HybridSucc:A Hybrid-learning Architecture for General and Species-specific Succinylation Site Prediction
- Author:
Ning WANSHAN
1
;
Xu HAODONG
;
Jiang PEIRAN
;
Cheng HAN
;
Deng WANKUN
;
Guo YAPING
;
Xue YU
Author Information
1. Department of Bioinformatics and Systems Biology, Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
- Keywords:
Lysine succinylation;
Post-translational modifica-tion;
Deep-learning;
Machine-learning;
Deep neural network
- From:
Genomics, Proteomics & Bioinformatics
2020;18(2):194-207
- CountryChina
- Language:Chinese
-
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 Hybrid-Succ 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 con-sideration. The online service of HybridSucc can be freely accessed for academic research at http://hybridsucc.biocuckoo.org/.
