# ROSBASE1.0: a comprehensive database of reactive oxygen species (ROS): categorization of cell organelles, proteins, taxonomy, and diseases based on ROS-related activities

## TL;DR

ROSBASE1.0 is a new database that compiles information on reactive oxygen species (ROS), their proteins, organelles, organisms, and diseases, helping researchers understand ROS-related biology and potential treatments.

## Contribution

ROSBASE1.0 is the first comprehensive database consolidating ROS-related proteins, taxonomy, diseases, and interactome data with experimentally curated entries.

## Key findings

- ROSBASE1.0 contains 2494 experimentally annotated entries with 9174 corresponding PDB IDs.
- The database categorizes 2447 genes, 16 cell organelles, 395 organisms, and 218 diseases related to ROS.
- It reveals that lower organisms prefer ROS-scavenging while higher organisms favor ROS production.

## Abstract

The reactive oxygen species (ROS) produced by various cell organelles often relate to pathophysiology or pathogenesis. Information on ROS-producing, scavenging, and regulatory proteins from various taxonomic kingdoms and organelles, enzyme classes, and diseases was scattered across the literature. Consolidation of all information would facilitate the researchers to understand ROS-related pathology and possible therapeutics. For the first time, we developed a secondary database, ROSBASE1.0, consolidating ROS protein features, taxonomy, diseases, and interactome data. Data sources were PubMed, UniProt, STRING, and Google Scholar databases. Notably, 81.5% of the reported data were experimentally curated. The ROS mechanism was elucidated for 79.2% of entries based on text-mining from the literature. A total of 2494 experimentally annotated entries (UniProt IDs) were curated in the database. Multiple PDB IDs correspond to individual UniProt IDs, resulting higher number of PDB entries (n = 9174). The database reported 2447 genes, 16 cell organelles, 395 organisms, and 218 disease entries (corresponding to 143 proteins) classified into 14 categories. In this database, proteins were classified based on taxonomic kingdoms, enzyme classes, and diseases. The keywords to search the database were—UniProt ID, PDB ID, cell organelle, organism name, and gene name. The tabular results depict all the above features, in addition to the interactome network. The database (https://rosbase.bits-hyderabad.ac.in/) for the first time quantitatively demonstrated the preferences of lower organisms towards ROS-scavenging versus ROS production by higher organisms. Categorization of different diseases according to their ROS involvement was also reported here for the first time. This database could potentially serve as a user guide to ROS Biology.