NRPreTo: A Machine Learning-Based Nuclear Receptor and Subfamily Prediction Tool.
NRPreTo: A Machine Learning-Based Nuclear Receptor and Subfamily Prediction Tool.
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DOI：
10.1021/acsomega.3c00286
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发表时间：
2023-06-13
期刊：
影响因子：
4.1
通讯作者：
Bozdag S
中科院分区：
文献类型：
--
作者：
Madugula SS;Pandey S;Amalapurapu S;Bozdag S
核受体（NR）超家族包括系统发育相关的配体活化蛋白，在各种细胞活动中发挥关键作用。NR蛋白根据其功能、机制和相互作用配体的性质被细分为七个亚家族。开发强大的工具来识别NR可以深入了解它们在疾病途径中的功能关系和参与。现有的NR预测工具仅使用几种基于序列的特征，并且在相对相似的独立数据集上进行测试；因此，当扩展到新属序列时，它们可能遭受过拟合。为了解决这个问题，我们开发了核受体预测工具（NRPreTo），这是一个具有独特训练方法的两级NR预测工具，除了现有NR预测工具使用的基于序列的特征外，还利用了六个额外的特征组来描述蛋白质的各种物理化学，结构和进化特征。NRPreTo的第一级允许成功预测查询蛋白为NR或非NR，并在第二级进一步将蛋白质亚分类为七个NR亚家族之一。我们开发了随机森林分类器来测试基准数据集，以及来自RefSeq和human protein Reference Database （HPRD）的整个人类蛋白质数据集。我们观察到，使用额外的特性组可以提高性能。我们还观察到NRPreTo在外部数据集上取得了高性能，并预测了人类蛋白质组中的59个新的rna。NRPreTo的源代码可以在。
The nuclear receptor (NR) superfamily includes phylogenetically related ligand-activated proteins, which play a key role in various cellular activities. NR proteins are subdivided into seven subfamilies based on their function, mechanism, and nature of the interacting ligand. Developing robust tools to identify NR could give insights into their functional relationships and involvement in disease pathways. Existing NR prediction tools only use a few types of sequence-based features and are tested on relatively similar independent datasets; thus, they may suffer from overfitting when extended to new genera of sequences. To address this problem, we developed Nuclear Receptor Prediction Tool (NRPreTo), a two-level NR prediction tool with a unique training approach where in addition to the sequence-based features used by existing NR prediction tools, six additional feature groups depicting various physiochemical, structural, and evolutionary features of proteins were utilized. The first level of NRPreTo allows for the successful prediction of a query protein as NR or non-NR and further subclassifies the protein into one of the seven NR subfamilies in the second level. We developed Random Forest classifiers to test on benchmark datasets, as well as the entire human protein datasets from RefSeq and Human Protein Reference Database (HPRD). We observed that using additional feature groups improved the performance. We also observed that NRPreTo achieved high performance on the external datasets and predicted 59 novel NRs in the human proteome. The source code of NRPreTo is publicly available at .
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