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首页> 外文期刊>Analytical Biochemistry: An International Journal of Analytical and Preparative Methods >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: Identifying lysine succinylation sites in proteins by incorporating sequence-coupling effects into pseudo components and optimizing imbalanced training dataset

机译:isuc-pseopt:通过将序列耦合效应掺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

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摘要

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.
机译:琥珀酰化是一种后期改变的修饰(PTM),其中将琥珀酰基加入蛋白质分子的Lys(K)残基中。赖氨酸琥珀酰化在策划各种生物过程中起着重要作用,但它也与某些疾病有关。因此,我们从基础研究和药物开发中受到以下问题的挑战:给定含有许多Lys残留物的无声蛋白质序列,其中一个可以是琥珀酰化,哪一个不能?随着在后一组年龄中产生的蛋白质序列的雪崩,问题的答案已经变得更加紧迫。幸运的是,蛋白质中琥珀酰化位点的统计显着性实验数据最近可用,用于开发计算方法以解决这个问题的必不可少的先决条件。通过将序列偶联效应掺入通用伪氨基酸组合物中并使用KNNC(K-CORMALT邻居清洁)处理和IHT(插入假设训练样本)处理以优化训练数据集,已经开发了一种称为ISUC-PSEOPT的预测器。严格的交叉验证表明它非常优于现有方法。在http://www.jci-bioinfo.cnfisuc-pseopt中建立了一个用于ISUC-PSEopt的用户友好的Web-Server,其中用户可以轻松获得所需的结果,而无需通过所涉​​及的复杂数学方程式。 (c)2015 Elsevier Inc.保留所有权利。

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