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HybridSucc: A Hybrid-learning Architecture for General and Species-specific Succinylation Site Prediction
- Source :
- Genomics, Proteomics & Bioinformatics, Vol 18, Iss 2, Pp 194-207 (2020)
- Publication Year :
- 2020
- Publisher :
- Oxford University Press, 2020.
-
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/.
Details
- Language :
- English
- ISSN :
- 16720229
- Volume :
- 18
- Issue :
- 2
- Database :
- Directory of Open Access Journals
- Journal :
- Genomics, Proteomics & Bioinformatics
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.20d3c8addff14278a866d6ec1a8b0b37
- Document Type :
- article
- Full Text :
- https://doi.org/10.1016/j.gpb.2019.11.010