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• W3082590494 abstract "As an important protein acylation modification, lysine succinylation (Ksucc) is involved in diverse biological processes, and participates in human tumorigenesis. Here, we collected 26,243 non-redundant known Ksucc sites from 13 species as the benchmark data set, combined 10 types of informative features, and implemented a hybrid-learning architecture by integrating deep-learning and conventional machine-learning algorithms into a single framework. We constructed a new tool named HybridSucc, which achieved area under curve (AUC) values of 0.885 and 0.952 for general and human-specific prediction of Ksucc sites, respectively. In comparison, the accuracy of HybridSucc was 17.84%-50.62% better than that of other existing tools. Using HybridSucc, we conducted a proteome-wide prediction and prioritized 370 cancer mutations that change Ksucc states of 218 important proteins, including PKM2, SHMT2, and IDH2. We not only developed a high-profile tool for predicting Ksucc sites, but also generated useful candidates for further experimental consideration. The online service of HybridSucc can be freely accessed for academic research at http://hybridsucc.biocuckoo.org/." @default.
• W3082590494 created "2020-09-08" @default.
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• W3082590494 date "2020-04-01" @default.
• W3082590494 modified "2023-10-16" @default.
• W3082590494 title "HybridSucc: A Hybrid-learning Architecture for General and Species-specific Succinylation Site Prediction" @default.
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• W3082590494 doi "https://doi.org/10.1016/j.gpb.2019.11.010" @default.
• W3082590494 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7647696" @default.
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