Reactive oxygen species (ROS) has been regarded as unwanted by-product of aerobic metabolism. However, under normal conditions, ROS could modify the structure and function of proteins in defined ways. ROS could also act as important signalling molecules in various cellular processes. Cysteine thiol groups of proteins are particularly susceptible to oxidation by ROS/RNS and other electrophilic molecules, and their reversible oxidation is of critical roles for the redox regulation of proteins.
Redox-sensitive cysteines hereby stands for these cysteines which could have changed redox status under different condition. Many of redox-sensitive cysteines were confirmed to be functionaly important.
Based on a newly collected dataset (RSC758) for various types of redox-sensitive cysteines, we identified several types of sequence-based features, including PSSM profile, secondary structure, and the sequential distance to nearby cysteines that can be used for redox-sensitive cysteine prediction. After further feature selection using SVM-RFE, we developed a SVM based tool for redox-sensitive cysteine prediction. Using 10-fold cross-validation, it could achieve overall accuracy of 67.9%, sensitivity of 0.602, specificity of 0.756, MCC of 0.362, and AUC of 0.727. It also achieved robust performance when applied to an independent dataset.
RSCP is the web server implement of this approach. It takes primary sequence as input, and predict the occurrence of redox-sensitive cysteines within the sequence.
Sun et al. Prediction of redox-sensitive cysteines using sequential distance and other sequence-based features. Manuscript submitted.