  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.
