dbSNO 2.0: a resource for exploring structural environment, functional and disease association and regulatory network of protein S-nitrosylation.
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.
Institute of Chemistry, Academia Sinica, Taipei 115, Taiwan.
Yi-Ju
YJ
Chen
Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan.
Cheng-Tsung
CT
Lu
Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan.
Min-Gang
MG
Su
Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan.
Kai-Yao
KY
Huang
Graduate Institute of Life Sciences, National Defense Medical Center, Taipei 114, Taiwan.
Wei-Chieh
WC
Ching
Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan.
Hsiao-Hsiang
HH
Yang
Institute of Chemistry, Academia Sinica, Taipei 115, Taiwan Department of Chemistry, National Taiwan University, Taipei 114, Taiwan.
Yen-Chen
YC
Liao
Institute of Chemistry, Academia Sinica, Taipei 115, Taiwan Department of Chemistry, National Taiwan University, Taipei 114, Taiwan.
Yu-Ju
YJ
Chen
Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan 320, Taiwan francis@saturn.yzu.edu.tw.
Tzong-Yi
TY
Lee
D000596:Amino Acids / Q000737:chemistry; D000818:Animals; D030562:Databases, Protein; D004194:Disease; D006801:Humans; D020407:Internet; D053858:Metabolic Networks and Pathways; D051379:Mice; D009569:Nitric Oxide / Q000378:metabolism*; D025941:Protein Interaction Mapping; D011499:Protein Processing, Post-Translational; D011506:Proteins / Q000737:chemistry / Q000378:metabolism; D051381:Rats; D015398:Signal Transduction
D000596:Amino Acids; D011506:Proteins; D009569:Nitric Oxide