Accurate prediction of nuclear receptors with conjoint triad feature.
Accurate prediction of nuclear receptors with conjoint triad feature.
复制标题
利用联合三联体特征准确预测核受体
DOI：
10.1186/s12859-015-0828-1
复制
发表时间：
2015-12-03
影响因子：
3
通讯作者：
Hu X
中科院分区：
文献类型：
--
作者：
Wang H;Hu X
背景核受体（Nuclear receptor，NRs）是一个配体诱导的转录因子大家族，参与调控胚胎发生、稳态、细胞生长和死亡等多种生理过程中的基因表达。这些核受体相关通路是上市药物的重要靶点。因此，一个可靠的计算模型的设计，用于预测从氨基酸序列的NR现在已经成为一个重要的生物医学problem.ResultsConjoint triad功能（CTF）主要考虑相邻关系的蛋白质序列编码每个蛋白质序列使用的黑社会（连续三个氨基酸）的频率分布提取的7个字母的简化字母表。此外，混沌博弈表示（CGR）可以研究隐藏在蛋白质序列中的模式，并直观地揭示以前未知的结构。本文采用CTF法、CGR法、氨基酸组成法（AAC）对蛋白质样品进行了定量分析。通过考虑三种方法的不同组合，我们研究了七组特征，每组特征都通过10倍交叉验证测试进行评估。同时，基于最新的NucleaRDB数据库，构建了一个包含474个NR序列和500个非NR序列的新的非冗余数据集。对比数值实验结果，CTF和AAC的组合特征组在识别NR和non-NR时获得了最好的结果，准确率为96.30%。此外，如果它被归类为NR，它将被进一步放入第二个级别，该级别将NR归类为八个主要子家族之一。在第二级，CTF和AAC的组合特征组也获得了最好的准确率为94.73%。最后，将该方法与现有的两种预测方法进行了比较，结果表明，两种预测方法的预测精度均显著提高，达到了98.79（NR-2L：92.56%; iNR-PhysChem：98.18%;第一水平）和93.71%（NR-2L：88.68%; iNR-PhysChem：92.45%;第二水平）。最后，通过统计显著性检验分析CTF特征的每个组成部分，并且仅使用所产生的前50个显著特征的简化模型实现了95.28%.ConclusionsThe实验结果表明，我们的基于CTF的方法是预测核受体蛋白的有效方法。此外，通过统计显著性检验获得的前50个显著特征被认为是基于相对重要性分析预测NR的“内在特征”。
BackgroundNuclear receptors (NRs) form a large family of ligand-inducible transcription factors that regulate gene expressions involved in numerous physiological phenomena, such as embryogenesis, homeostasis, cell growth and death. These nuclear receptors-related pathways are important targets of marketed drugs. Therefore, the design of a reliable computational model for predicting NRs from amino acid sequence has now been a significant biomedical problem.ResultsConjoint triad feature (CTF) mainly considers neighbor relationships in protein sequences by encoding each protein sequence using the triad (continuous three amino acids) frequency distribution extracted from a 7-letter reduced alphabet. In addition, chaos game representation (CGR) can investigate the patterns hidden in protein sequences and visually reveal previously unknown structure. In this paper, three methods, CTF, CGR, amino acid composition (AAC), are applied to formulate the protein samples. By considering different combinations of three methods, we study seven groups of features, and each group is evaluated by the 10-fold cross-validation test. Meanwhile, a new non-redundant dataset containing 474 NR sequences and 500 non-NR sequences is built based on the latest NucleaRDB database. Comparing the results of numerical experiments, the group of combined features with CTF and AAC gets the best result with the accuracy of 96.30 % for identifying NRs from non-NRs. Moreover, if it is classified as a NR, it will be further put into the second level, which will classify a NR into one of the eight main subfamilies. At the second level, the group of combined features with CTF and AAC also gets the best accuracy of 94.73 %. Subsequently, the proposed predictor is compared with two existing methods, and the comparisons show that the accuracies of two levels significantly increase to 98.79 % (NR-2L: 92.56 %; iNR-PhysChem: 98.18 %; the first level) and 93.71 % (NR-2L: 88.68 %; iNR-PhysChem: 92.45 %; the second level) with the introduction of our CTF-based method. Finally, each component of CTF features is analyzed via the statistical significant test, and a simplified model only with the resulting top-50 significant features achieves accuracy of 95.28 %.ConclusionsThe experimental results demonstrate that our CTF-based method is an effective way for predicting nuclear receptor proteins. Furthermore, the top-50 significant features obtained from the statistical significant test are considered as the “intrinsic features” in predicting NRs based on the analysis of relative importance.