PredPhos: an ensemble framework for structure-based prediction of phosphorylation sites.

IF 1.9 3区 生物学 Q2 BIOLOGY
Journal of Biological Research-Thessaloniki Pub Date : 2016-07-04 eCollection Date: 2016-05-01 DOI:10.1186/s40709-016-0042-y
Yong Gao, Weilin Hao, Jing Gu, Diwei Liu, Chao Fan, Zhigang Chen, Lei Deng
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引用次数: 8

Abstract

Background: Post-translational modifications (PTMs) occur on almost all proteins and often strongly affect the functions of modified proteins. Phosphorylation is a crucial PTM mechanism with important regulatory functions in biological systems. Identifying the potential phosphorylation sites of a target protein may increase our understanding of the molecular processes in which it takes part.

Results: In this paper, we propose PredPhos, a computational method that can accurately predict both kinase-specific and non-kinase-specific phosphorylation sites by using optimally selected properties. The optimal combination of features was selected from a set of 153 novel structural neighborhood properties by a two-step feature selection method consisting of a random forest algorithm and a sequential backward elimination method. To overcome the imbalanced problem, we adopt an ensemble method, which combines bootstrap resampling technique, support vector machine-based fusion classifiers and majority voting strategy. We evaluate the proposed method using both tenfold cross validation and independent test. Results show that our method achieves a significant improvement on the prediction performance for both kinase-specific and non-kinase-specific phosphorylation sites.

Conclusions: The experimental results demonstrate that the proposed method is quite effective in predicting phosphorylation sites. Promising results are derived from the new structural neighborhood properties, the novel way of feature selection, as well as the ensemble method.

Abstract Image

Abstract Image

Abstract Image

PredPhos:一个基于结构预测磷酸化位点的集成框架。
翻译后修饰(Post-translational modification, PTMs)发生在几乎所有的蛋白质上,并且常常强烈地影响被修饰蛋白质的功能。磷酸化是一个重要的PTM机制,在生物系统中具有重要的调控功能。确定目标蛋白的潜在磷酸化位点可能会增加我们对其参与的分子过程的理解。结果:在本文中,我们提出了PredPhos,这是一种计算方法,可以通过最优选择的性质准确预测激酶特异性和非激酶特异性磷酸化位点。采用随机森林算法和顺序反向消去法两步特征选择方法,从153个新结构邻域属性中选择最优特征组合。为了克服不平衡问题,我们采用了一种集成方法,该方法结合了自举重采样技术、基于支持向量机的融合分类器和多数投票策略。我们使用十倍交叉验证和独立检验来评估所提出的方法。结果表明,我们的方法对激酶特异性和非激酶特异性磷酸化位点的预测性能都有显著提高。结论:实验结果表明,该方法对磷酸化位点的预测是非常有效的。新的结构邻域特性、新的特征选择方法以及集成方法都取得了令人满意的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.20
自引率
0.00%
发文量
0
审稿时长
>12 weeks
期刊介绍: Journal of Biological Research-Thessaloniki is a peer-reviewed, open access, international journal that publishes articles providing novel insights into the major fields of biology. Topics covered in Journal of Biological Research-Thessaloniki include, but are not limited to: molecular biology, cytology, genetics, evolutionary biology, morphology, development and differentiation, taxonomy, bioinformatics, physiology, marine biology, behaviour, ecology and conservation.
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