Background: Among all the major post-translational modifications, amidation seems to be a small change, where a peptide ends with an amide group (-NH 2), not a carboxyl group (-COOH). Thus, to study their physicochemical properties, identification of the amidation mechanism is very important. However, the in vitro, ex vivo and in vivo identification can be laborious, time-taking and costly. There is a dire need for an efficient and accurate computational model to help researchers and biologists identifying these sites, in an easy manner.

Objectives: Herein, we propose a novel predictor for the identification of arginine amide (R-Amide) sites in proteins, by integrating the Chou’s Pseudo Amino Acid Composition (PseAAC) with deep features. Methods: We use well-known DNNs for both the tasks of learning a feature representation of peptide sequences and performing classifications.

Results: Among different DNNs, CNN showed the highest scores in terms of accuracy, and all other computed measures outperformed all the previously reported predictors.

Conclusion: Based on these results, it is concluded that the proposed model can help identify arginine amidation in a very efficient and accurate manner, which can help scientists understand the mechanism of this modification in proteins.

背景：在所有主要的翻译后修饰中，酰胺化似乎是一个很小的变化，其中肽的末端是酰胺基（-NH 2），而不是羧基（-COOH）。因此，研究其理化性质，确定酰胺化机理非常重要。然而，体外，离体和体内鉴定可能是费力，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。

目的：在本文中，我们通过结合具有深层特征的Chou的伪氨基酸组成（PseAAC），提出了一种新的预测蛋白，用于鉴定蛋白质中的精氨酸酰胺（R-酰胺）位点。方法：我们使用众所周知的DNN来完成学习肽序列特征表示和执行分类的任务。

结果：在不同的DNN中，CNN在准确性方面得分最高，所有其他计算指标均优于所有先前报道的预测指标。

结论：基于这些结果，可以得出结论，该模型可以帮助以非常有效和准确的方式鉴定精氨酸酰胺化，从而可以帮助科学家了解蛋白质中这种修饰的机理。