Data Catalog

The prediction of calmodulin-binding (CaM-binding) proteins plays a very important role in the fields of biology and biochemistry, because the calmodulin protein binds and regulates a multitude of protein targets affecting different cellular processes. Computational methods that can accurately identify CaM-binding proteins and CaM-binding domains would accelerate research in calcium signaling and calmodulin function. Short-linear motifs (SLiMs), on the other hand, have been effectively used as features for analyzing protein-protein interactions, though their properties have not been utilized in the prediction of CaM-binding proteins. In this study, researchers propose a new method for the prediction of CaM-binding proteins based on both the total and average scores of known and new SLiMs in protein sequences using a new scoring method called sliding window scoring (SWS) as features for the prediction module. A dataset of 194 manually curated human CaM-binding proteins and 193 mitochondrial proteins have been obtained and used for testing the proposed model.

Proteomics data from a study on whether mercury exposure alters B cell responsiveness to self-antigens by interfering with B cell receptor (BCR) signal transduction. These data show the effects of mercury on the protein tyrosine kinase SYK, a critical protein involved in regulation of the BCR signaling pathway. The raw data for quantitation of SYK phosphorylation status of selected sites were obtained using multiple reaction monitoring (MRM) on a TSQ triple quadrupole mass spectrometer.

Phosophoproteomic analysis was used to profile cell lines in the MCF-10A lineage of human mammary epithelial cells to determine how human breast cells can be reprogrammed during tumorigenic progression. Data were collected using a LTQ-XL mass spectrometer (Thermo). Phosphopeptides were enriched from cell extracts from 3 independent biological replicates, and each replicate was analyzed as 3 technical replicates for a total of 9 LC/MS/MS runs per cell line.