G蛋白偶联受体的鉴定方法

Meriem Zekri, K. Alem, L. Souici-Meslati
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引用次数: 2

摘要

G蛋白偶联受体(gpcr)是分子生物学中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的细胞信号网络中发挥关键作用,如视觉、嗅觉、味觉、神经传递、分泌、免疫反应、代谢和细胞生长。因此,这些蛋白质对于理解人体生理学非常重要,它们与几种疾病有关。因此,药物研究中的许多努力都是为了了解它们的结构和功能,这不是一项容易的任务,因为尽管已知数千个GPCR序列,但其中许多仍然是孤儿。为了解决这个问题,已经开发了许多方法,如统计学、机器学习算法和生物启发方法。在本文中,作者回顾了用于开发gpcr分类算法的方法,试图突出这些不同方法的优缺点,并提供了它们的性能比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification Methods of G Protein-Coupled Receptors
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.
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