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• W4298002012 abstract "Identification of lysine (symbol Lys or K) succinylation (Ksucc) sites centralizes the basis for disclosing the mechanism and function of lysine succinylation modifications. Traditional experimental methods for Ksucc site ientification are often costly and time-consuming. Therefore, it is necessary to construct an efficient computational method to prediction the presence of Ksucc sites in protein sequences. In this study, we proposed a novel and effective predictor for the identification of Ksucc sites based on deep learning algorithms that was termed as Deep_KsuccSite. The predictor adopted Composition, Transition, and Distribution (CTD) Composition (CTDC), Enhanced Grouped Amino Acid Composition (EGAAC), Amphiphilic Pseudo-Amino Acid Composition (APAAC), and Embedding Encoding methods to encode peptides, then constructed three base classifiers using one-dimensional (1D) convolutional neural network (CNN) and 2D-CNN, and finally utilized voting method to get the final results. K-fold cross-validation and independent testing showed that Deep_KsuccSite could serve as an effective tool to identify Ksucc sites in protein sequences. In addition, the ablation experiment results based on voting, feature combination, and model architecture showed that Deep_KsuccSite could make full use of the information of different features to construct an effective classifier. Taken together, we developed Deep_KsuccSite in this study, which was based on deep learning algorithm and could achieved better prediction accuracy than current methods for lysine succinylation sites. The code and dataset involved in this methodological study are permanently available at the URL https://github.com/flyinsky6/Deep_KsuccSite." @default.
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• W4298002012 date "2022-09-29" @default.
• W4298002012 modified "2023-09-26" @default.
• W4298002012 title "Deep_KsuccSite: A novel deep learning method for the identification of lysine succinylation sites" @default.
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• W4298002012 doi "https://doi.org/10.3389/fgene.2022.1007618" @default.
• W4298002012 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36246655" @default.
• W4298002012 hasPublicationYear "2022" @default.
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