Deep-Ace: LSTM-based Prokaryotic Lysine Acetylation Site Predictor

Abstract

Acetylation of lysine residues (K-Ace) is a post-translation modification occurring in both prokaryotes and eukaryotes. It plays a crucial role in disease pathology and cell biology hence it is important to identify these K-Ace sites. In the past, many machine learning-based models using hand-crafted features and encodings have been used to find and analyze the characteristics of K-Ace sites however these methods ignore long term relationships within sequences and therefore observe performance degradation. In the current work we propose Deep-Ace, a deep learning-based framework using Long-Short-Term-Memory (LSTM) network which has the ability to understand and encode long-term relationships within a sequence. Such relations are vital for learning discriminative and effective sequence representations. In the work reported here, the use of LSTM to extract deep features as well as for prediction of K-Ace sites using fully connected layers for eight different species of prokaryotic models (including B. subtilis, C. glutamicum, E. coli, G. kaustophilus, S. eriocheiris, B. velezensis, S. typhimurium, and M. tuberculosis) has been explored. Our proposed method has outperformed existing state of the art models achieving accuracy as 0.80, 0.79, 0.71, 0.75, 0.80, 0.83, 0.756, and 0.82 respectively for eight bacterial species mentioned above. The method with minor modifications can be used for eukaryotic systems and can serve as a tool for the prognosis and diagnosis of various diseases in humans.