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As a reversible and widely existing post-translational modification of proteins, acetylation plays a crucial role in transcriptional regulation, apoptosis, and cytokine signaling. To better understand the molecular mechanism of acetylation, identification of acetylation sites is vital. The traditional experimental methods are time-consuming and cost-prohibitive, and the majority of acetylation sites remain unknown. It is necessary to develpoe an effective and accurate computational approach to predict the acetylation sites, especially utilizing the advanced deep learning technique. In this study, we propose a prokaryote acetylation sites prediction method based on the deep neural networks, named DNNAce. We extract the protein features from sequence information, physicochemical information, and evolution information of amino acid residues and obtain the initial feature set All. Moreover, we use Group Lasso to remove the irrelevant features that are not effective for classification in the sites prediction area. Compared to other machine learning methods, we use deep neural networks to predict acetylation sites. The 10-fold cross-validation on independent test datasets indicates that DNNAce has the highest values of accuracy for predicting acetylation sites. That DNNAce accurately identifies acetylation sites assists us in understanding its molecular mechanism, and provides related theoretical foundation for the development of drug targets for various diseases. The source code and all datasets are available at https://github.com/QUST-AIBBDRC/DNNAce/. • A novel method (DNNAce) to predict the prokaryote lysine acetylation sites in nine datasets. • The protein sequences are encoded by combining sequence information, physicochemical information and evolution information. • Group Lasso method remove the redundant and irrelevant feature to select the optimal feature subset for the first time. • Compared with other classifiers, DNNAce can extract abstract features from protein sequence through the hidden layers. • The proposed method increases the prediction performance on independent test datasets compared with other tools. [ABSTRACT FROM AUTHOR]