PTMD: A Database of Human Disease-associated Post-translational Modifications.
10.1016/j.gpb.2018.06.004
- Author:
Haodong XU
1
;
Yongbo WANG
1
;
Shaofeng LIN
1
;
Wankun DENG
1
;
Di PENG
1
;
Qinghua CUI
2
;
Yu XUE
3
Author Information
1. Department of Bioinformatics & Systems Biology, MOE Key Laboratory of Molecular Biophysics, College of Life Science and Technology and the Collaborative Innovation Center for Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
2. Department of Biomedical Informatics, School of Basic Medical Sciences, MOE Key Laboratory of Molecular Cardiovascular Sciences, Center for Non-coding RNA Medicine, Peking University, Beijing 100191, China. Electronic address: cuiqinghua@bjmu.edu.cn.
3. Department of Bioinformatics & Systems Biology, MOE Key Laboratory of Molecular Biophysics, College of Life Science and Technology and the Collaborative Innovation Center for Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China. Electronic address: xueyu@hust.edu.cn.
- Publication Type:Journal Article
- Keywords:
AKT1;
Disease–gene network;
PTM–disease association;
Phosphorylation;
Posttranslational modification
- MeSH:
Databases, Protein;
Disease;
genetics;
Gene Regulatory Networks;
Humans;
Phosphorylation;
Protein Processing, Post-Translational;
Proteins;
metabolism;
Search Engine
- From:
Genomics, Proteomics & Bioinformatics
2018;16(4):244-251
- CountryChina
- Language:English
-
Abstract:
Various posttranslational modifications (PTMs) participate in nearly all aspects of biological processes by regulating protein functions, and aberrant states of PTMs are frequently implicated in human diseases. Therefore, an integral resource of PTM-disease associations (PDAs) would be a great help for both academic research and clinical use. In this work, we reported PTMD, a well-curated database containing PTMs that are associated with human diseases. We manually collected 1950 known PDAs in 749 proteins for 23 types of PTMs and 275 types of diseases from the literature. Database analyses show that phosphorylation has the largest number of disease associations, whereas neurologic diseases have the largest number of PTM associations. We classified all known PDAs into six classes according to the PTM status in diseases and demonstrated that the upregulation and presence of PTM events account for a predominant proportion of disease-associated PTM events. By reconstructing a disease-gene network, we observed that breast cancers have the largest number of associated PTMs and AKT1 has the largest number of PTMs connected to diseases. Finally, the PTMD database was developed with detailed annotations and can be a useful resource for further analyzing the relations between PTMs and human diseases. PTMD is freely accessible at http://ptmd.biocuckoo.org.
