱蛋白质组学对于推进系统生物学至关重要.
- 集成的数据和建模改善了对生物反应和功能的预测.
- 这种方法有助于治疗选择,化合物设计和实验优化.
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