HybridSucc: A Hybrid-learning Architecture for General and Species-specific Succinylation Site Prediction
| Autor: | Yu Xue, Hao-Dong Xu, Peiran Jiang, Yaping Guo, Wankun Deng, Han Cheng, Wanshan Ning |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Service (systems architecture)
Proteome Computer science Acylation Succinic Acid Method Deep neural network Machine learning computer.software_genre Deep-learning Biochemistry Set (abstract data type) Machine Learning 03 medical and health sciences Succinylation Protein acylation 0302 clinical medicine Species Specificity Neoplasms Genetics Humans Cancer mutations Amino Acid Sequence Architecture lcsh:QH301-705.5 Molecular Biology Machine-learning 030304 developmental biology 0303 health sciences Lysine succinylation business.industry Deep learning Lysine Proteins Hybrid learning Computational Mathematics lcsh:Biology (General) ROC Curve Area Under Curve Artificial intelligence Post-translational modification business computer 030217 neurology & neurosurgery Algorithms |
| Zdroj: | Genomics, Proteomics & Bioinformatics Genomics, Proteomics & Bioinformatics, Vol 18, Iss 2, Pp 194-207 (2020) |
| ISSN: | 2210-3244 1672-0229 |
| Popis: | 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/. |
| Databáze: | OpenAIRE |
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