SucStruct: Prediction of succinylated lysine residues by using structural properties of amino acids
SucStruct: Prediction of succinylated lysine residues by using structural properties of amino acids
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DOI：
10.1016/j.ab.2017.03.021
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发表时间：
2017-06-15
影响因子：
2.9
通讯作者：
Sharma, Alok
中科院分区：
文献类型：
--
作者：
Lopez, Yosvany;Dehzangi, Abdollah;Sharma, Alok
翻译后修饰（PTM）是一种有助于蛋白质组多样化的生物学反应。尽管许多修饰在细胞活性中具有重要作用，但赖氨酸琥珀酰化最近已成为重要的PTM标记。它改变赖氨酸的化学结构，导致蛋白质结构和功能的显着变化。与后基因组时代测序的大量蛋白质相比，琥珀酰化残基的实验检测仍然昂贵、低效且耗时。因此，开发计算工具，准确预测琥珀酰化赖氨酸是一个迫切的必要性。迄今为止，已经提出了几种方法，但据报道，它们的敏感性很差。在本文中，我们提出了一种方法，利用氨基酸的结构特征，以提高赖氨酸琥珀酰化预测。琥珀酰化和非琥珀酰化赖氨酸首先从670个蛋白质中检索，并将其特征如可及表面积、骨架扭转角和局部结构构象并入。我们使用k-最近邻清洗处理类不平衡，并设计了一个修剪的决策树分类。我们的预测器，被称为SucStruct（使用结构特征的琥珀酰化），被证明显着提高性能时，与以前的预测器相比，灵敏度，准确度和马修的相关系数等于0.7334-0.7946，0.7444-0.7608和0.4884-0.5240，分别。(C)2017爱思唯尔公司All rights reserved.
Post-Translational Modification (PTM) is a biological reaction which contributes to diversify the proteome. Despite many modifications with important roles in cellular activity, lysine succinylation has recently emerged as an important PTM mark. It alters the chemical structure of lysines, leading to remarkable changes in the structure and function of proteins. In contrast to the huge amount of proteins being sequenced in the post-genome era, the experimental detection of succinylated residues remains expensive, inefficient and time-consuming. Therefore, the development of computational tools for accurately predicting succinylated lysines is an urgent necessity. To date, several approaches have been proposed but their sensitivity has been reportedly poor. In this paper, we propose an approach that utilizes structural features of amino acids to improve lysine succinylation prediction. Succinylated and non-succinylated lysines were first retrieved from 670 proteins and characteristics such as accessible surface area, backbone torsion angles and local structure conformations were incorporated. We used the k-nearest neighbors cleaning treatment for dealing with class imbalance and designed a pruned decision tree for classification. Our predictor, referred to as SucStruct (Succinylation using Structural features), proved to significantly improve performance when compared to previous predictors, with sensitivity, accuracy and Mathew's correlation coefficient equal to 0.7334-0.7946, 0.7444-0.7608 and 0.4884-0.5240, respectively. (C) 2017 Elsevier Inc. All rights reserved.