Redox-sensitive cysteines undergo reversible thiol modifications in response to ROS or RNS, thereby modulate protein function, activity or localization, and serve as a regulatory switch for proteins in response to cellular redox state. Alternatively, sulfenic acids can be oxidized to disulfide bonds or S-nitrosothiols, which can be reduced back to thiols by thioredoxin, glutaredoxin, or glutathione. Sulfinic acid and sulfonic acid represent the irreversibly oxidized products. Sulfenic acids are usually the intermediate of thiol-modification, which can react with other thiols or be further oxidized. These thiol groups can be oxidized to disulfide (S-S), sulfenic acid (S-OH), sulfinic acid (SO 2H), sulfonic acid (SO 3H), S-nitrosothiol (S-NO) or S-glutathione (S-SG). Cysteine residues bear a thiol group that represents the most reduced state of sulfur in proteins.

However, cysteine residues usually are more conserved and tend to play critical roles. Ĭysteine is of the least abundance among 20 common amino acids.

−) produced via the enzymatic activity of inducible nitric oxide synthase 2 and NADPH oxidase respectively, also play similar roles as ROS does.

Reactive nitrogen species (RNS), a family of antimicrobial molecules derived from nitrite oxide ( ROS may also act as important signaling molecules in gene transcription and translation, stress protection, apoptosis, metabolism and other processes. However, under normal conditions, ROS can modify the structure and function of proteins in defined ways.

Thus, ROS has traditionally been regarded as unwanted by-products of aerobic metabolism. It has been indicated that many diseases, including type II diabetes, cancer, neurodegenerative diseases and cardiovascular disease, are associated with oxidative stress. Accumulation of ROS may result in the damage of different cellular components including proteins, nucleic acids, lipids and metal cofactors. Reactive oxygen species (ROS) are toxic oxygen-derived molecules generated during various cellular processes. Further validation using an independent dataset indicates it is robust and of relatively better accuracy for predicting redox-sensitive cysteines from non-enzyme proteins. When evaluated using 10-fold cross-validation with BALOSCTdb dataset which has structure information, the model achieved performance comparable to current structure-based method. Using 10-fold cross-validation on RSC758 dataset, the accuracy, sensitivity, specificity, MCC and AUC were estimated as 0.679, 0.602, 0.756, 0.362 and 0.727, respectively. After further feature selection using SVM-RFE, we developed Redox-Sensitive Cysteine Predictor (RSCP), a SVM based classifier for redox-sensitive cysteine prediction using primary sequence only. These features are: sequential distance to the nearby cysteines, PSSM profile and predicted secondary structure of flanking residues. In this study, we analyzed various sequence-based features potentially related to cysteine redox-sensitivity, and identified three types of features for efficient computational prediction of redox-sensitive cysteines.

Net sensitivity for sequential testing