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Identification Methods of G Protein-Coupled Receptors

机译:G蛋白偶联受体的鉴定方法

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摘要

The G protein-coupled receptors (GPCRs) include one of the largest and most important families of multifunctional proteins known to molecular biology. They play a key role in cell signaling networks that regulate many physiological processes, such as vision, smell, taste, neurotransmission, secretion, immune responses, metabolism, and cell growth. These proteins are thus very important for understanding human physiology and they are involved in several diseases. Therefore, many efforts in pharmaceutical research are to understand their structures and functions, which is not an easy task, because although thousands GPCR sequences are known, many of them remain orphans. To remedy this, many methods have been developed using methods such as statistics, machine learning algorithms, and bio-inspired approaches. In this article, the authors review the approaches used to develop algorithms for classification GPCRs by trying to highlight the strengths and weaknesses of these different approaches and providing a comparison of their performances.
机译:G蛋白偶联受体(GPCR)包括分子生物学已知的最大和最重要的多功能蛋白家族之一。它们在调节许多生理过程(例如视力,气味,味道,神经传递,分泌,免疫反应,新陈代谢和细胞生长)的细胞信号网络中起关键作用。因此,这些蛋白质对于理解人类生理学非常重要,并且涉及多种疾病。因此,药物研究的许多努力都是为了了解它们的结构和功能,这不是一件容易的事,因为尽管已知成千上万的GPCR序列,但其中许多仍是孤儿。为了解决这个问题,已经使用统计方法,机器学习算法和生物启发方法开发了许多方法。在本文中,作者试图通过突出显示这些不同方法的优缺点并对其性能进行比较,来回顾用于开发GPCR分类算法的方法。

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