Lysine Acetylation Site Prediction in Prokaryotes: A Deep Learning Approach

Abstract

Post-Translational Modification (PTM) of proteins plays a vital role in both disease and normal states. Protein acetylation is an important PTM in eukaryotes as it greatly changes the properties of a protein including hydrophobicity and solubility. Therefore, in both metabolism and regulatory processes, acetylation and other PTMs perform a critical role. By Investigating and accurately spotting lysine acetylation sites can stop or alter faulty modifications that were previously supposed to occur. This can help in changing the course of microbiological diseases like Bacteremia, UTI's, meningitis and others. Several models have been developed to identify lysine acetylation (Kace) sites with appreciable performances. This manuscript presents an improved approach to identify lysine acetylation (Kace) sites which achieves 0.951, 0.891, 0.813, 0.969, 0.946, and 1.0 MCC for B. subtilis, C. glutamicum, E. coli, G. kaustophilus, M. tuberculosis and S. typhimurium species respectively. Machine Learning algorithms require feature extraction from protein sequences, which is a complex and time taking process. This study has introduced an approach to identify kace sites using a deep learning-based model. The proposed approach significantly outperforms the existing approaches. The experimental results on the benchmark and independent datasets achieve significantly higher accuracy, very close to the actual labels. The source code accurate prokaryotic-lysine-acetylation-site-prediction for the proposed approach is made publicly available online for validation purposes.