MDDeep-Ace: species-specific acetylation site prediction based on multi-domain adaptation
Yu Liu, Chaofan Ye, Can Lin, Kangkang Mao, Ming Zhu
TL;DR
MDDeep-Ace is a new deep learning tool that improves prediction of acetylation sites in proteins across different species.
Contribution
MDDeep-Ace introduces a multi-domain adaptation approach to enhance species-specific acetylation site prediction.
Findings
MDDeep-Ace improves prediction accuracy across multiple species.
The multi-domain adaptation approach outperforms existing lysine acetylation site prediction tools.
Abstract
Lysine post-translational modification (PTM) is pivotal in regulating diverse cellular processes, profoundly impacting protein structure and function. Over recent decades, numerous experimental techniques have advanced PTM site identification, significantly contributing to research progress. However, these methods are time-intensive and labor-intensive. Deep learning technologies have shown promise in predicting PTM sites, yet current approaches struggle with species-specific PTM site prediction. We introduce MDDeep-Ace, a novel deep learning method based on multi-domain adaptation for predicting lysine acetylation sites. By integrating data from multiple species, MDDeep-Ace enhances the generalization of species-specific prediction models, improving predictive performance. Experimental findings illustrate that our proposed multi-domain adaptation approach significantly enhances…
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Taxonomy
TopicsAntimicrobial Peptides and Activities · Machine Learning in Bioinformatics · Genomics and Phylogenetic Studies