Post-translational modifications (PTMs) such as phosphorylation, acetylation, methylation, and ubiquitination are crucial in modulating protein activity, stability, localization, and interactions that enhance the functional diversity of proteins beyond their primary amino acid sequences. BiOmics Lab has, therefore, created a comprehensive PTM database. 

https://biomics.lab.nycu.edu.tw/dbPTM/

An integrated system for identifying Anti-Microbial Peptides (AMPs) with their functional types based on high-throughput transcriptome and proteome data. In recent years, the widespread use of antibiotics has inspired the rapid growth of antibiotic-resistant microorganisms that usually induce critical infection and pathogenesis. However, as the key components of innate immunity, human host defense AMPs are crucial in warding off invading microbial pathogens. Typical examples are Human γδ T cells that produce antimicrobial peptide elafin (dbAMP_00487), a major antiviral protein in cervicovaginal lavage fluid. Human Lactoferricin derivatives (dbAMP_04016) showed an inhibitory effect on bacteria and melanoma cells. 

https://awi.cuhk.edu.cn/dbAMP/

Protein phosphorylation catalyzed by kinases plays crucial roles in regulating a variety of intracellular processes. Owing to an increasing number of in vivo phosphorylation sites that have been identified by mass spectrometry (MS)-based proteomics, RegPhos was developed to explore protein phosphorylation networks in human. In this update, we not only enhance the data content in human but also investigate kinase–substrate phosphorylation networks in mouse and rat. 

http://csb.cse.yzu.edu.tw/RegPhos2/

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, GPS 5.0, and Phospho.ELM. A total of 1,380 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. 

https://awi.cuhk.edu.cn/KinasePhos/

Protein phosphorylation catalyzed by kinases plays crucial regulatory roles in intracellular signal transduction. Due to the difficulty in performing high-throughput mass spectrometry-based experiments, there is a desire to predict phosphorylation sites using computational methods. However, previous studies regarding in silico prediction of plant phosphorylation sites lack the consideration of kinase-specific phosphorylation data. Thus, we are motivated to propose a new method that investigates different substrate specificities in plant phosphorylation sites. 

http://csb.cse.yzu.edu.tw/PlantPhos/