RF-Hydroxysite: a random forest based predictor for hydroxylation sites.
RF-Hydroxysite: a random forest based predictor for hydroxylation sites.
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DOI：
10.1039/c6mb00179c
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发表时间：
2016-07-19
影响因子：
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通讯作者：
Kc DB
中科院分区：
文献类型：
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作者：
Ismail HD;Newman RH;Kc DB
蛋白质羟基化是一种新兴的翻译后修饰，涉及正常细胞过程和越来越多的病理状态，包括几种癌症。蛋白质羟基化是由羟化酶家族成员介导的，该酶催化其目标底物上选定的赖氨酸或脯氨酸残基处的炔基转化为羟基。传统上，羟基化是使用昂贵且耗时的实验方法（例如串联质谱法）来鉴定的。因此，为了促进假定的羟基化位点的识别并补充现有的实验方法，最近开发了旨在预测蛋白质序列中羟基化位点的计算方法。基于这些努力，我们开发了一种称为 RF-Hydroxysite 的新方法，该方法仅使用一级氨基酸序列作为输入，使用随机森林来识别蛋白质中假定的羟赖氨酸和羟脯氨酸残基。 RF-Hydroxysite 将先前显示有助于羟基化位点预测的功能与我们发现可显着增强性能的几个新功能集成在一起。其中包括从蛋白质序列中捕获物理化学、结构、序列顺序和进化信息的特征。最终模型中使用的特征是根据它们对预测的贡献来选择的。研究发现物理化学信息对模型贡献最大。本研究还揭示了进化、序列顺序和蛋白质无序区域信息对羟基化位点预测的贡献。 RF-Hydroxysite 的网络服务器可在线获取：http://bcb.ncat.edu/RF_Hydroxy/。
Protein hydroxylation is an emerging posttranslational modification involved in both normal cellular processes and a growing number of pathological states, including several cancers. Protein hydroxylation is mediated by members of the hydroxylase family of enzymes, which catalyze the conversion of an alkyne group at select lysine or proline residues on their target substrates to a hydroxyl. Traditionally, hydroxylation has been identified using expensive and time-consuming experimental methods, such as tandem mass spectrometry. Therefore, to facilitate identification of putative hydroxylation sites and to complement existing experimental approaches, computational methods designed to predict the hydroxylation sites in protein sequences have recently been developed. Building on these efforts, we have developed a new method, termed RF-Hydroxysite, that uses random forest to identify putative hydroxylysine and hydroxyproline residues in proteins using only the primary amino acid sequence as input. RF-Hydroxysite integrates features previously shown to contribute to hydroxylation site prediction with several new features that we found to augment the performance remarkably. These include features that capture physicochemical, structural, sequence-order and evolutionary information from the protein sequences. The features used in the final model were selected based on their contribution to the prediction. Physicochemical information was found to contribute the most to the model. The present study also sheds light on the contribution of evolutionary, sequence order, and protein disordered region information to hydroxylation site prediction. The web server for RF-Hydroxysite is available online at http://bcb.ncat.edu/RF_hydroxy/.