Receptor Pharmacology Unit, National Institute on Aging, Baltimore, Maryland, USA.
Prog Mol Biol Transl Sci. 2013;118:431-67. doi: 10.1016/B978-0-12-394440-5.00017-6.
To fully appreciate the diversity and specificity of complex cellular signaling events, such as arrestin-mediated signaling from G protein-coupled receptor activation, a complex systems-level investigation currently appears to be the best option. A rational combination of transcriptomics, proteomics, and interactomics, all coherently integrated with applied next-generation bioinformatics, is vital for the future understanding of the development, translation, and expression of GPCR-mediated arrestin signaling events in physiological contexts. Through a more nuanced, systems-level appreciation of arrestin-mediated signaling, the creation of arrestin-specific molecular response "signatures" should be made simple and ultimately amenable to drug discovery processes. Arrestin-based signaling paradigms possess important aspects, such as its specific temporal kinetics and ability to strongly affect transcriptional activity, that make it an ideal test bed for next-generation of drug discovery bioinformatic approaches such as multi-parallel dose-response analysis, data texturization, and latent semantic indexing-based natural language data processing and feature extraction.
为了充分了解复杂细胞信号事件的多样性和特异性，例如 G 蛋白偶联受体激活引起的 arrestin 介导的信号转导，目前看来，采用复杂的系统级研究是最佳选择。将转录组学、蛋白质组学和相互作用组学进行合理组合，并与应用下一代生物信息学进行整合，对于未来理解 GPCR 介导的 arrestin 信号转导事件在生理环境中的发展、转化和表达至关重要。通过更细致、系统的方式来理解 arrestin 介导的信号转导，应该可以简单地创建 arrestin 特异性分子反应“特征”，并最终适用于药物发现过程。基于 arrestin 的信号转导范式具有重要方面，例如其特定的时变动力学和强烈影响转录活性的能力，使其成为下一代药物发现生物信息学方法的理想试验台，例如多平行剂量反应分析、数据纹理化和基于潜在语义索引的自然语言数据处理和特征提取。
