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NPalmitoylDeep-PseAAC: A Predictor of N-Palmitoylation Sites in Proteins Using Deep Representations of Proteins and PseAAC via Modified 5-Steps Rule
Current Bioinformatics ( IF 4 ) Pub Date : 2021-01-31 , DOI: 10.2174/1574893615999200605142828
Sheraz Naseer 1 , Waqar Hussain 2 , Yaser Daanial Khan 1 , Nouman Rasool 3
Affiliation  

Background: Among all the major Post-translational modification, lipid modifications possess special significance due to their widespread functional importance in eukaryotic cells. There exist multiple types of lipid modifications and Palmitoylation, among them, is one of the broader types of modification, having three different types. The N-Palmitoylation is carried out by attachment of palmitic acid to an N-terminal cysteine. Due to the association of N-Palmitoylation with various biological functions and diseases such as Alzheimer’s and other neurodegenerative diseases, its identification is very important.

Objective: The in vitro, ex vivo and in vivo identification of Palmitoylation is laborious, time-taking and costly. There is a dire need for an efficient and accurate computational model to help researchers and biologists identify these sites, in an easy manner. Herein, we propose a novel prediction model for the identification of N-Palmitoylation sites in proteins.

Methods: The proposed prediction model is developed by combining the Chou’s Pseudo Amino Acid Composition (PseAAC) with deep neural networks. We used well-known deep neural networks (DNNs) for both the tasks of learning a feature representation of peptide sequences and developing a prediction model to perform classification.

Results: Among different DNNs, Gated Recurrent Unit (GRU) based RNN model showed the highest scores in terms of accuracy, and all other computed measures, and outperforms all the previously reported predictors.

Conclusion: The proposed GRU based RNN model can help to identify N-Palmitoylation in a very efficient and accurate manner which can help scientists understand the mechanism of this modification in proteins.



中文翻译:

NPalmitoylDeep-PseAAC:使用蛋白质和PseAAC的深层表示法通过修饰的5步法则预测蛋白质中N-棕榈酸酯化位点

背景:在所有主要的翻译后修饰中,脂质修饰由于在真核细胞中具有广泛的功能重要性而具有特殊的意义。脂质修饰存在多种类型,其中棕榈酰化是较宽泛的修饰类型之一,具有三种不同类型。N-棕榈酸酯化通过将棕榈酸附接到N-末端半胱氨酸来进行。由于N-棕榈酰化与多种生物学功能和疾病如阿尔茨海默氏病和其他神经退行性疾病相关联,其鉴定非常重要。

目的:在体外,离体和体内鉴定棕榈酰化是费力,费时且昂贵的。迫切需要一种高效,准确的计算模型,以帮助研究人员和生物学家轻松地识别这些位点。在这里,我们提出了一种新的预测模型,用于鉴定蛋白质中的N-棕榈酰化位点。

方法:通过将Chou的伪氨基酸组成(PseAAC)与深层神经网络相结合,开发出所提出的预测模型。我们使用众所周知的深度神经网络(DNN)来完成学习肽序列特征表示和开发预测模型以进行分类的任务。

结果:在不同的DNN中,基于门控递归单元(GRU)的RNN模型在准确性和所有其他计算量度方面均表现出最高分,并且优于所有先前报道的预测指标。

结论:基于GRU提出的RNN模型可以以非常有效和准确的方式帮助鉴定N-棕榈酸酯化,这可以帮助科学家了解蛋白质中这种修饰的机理。

更新日期:2021-01-31
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