Kinetic and structural assessment of the reduction of human 2-Cys peroxiredoxins by thioredoxins.

We have studied the reduction reactions of two cytosolic human peroxiredoxins (Prx) in their disulfide form by three thioredoxins (Trx; two human and one bacterial), with the aim of better understanding the rate and mechanism of those reactions, and their relevance in the context of the catalytic cycle of Prx. We have developed a new methodology based on stopped-flow and intrinsic fluorescence to study the bimolecular reactions, and found rate constants in the range of 105 -106 m-1 s-1 in all cases, showing that there is no marked kinetic preference for the expected Trx partner. By combining experimental findings and molecular dynamics studies, we found that the reactivity of the nucleophilic cysteine (CN ) in the Trx is greatly affected by the formation of the Prx-Trx complex. The protein-protein interaction forces the CN thiolate into an unfavorable hydrophobic microenvironment that reduces its hydration and results in a remarkable acceleration of the thiol-disulfide exchange reactions by more than three orders of magnitude and also produces a measurable shift in the pKa of the CN . This mechanism of activation of the thiol disulfide exchange may help understand the reduction of Prx by alternative reductants involved in redox signaling.

Accurate Identification of Antioxidant Proteins Based on a Combination of Machine Learning Techniques and Hidden Markov Model Profiles.

Antioxidant proteins (AOPs) play important roles in the management and prevention of several human diseases due to their ability to neutralize excess free radicals. However, the identification of AOPs by using wet-lab experimental techniques is often time-consuming and expensive. In this study, we proposed an accurate computational model, called AOP-HMM, to predict AOPs by extracting discriminatory evolutionary features from hidden Markov model (HMM) profiles. First, auto cross-covariance (ACC) variables were applied to transform the HMM profiles into fixed-length feature vectors. Then, we performed the analysis of variance (ANOVA) method to reduce the dimensionality of the raw feature space. Finally, a support vector machine (SVM) classifier was adopted to conduct the prediction of AOPs. To comprehensively evaluate the performance of the proposed AOP-HMM model, the 10-fold cross-validation (CV), the jackknife CV, and the independent test were carried out on two widely used benchmark datasets. The experimental results demonstrated that AOP-HMM outperformed most of the existing methods and could be used to quickly annotate AOPs and guide the experimental process.