论文标题
预测具有改进深度学习体系结构的蛋白质中的琥珀酰化位点
Predicting Succinylation Sites in Proteins with Improved Deep Learning Architecture
论文作者
论文摘要
翻译过程后蛋白质中的翻译后修饰（PTM）发生。 PTMS解释了许多细胞过程，例如脱氧核糖核酸（DNA）修复，细胞信号传导和细胞死亡。最近的PTM之一是琥珀酰化。琥珀酰化将赖氨酸残留物从$ -1 $提高到$+1 $。使用实验方法（例如质谱法）定位琥珀酰化位点非常费力。因此，使用机器学习技术有利于计算方法。本文提出了一个深度学习体系结构，以预测琥珀酰化位点。将所提出的体系结构的性能与最先进的深度学习体系结构和其他传统的机器学习技术进行了比较。从性能指标表明，所提出的体系结构在计算速度和分类精度之间提供了良好的权衡。
Post-translational modifications (PTMs) in proteins occur after the process of translation. PTMs account for many cellular processes such as deoxyribonucleic acid (DNA) repair, cell signaling and cell death. One of the recent PTMs is succinylation. Succinylation modifies lysine residue from $-1$ to $+1$. Locating succinylation sites using experimental methods, such as mass spectrometry is very laborious. Hence, computational methods are favored using machine learning techniques. This paper proposes a deep learning architecture to predict succinylation sites. The performance of the proposed architecture is compared to the state-of-the-art deep learning architecture and other traditional machine learning techniques for succinylation. It is shown from the performance metrics that the proposed architecture provides a good trade-off between speed of computation and classification accuracy.
