DF-Phos: Prediction of Protein Phosphorylation Sites by Deep Forest.

IF 2.1 4区 生物学 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY Journal of biochemistry Pub Date : 2024-03-25 DOI:10.1093/jb/mvad116
Zeynab Zahiri, Nasser Mehrshad, Maliheh Mehrshad
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Abstract

Phosphorylation is the most important and studied post-translational modification (PTM), which plays a crucial role in protein function studies and experimental design. Many significant studies have been performed to predict phosphorylation sites using various machine-learning methods. Recently, several studies have claimed that deep learning-based methods are the best way to predict the phosphorylation sites because deep learning as an advanced machine learning method can automatically detect complex representations of phosphorylation patterns from raw sequences and thus offers a powerful tool to improve phosphorylation site prediction. In this study, we report DF-Phos, a new phosphosite predictor based on the Deep Forest to predict phosphorylation sites. In DF-Phos, the feature vector taken from the CkSAApair method is as input for a Deep Forest framework for predicting phosphorylation sites. The results of 10-fold cross-validation show that the Deep Forest method has the highest performance among other available methods. We implemented a Python program of DF-Phos, which is freely available for non-commercial use at https://github.com/zahiriz/DF-Phos Moreover, users can use it for various PTM predictions.

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DF-Phos:深林预测蛋白质磷酸化位点
磷酸化是最重要的翻译后修饰(PTM),在蛋白质功能研究和实验设计中起着至关重要的作用。利用各种机器学习方法预测磷酸化位点已经开展了许多重要研究。最近,一些研究称,基于深度学习的方法是预测磷酸化位点的最佳方法,因为深度学习作为一种先进的机器学习方法,可以从原始序列中自动检测磷酸化模式的复杂表征,从而为改进磷酸化位点预测提供了强有力的工具。在本研究中,我们报告了一种基于深林预测磷酸化位点的新型磷酸化位点预测器--DF-Phos。在 DF-Phos 中,从 CkSAApair 方法中提取的特征向量是预测磷酸化位点的深林框架的输入。10 倍交叉验证的结果表明,在其他可用方法中,深林方法的性能最高。我们实现了 DF-Phos 的 Python 程序,该程序可在 https://github.com/zahiriz/DF-Phos 免费用于非商业目的,而且用户可以用它进行各种 PTM 预测。
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
Journal of biochemistry
Journal of biochemistry 生物-生化与分子生物学
CiteScore
4.80
自引率
3.70%
发文量
101
审稿时长
4-8 weeks
期刊介绍: The Journal of Biochemistry founded in 1922 publishes the results of original research in the fields of Biochemistry, Molecular Biology, Cell, and Biotechnology written in English in the form of Regular Papers or Rapid Communications. A Rapid Communication is not a preliminary note, but it is, though brief, a complete and final publication. The materials described in Rapid Communications should not be included in a later paper. The Journal also publishes short reviews (JB Review) and papers solicited by the Editorial Board.
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