Protein post-translational modification (PTM) is an important regulatory mechanism that has a key role in both normal and disease states. Acetylation on lysine residues is one of the most potent PTMs due to its critical role in cellular metabolism and regulatory processes. Identifying protein lysine acetylation (Kace) sites is a challenging task in bioinformatics. To date, several machine learning-based methods for the in silico identification of Kace sites have been developed. Of those, a few of them are prokaryotic species-specific. Unfortunately, these methods have certain limitations despite their attractive advantages and performances. Henceforth, this study proposes a novel predictor termed STALLION (STacking-based Predictor for ProkAryotic Lysine AcetyLatION) containing six prokaryotic species-specific models to identify Kace sites accurately. To extract crucial patterns around Kace sites, we employed eleven different encodings representing three different characteristics. Subsequently, a systematic and rigorous feature selection approach was employed to identify the optimal feature set independently for five tree-based ensemble algorithms and built their respective baseline model for each species. Finally, the baseline models' predicted values were utilized and trained with an appropriate classifier using the stacking strategy to develop STALLION. Comparative benchmarking experiments showed that STALLION significantly outperformed existing predictors on independent tests.

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