iSuc-PseOpt: Identifying lysine succinylation sites in proteins by incorporating sequence-coupling effects into pseudo components and optimizing imbalanced training dataset
iSuc-PseOpt: Identifying lysine succinylation sites in proteins by incorporating sequence-coupling effects into pseudo components and optimizing imbalanced training dataset
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iSuc-PseOpt：通过将序列耦合效应纳入伪组件并优化不平衡训练数据集来识别蛋白质中的赖氨酸琥珀酰化位点
DOI：
10.1016/j.ab.2015.12.009
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发表时间：
2016-03-15
影响因子：
2.9
通讯作者：
Chou, Kuo-Chen
中科院分区：
文献类型：
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作者：
Jia, Jianhua;Liu, Zi;Chou, Kuo-Chen
琥珀酰化是一种翻译后修饰（PTM），其中琥珀酰基被添加到蛋白质分子的Lys（K）残基上。赖氨酸琥珀酰化在协调各种生物学过程中起着重要作用，但它也与一些疾病有关。因此，我们面临着来自基础研究和药物开发的以下问题：给定含有许多Lys残基的未表征的蛋白质序列，其中哪一个可以琥珀酰化，哪一个不能？随着后基因组时代产生的大量蛋白质序列，这个问题的答案变得更加紧迫。幸运的是，蛋白质中琥珀酰化位点的统计显著性实验数据最近已经变得可用，这是开发解决这个问题的计算方法的不可或缺的先决条件。通过将序列偶联效应纳入一般的伪氨基酸组合物中，并使用KNNC（K-最近邻清洗）处理和IHTS（插入假设训练样本）处理来优化训练数据集，开发了一种称为iSuc-PseOpt的预测器。严格的交叉验证表明，它显着优于现有的方法。在http://www.jci-bioinfo.cnfiSuc-PseOpt上建立了一个用户友好的iSuc-PseOpt网络服务器，用户可以很容易地得到他们想要的结果，而不需要通过所涉及的复杂数学方程。(C)2015 Elsevier Inc. All rights reserved.
Succinylation is a posttranslational modification (PTM) where a succinyl group is added to a Lys (K) residue of a protein molecule. Lysine succinylation plays an important role in orchestrating various biological processes, but it is also associated with some diseases. Therefore, we are challenged by the following problem from both basic research and drug development: given an uncharacterized protein sequence containing many Lys residues, which one of them can be succinylated, and which one cannot? With the avalanche of protein sequences generated in the postgenomic age, the answer to the problem has become even more urgent. Fortunately, the statistical significance experimental data for succinylated sites in proteins have become available very recently, an indispensable prerequisite for developing a computational method to address this problem. By incorporating the sequence-coupling effects into the general pseudo amino acid composition and using KNNC (K-nearest neighbors cleaning) treatment and IHTS (inserting hypothetical training samples) treatment to optimize the training dataset, a predictor called iSuc-PseOpt has been developed. Rigorous cross-validations indicated that it remarkably outperformed the existing method. A user-friendly web-server for iSuc-PseOpt has been established at http://www.jci-bioinfo.cnfiSuc-PseOpt, where users can easily get their desired results without needing to go through the complicated mathematical equations involved. (C) 2015 Elsevier Inc. All rights reserved.