LYSINE ACETYLATION SITE PREDICTION IN PROKARYOTES A DEEP LEARNING APPROACH
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Date
2023
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UMT, Lahore
Abstract
Post-Translational Modification (PTM) of proteins plays a vital role in both normal and disease 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 and pre-pone stop or alter faulty modifications that were previously supposed to occur, one can change 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 paper 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 respectively. Machine Learning algorithms require feature extraction from protein sequences, which is a complex and time taking process. This study has introduced a deep learning-based model for the identification of Kace sites. The proposed approach significantly outperforms the existing approaches. The experimental results on the benchmark and independent dataset achieve significantly higher accuracy, very close to the actual labels. The source code for the proposed approach is available at GitHub for validation purposes: https://github.com/RaoHassanKaleem/Deep-Learning-algorithm for-accurate-prokaryotic-lysine-acetylation-site-predictio