Abstract
In the biological systems, Acetylation is a crucial post-translational modification, prevalent in various physiological functions and pathological conditions like carcinoma and malignancies. To better understand serine acetylation, the first step is the efficient identification of the same. Although multiple large-scale in-vivo, ex-vivo, and in-vitro methods have been applied to detect serine acetylation biomarkers, these experimental methods are time-consuming and labor-intensive. This research aims to develop an in-silico solution to supplement wetlab experiments for efficient detection of serine acetylation sites by combining Chou's Pseudo Amino Acid Composition (PseAAC) with deep neural networks (DNNs). By employing well-known DNNs for feature learning and classification of peptide sequences, our approach obsoletes the need to separately perform costly and cumbersome feature learning process. Based on performance evaluation using standard evaluation metrics, CNN and FCN based models, for AcetylSerine site identification, surpassed previously reported predictors which shows the efficacy of proposed approach.