francis@saturn.yzu.edu.tw

Given the increasing number of proteins reported to be regulated by S-nitrosylation (SNO), it is considered to act, in a manner analogous to phosphorylation, as a pleiotropic regulator that elicits dual effects to regulate diverse pathophysiological processes by altering protein function, stability, and conformation change in various cancers and human disorders. Due to its importance in regulating protein functions and cell signaling, dbSNO (http://dbSNO.mbc.nctu.edu.tw) is extended as a resource for exploring structural environment of SNO substrate sites and regulatory networks of S-nitrosylated proteins. An increasing interest in the structural environment of PTM substrate sites motivated us to map all manually curated SNO peptides (4165 SNO sites within 2277 proteins) to PDB protein entries by sequence identity, which provides the information of spatial amino acid composition, solvent-accessible surface area, spatially neighboring amino acids, and side chain orientation for 298 substrate cysteine residues. Additionally, the annotations of protein molecular functions, biological processes, functional domains and human diseases are integrated to explore the functional and disease associations for S-nitrosoproteome. In this update, users are allowed to search a group of interested proteins/genes and the system reconstructs the SNO regulatory network based on the information of metabolic pathways and protein-protein interactions. Most importantly, an endogenous yet pathophysiological S-nitrosoproteomic dataset from colorectal cancer patients was adopted to demonstrate that dbSNO could discover potential SNO proteins involving in the regulation of NO signaling for cancer pathways.

BackgroundProtein phosphorylation catalyzed by kinases plays crucial regulatory roles in intracellular signal transduction. Due to the difficulty in performing high-throughput mass spectrometry-based experiment, 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.ResultsExperimentally verified phosphorylation data were extracted from TAIR9-a protein database containing 3006 phosphorylation data from the plant species Arabidopsis thaliana. In an attempt to investigate the various substrate motifs in plant phosphorylation, maximal dependence decomposition (MDD) is employed to cluster a large set of phosphorylation data into subgroups containing significantly conserved motifs. Profile hidden Markov model (HMM) is then applied to learn a predictive model for each subgroup. Cross-validation evaluation on the MDD-clustered HMMs yields an average accuracy of 82.4% for serine, 78.6% for threonine, and 89.0% for tyrosine models. Moreover, independent test results using Arabidopsis thaliana phosphorylation data from UniProtKB/Swiss-Prot show that the proposed models are able to correctly predict 81.4% phosphoserine, 77.1% phosphothreonine, and 83.7% phosphotyrosine sites. Interestingly, several MDD-clustered subgroups are observed to have similar amino acid conservation with the substrate motifs of well-known kinases from Phospho.ELM-a database containing kinase-specific phosphorylation data from multiple organisms.ConclusionsThis work presents a novel method for identifying plant phosphorylation sites with various substrate motifs. Based on cross-validation and independent testing, results show that the MDD-clustered models outperform models trained without using MDD. The proposed method has been implemented as a web-based plant phosphorylation prediction tool, PlantPhos http://csb.cse.yzu.edu.tw/PlantPhos/. Additionally, two case studies have been demonstrated to further evaluate the effectiveness of PlantPhos.

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, the RegPhos, available online at http://csb.cse.yzu.edu.tw/RegPhos2/, 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. The experimentally validated phosphorylation sites as well as their catalytic kinases were extracted from public resources, and MS/MS phosphopeptides were manually curated from research articles. RegPhos 2.0 aims to provide a more comprehensive view of intracellular signaling networks by integrating the information of metabolic pathways and protein–protein interactions. A case study shows that analyzing the phosphoproteome profile of time-dependent cell activation obtained from Liquid chromatography-mass spectrometry (LC-MS/MS) analysis, the RegPhos deciphered not only the consistent scheme in B cell receptor (BCR) signaling pathway but also novel regulatory molecules that may involve in it. With an attempt to help users efficiently identify the candidate biomarkers in cancers, 30 microarray experiments, including 39 cancerous versus normal cells, were analyzed for detecting cancer-specific expressed genes coding for kinases and their substrates. Furthermore, this update features an improved web interface to facilitate convenient access to the exploration of phosphorylation networks for a group of genes/proteins.Database URL: http://csb.cse.yzu.edu.tw/RegPhos2/