Comprehensive comparative analysis and identification of RNA-binding protein domains: multi-class classification and feature selection.
Comprehensive comparative analysis and identification of RNA-binding protein domains: multi-class classification and feature selection.
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DOI：
10.1016/j.jtbi.2012.07.013
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发表时间：
2012-11-07
影响因子：
2
通讯作者：
Zhi, Degui
中科院分区：
文献类型：
--
作者：
Jahandideh, Samad;Srinivasasainagendra, Vinodh;Zhi, Degui
关键词：
RNA-蛋白质相互作用在蛋白质合成、基因调控、转录后基因调控、可变剪接和RNA病毒感染等多种细胞过程中起着重要作用。在这项研究中，使用基因本体论注释（果阿）和蛋白质结构分类（SCOP）数据库的自动程序设计捕获结构解决RNA结合蛋白结构域在不同的子类。随后，我们应用调优多类SVM（TMCSVM），随机森林（RF），和多类RMB 1/RMBq-正则化逻辑回归（MCLR）的分析和分类RNA结合蛋白结构域的基础上，一套全面的序列和结构特征。在这项研究中，我们比较了三种不同的最先进的预测方法的预测精度。从我们的研究结果，TMCSVM优于其他方法，并建议TMCSVM作为一个有用的工具，促进RNA结合蛋白质结构域的多类预测的潜力。另一方面，MCRLR通过阐明特征对RNA结合蛋白质结构域亚类预测准确性的贡献的重要性，帮助我们提供一些生物学见解，以了解序列和结构在蛋白质-RNA相互作用中的作用。
RNA-protein interaction plays an important role in various cellular processes, such as protein synthesis, gene regulation, post-transcriptional gene regulation, alternative splicing, and infections by RNA viruses. In this study, using Gene Ontology Annotated (GOA) and Structural Classification of Proteins (SCOP) databases an automatic procedure was designed to capture structurally solved RNA-binding protein domains in different subclasses. Subsequently, we applied tuned multi-class SVM (TMCSVM), Random Forest (RF), and multi-class ℓ1/ℓq-regularized logistic regression (MCRLR) for analysis and classifying RNA-binding protein domains based on a comprehensive set of sequence and structural features. In this study, we compared prediction accuracy of three different state-of-the-art predictor methods. From our results, TMCSVM outperforms the other methods and suggests the potential of TMCSVM as a useful tool for facilitating the multi-class prediction of RNA-binding protein domains. On the other hand, MCRLR by elucidating importance of features for their contribution in predictive accuracy of RNA-binding protein domains subclasses, helps us to provide some biological insights into the roles of sequences and structures in protein–RNA interactions.
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