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通过将 k 间隔氨基酸对纳入周元的通用伪氨基酸组成来预测瓜氨酸化位点。

Prediction of citrullination sites by incorporating k-spaced amino acid pairs into Chou's general pseudo amino acid composition.

机构信息

College of Science, Shenyang Aerospace University, 110136, China.

College of Science, Shenyang Aerospace University, 110136, China.

出版信息

Gene. 2018 Jul 20;664:78-83. doi: 10.1016/j.gene.2018.04.055. Epub 2018 Apr 23.

DOI:10.1016/j.gene.2018.04.055
PMID:29694908
Abstract

As one of the most important and common protein post-translational modifications, citrullination plays a key role in regulating various biological processes and is associated with several human diseases. The accurate identification of citrullination sites is crucial for elucidating the underlying molecular mechanisms of citrullination and designing drugs for related human diseases. In this study, a novel bioinformatics tool named CKSAAP_CitrSite is developed for the prediction of citrullination sites. With the assistance of support vector machine algorithm, the highlight of CKSAAP_CitrSite is to adopt the composition of k-spaced amino acid pairs surrounding a query site as input. As illustrated by 10-fold cross-validation, CKSAAP_CitrSite achieves a satisfactory performance with a Sensitivity of 77.59%, a Specificity of 95.26%, an Accuracy of 89.37% and a Matthew's correlation coefficient of 0.7566, which is much better than those of the existing prediction method. Feature analysis shows that the N-terminal space containing pairs may play an important role in the prediction of citrullination sites, and the arginines close to N-terminus tend to be citrullinated. The conclusions derived from this study could offer useful information for elucidating the molecular mechanisms of citrullination and related experimental validations. A user-friendly web-server for CKSAAP_CitrSite is available at 123.206.31.171/CKSAAP_CitrSite/.

摘要

作为最重要和最常见的蛋白质翻译后修饰之一,瓜氨酸化在调节各种生物过程中起着关键作用,并与几种人类疾病有关。准确识别瓜氨酸化位点对于阐明瓜氨酸化的潜在分子机制和设计相关人类疾病的药物至关重要。在这项研究中,开发了一种名为 CKSAAP_CitrSite 的新型生物信息学工具,用于预测瓜氨酸化位点。在支持向量机算法的辅助下,CKSAAP_CitrSite 的突出特点是采用查询位点周围的 k 间隔氨基酸对的组成作为输入。通过 10 倍交叉验证表明,CKSAAP_CitrSite 的性能令人满意,灵敏度为 77.59%,特异性为 95.26%,准确性为 89.37%,马修相关系数为 0.7566,明显优于现有预测方法。特征分析表明,N 端空间中包含的对可能在瓜氨酸化位点的预测中发挥重要作用,靠近 N 端的精氨酸倾向于被瓜氨酸化。本研究得出的结论可为阐明瓜氨酸化的分子机制和相关的实验验证提供有用的信息。CKSAAP_CitrSite 的用户友好型网络服务器可在 123.206.31.171/CKSAAP_CitrSite/ 上获得。

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