Computer Science Department, Stanford University, Stanford, CA, USA.
Bioengineering Department, Stanford University, Stanford, CA, USA.
Nat Commun. 2021 Mar 19;12(1):1796. doi: 10.1038/s41467-021-21770-8.
Most diseases disrupt multiple proteins, and drugs treat such diseases by restoring the functions of the disrupted proteins. How drugs restore these functions, however, is often unknown as a drug's therapeutic effects are not limited to the proteins that the drug directly targets. Here, we develop the multiscale interactome, a powerful approach to explain disease treatment. We integrate disease-perturbed proteins, drug targets, and biological functions into a multiscale interactome network. We then develop a random walk-based method that captures how drug effects propagate through a hierarchy of biological functions and physical protein-protein interactions. On three key pharmacological tasks, the multiscale interactome predicts drug-disease treatment, identifies proteins and biological functions related to treatment, and predicts genes that alter a treatment's efficacy and adverse reactions. Our results indicate that physical interactions between proteins alone cannot explain treatment since many drugs treat diseases by affecting the biological functions disrupted by the disease rather than directly targeting disease proteins or their regulators. We provide a general framework for explaining treatment, even when drugs seem unrelated to the diseases they are recommended for.
大多数疾病会扰乱多种蛋白质，而药物通过恢复被扰乱的蛋白质的功能来治疗这些疾病。然而，由于药物的治疗效果不仅限于药物直接靶向的蛋白质，因此药物如何恢复这些功能通常是未知的。在这里，我们开发了多尺度互作组学，这是一种强大的解释疾病治疗的方法。我们将疾病扰乱的蛋白质、药物靶点和生物学功能整合到一个多尺度互作组学网络中。然后，我们开发了一种基于随机游走的方法，该方法可以捕捉药物效应如何通过生物学功能和物理蛋白质-蛋白质相互作用的层次结构传播。在三个关键的药理学任务中，多尺度互作组学预测了药物-疾病的治疗效果，识别了与治疗相关的蛋白质和生物学功能，并预测了改变治疗效果和不良反应的基因。我们的结果表明，仅蛋白质之间的物理相互作用不能解释治疗效果，因为许多药物通过影响疾病扰乱的生物学功能来治疗疾病，而不是直接针对疾病蛋白或其调节剂。我们提供了一个通用的框架来解释治疗效果，即使药物与它们推荐用于治疗的疾病似乎没有关系。
