دورية أكاديمية

KinasePhos 3.0: Redesign and Expansion of the Prediction on Kinase-specific Phosphorylation Sites

التفاصيل البيبلوغرافية
العنوان: KinasePhos 3.0: Redesign and Expansion of the Prediction on Kinase-specific Phosphorylation Sites
المؤلفون: Renfei Ma, Shangfu Li, Wenshuo Li, Lantian Yao, Hsien-Da Huang, Tzong-Yi Lee
المصدر: Genomics, Proteomics & Bioinformatics, Vol 21, Iss 1, Pp 228-241 (2023)
بيانات النشر: Elsevier, 2023.
سنة النشر: 2023
المجموعة: LCC:Biology (General)
مصطلحات موضوعية: Kinase-specific phosphorylation, Phosphorylation site prediction, Phosphorylation, SHAP feature importance, Kinase, Biology (General), QH301-705.5
الوصف: The purpose of this work is to enhance KinasePhos, a machine learning-based kinase-specific phosphorylation site prediction tool. Experimentally verified kinase-specific phosphorylation data were collected from PhosphoSitePlus, UniProtKB, the GPS 5.0, and Phospho.ELM. In total, 41,421 experimentally verified kinase-specific phosphorylation sites were identified. A total of 1380 unique kinases were identified, including 753 with existing classification information from KinBase and the remaining 627 annotated by building a phylogenetic tree. Based on this kinase classification, a total of 771 predictive models were built at the individual, family, and group levels, using at least 15 experimentally verified substrate sites in positive training datasets. The improved models demonstrated their effectiveness compared with other prediction tools. For example, the prediction of sites phosphorylated by the protein kinase B, casein kinase 2, and protein kinase A families had accuracies of 94.5%, 92.5%, and 90.0%, respectively. The average prediction accuracy for all 771 models was 87.2%. For enhancing interpretability, the SHapley Additive exPlanations (SHAP) method was employed to assess feature importance. The web interface of KinasePhos 3.0 has been redesigned to provide comprehensive annotations of kinase-specific phosphorylation sites on multiple proteins. Additionally, considering the large scale of phosphoproteomic data, a downloadable prediction tool is available at https://awi.cuhk.edu.cn/KinasePhos/download.htmlTest or https://github.com/tom-209/KinasePhos-3.0-executable-fileTest.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 1672-0229
العلاقة: http://www.sciencedirect.com/science/article/pii/S167202292200081XTest; https://doaj.org/toc/1672-0229Test
DOI: 10.1016/j.gpb.2022.06.004
الوصول الحر: https://doaj.org/article/09090073832e4ae2bbe0fcccb1fa75e0Test
رقم الانضمام: edsdoj.09090073832e4ae2bbe0fcccb1fa75e0
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:16720229
DOI:10.1016/j.gpb.2022.06.004