Predicting Succinylation Sites in Proteins with Improved Deep Learning Architecture
This work addresses a domain-specific problem for researchers in bioinformatics by providing an incremental improvement in computational methods for protein succinylation prediction.
The paper tackles the problem of predicting succinylation sites in proteins by proposing a deep learning architecture, which achieves a good trade-off between computational speed and classification accuracy compared to state-of-the-art methods.
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.