蛋白质组
仿形(计算机编程)
计算生物学
跨膜蛋白
生物
萧条(经济学)
神经科学
生物信息学
受体
计算机科学
遗传学
操作系统
宏观经济学
经济
作者
Shanshan Li,Huoqing Luo,Ronghui Lou,Cuiping Tian,Miao Chen,Lisha Xia,Chen Pan,Xiaoxiao Duan,Ting Dang,Hui Li,Chengyu Fan,Pan Tang,Zhuangzhuang Zhang,Yan Liu,Yunxia Li,Fei Xu,Yaoyang Zhang,Guisheng Zhong,Ji Hu,Wenqing Shui
出处
期刊:Science Advances
[American Association for the Advancement of Science]
日期:2021-07-21
卷期号:7 (30)
被引量:27
标识
DOI:10.1126/sciadv.abf0634
摘要
Transmembrane proteins play vital roles in mediating synaptic transmission, plasticity, and homeostasis in the brain. However, these proteins, especially the G protein-coupled receptors (GPCRs), are underrepresented in most large-scale proteomic surveys. Here, we present a new proteomic approach aided by deep learning models for comprehensive profiling of transmembrane protein families in multiple mouse brain regions. Our multiregional proteome profiling highlights the considerable discrepancy between messenger RNA and protein distribution, especially for region-enriched GPCRs, and predicts an endogenous GPCR interaction network in the brain. Furthermore, our new approach reveals the transmembrane proteome remodeling landscape in the brain of a mouse depression model, which led to the identification of two previously unknown GPCR regulators of depressive-like behaviors. Our study provides an enabling technology and rich data resource to expand the understanding of transmembrane proteome organization and dynamics in the brain and accelerate the discovery of potential therapeutic targets for depression treatment.
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