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NR-2L: A Two-Level Predictor for Identifying Nuclear Receptor Subfamilies Based on Sequence-Derived Features

机译:NR-2L:基于序列派生特征识别核受体亚家族的二级预测器

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摘要

Nuclear receptors (NRs) are one of the most abundant classes of transcriptional regulators in animals. They regulate diverse functions, such as homeostasis, reproduction, development and metabolism. Therefore, NRs are a very important target for drug development. Nuclear receptors form a superfamily of phylogenetically related proteins and have been subdivided into different subfamilies due to their domain diversity. In this study, a two-level predictor, called NR-2L, was developed that can be used to identify a query protein as a nuclear receptor or not based on its sequence information alone; if it is, the prediction will be automatically continued to further identify it among the following seven subfamilies: (1) thyroid hormone like (NR1), (2) HNF4-like (NR2), (3) estrogen like, (4) nerve growth factor IB-like (NR4), (5) fushi tarazu-F1 like (NR5), (6) germ cell nuclear factor like (NR6), and (7) knirps like (NR0). The identification was made by the Fuzzy K nearest neighbor (FK-NN) classifier based on the pseudo amino acid composition formed by incorporating various physicochemical and statistical features derived from the protein sequences, such as amino acid composition, dipeptide composition, complexity factor, and low-frequency Fourier spectrum components. As a demonstration, it was shown through some benchmark datasets derived from the NucleaRDB and UniProt with low redundancy that the overall success rates achieved by the jackknife test were about 93% and 89% in the first and second level, respectively. The high success rates indicate that the novel two-level predictor can be a useful vehicle for identifying NRs and their subfamilies. As a user-friendly web server, NR-2L is freely accessible at either or . Each job submitted to NR-2L can contain up to 500 query protein sequences and be finished in less than 2 minutes. The less the number of query proteins is, the shorter the time will usually be. All the program codes for NR-2L are available for non-commercial purpose upon request.
机译:核受体(NRs)是动物中最丰富的转录调节因子之一。它们调节多种功能,例如体内平衡,繁殖,发育和新陈代谢。因此,NRs是药物开发的重要目标。核受体形成了系统发育相关蛋白的超家族,由于其结构域多样性,已细分为不同的亚家族。在这项研究中,开发了一种称为NR-2L的两级预测因子,可用于仅基于序列信息将查询蛋白识别为核受体,也可以不将其识别为核受体。如果是,则预测将自动继续,以在以下七个亚家族中进一步识别该预测:(1)甲状腺激素样(NR1),(2)HNF4类样(NR2),(3)雌激素样,(4)神经生长因子IB类(NR4),(5)fushi tarazu-F1类(NR5),(6)生殖细胞核因子类(NR6)和( 7 )杀伤性类(NR0)。鉴定是由模糊K最近邻(FK-NN)分类器根据假氨基酸组成进行确定的,该假氨基酸组成是通过结合蛋白质序列的各种理化和统计特征(例如氨基酸组成,二肽组成,复杂性因子和低频傅立叶频谱分量。作为演示,通过从低冗余的NucleaRDB和UniProt衍生出的一些基准数据集显示,在第一级和第二级中,通过折刀测试获得的总体成功率分别约为93%和89%。高成功率表明,新颖的两级预测因子可以成为识别NR及其亚家族的有用工具。作为用户友好的Web服务器, NR-2L 可通过或免费访问。提交给 NR-2L 的每个作业最多可以包含500个查询蛋白序列,并且可以在不到2分钟的时间内完成。查询蛋白的数量越少,通常所需时间越短。根据要求,所有用于 NR-2L 的程序代码都可以用于非商业目的。

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