In Vitro Kinase-to-Phosphosite Database (iKiP-DB) Predicts Kinase Activity in Phosphoproteomic Datasets.

Phosphoproteomics routinely quantifies changes in the levels of thousands of phosphorylation sites, but functional analysis of such data remains a major challenge. While databases like PhosphoSitePlus contain information about many phosphorylation sites, the vast majority of known sites is not assigned to any protein kinase. Assigning changes in the phosphoproteome to the activity of individual kinases therefore remains a key challenge. A recent large-scale study systematically identified in vitro substrates for most human protein kinases. Here, we reprocessed and filtered these data to generate an in vitro Kinase-to-Phosphosite database (iKiP-DB). We show that iKiP-DB can accurately predict changes in kinase activity in published phosphoproteomic data sets for both well-studied and poorly characterized kinases. We apply iKiP-DB to a newly generated phosphoproteomic analysis of SARS-CoV-2 infected human lung epithelial cells and provide evidence for coronavirus-induced changes in host cell kinase activity. In summary, we show that iKiP-DB is widely applicable to facilitate the functional analysis of phosphoproteomic data sets.

Yeast-based high-throughput screens for discovery of kinase inhibitors for neglected diseases.

The discovery and development of a new drug is a complex, time consuming and costly process that typically takes over 10 years and costs around 1 billion dollars from bench to market. This scenario makes the discovery of novel drugs targeting neglected tropical diseases (NTDs), which afflict in particular people in low-income countries, prohibitive. Despite the intensive use of High-Throughput Screening (HTS) in the past decades, the speed with which new drugs come to the market has remained constant, generating doubts about the efficacy of this approach. Here we review a few of the yeast-based high-throughput approaches that can work synergistically with parasite-based, in vitro, or in silico methods to identify and optimize novel antiparasitic compounds. These yeast-based methods range from HTP screens to identify novel hits against promising parasite kinase targets to the identification of potential antiparasitic kinase inhibitors extracted from databases of yeast chemical genetic screens.

VPTMdb: a viral posttranslational modification database.

In viruses, posttranslational modifications (PTMs) are essential for their life cycle. Recognizing viral PTMs is very important for a better understanding of the mechanism of viral infections and finding potential drug targets. However, few studies have investigated the roles of viral PTMs in virus-human interactions using comprehensive viral PTM datasets. To fill this gap, we developed the first comprehensive viral posttranslational modification database (VPTMdb) for collecting systematic information of PTMs in human viruses and infected host cells. The VPTMdb contains 1240 unique viral PTM sites with 8 modification types from 43 viruses (818 experimentally verified PTM sites manually extracted from 150 publications and 422 PTMs extracted from SwissProt) as well as 13 650 infected cells' PTMs extracted from seven global proteomics experiments in six human viruses. The investigation of viral PTM sequences motifs showed that most viral PTMs have the consensus motifs with human proteins in phosphorylation and five cellular kinase families phosphorylate more than 10 viral species. The analysis of protein disordered regions presented that more than 50% glycosylation sites of double-strand DNA viruses are in the disordered regions, whereas single-strand RNA and retroviruses prefer ordered regions. Domain-domain interaction analysis indicating potential roles of viral PTMs play in infections. The findings should make an important contribution to the field of virus-human interaction. Moreover, we created a novel sequence-based classifier named VPTMpre to help users predict viral protein phosphorylation sites. VPTMdb online web server (http://vptmdb.com:8787/VPTMdb/) was implemented for users to download viral PTM data and predict phosphorylation sites of interest.