AOPM: Application of Antioxidant Protein Classification Model in Predicting the Composition of Antioxidant Drugs.

Yixiao Zhai,Tianjiao Zhang,Zixiao Zhang,Dandan Zhang,Yue Gong,Jingyu Zhang,Yuming Zhao

doi:10.3389/fphar.2021.818115

Abstract

Antioxidant proteins can not only balance the oxidative stress in the body, but are also an important component of antioxidant drugs. Accurate identification of antioxidant proteins is essential to help humans fight diseases and develop new drugs. In this paper, we developed a friendly method AOPM to identify antioxidant proteins. 188D and the Composition of k-spaced Amino Acid Pairs were adopted as the feature extraction method. In addition, the Max-Relevance-Max-Distance algorithm (MRMD) and random forest were the feature selection and classifier, respectively. We used 5-folds cross-validation and independent test dataset to evaluate our model. On the test dataset, AOPM presented a higher performance compared with the state-of-the-art methods. The sensitivity, specificity, accuracy, Matthew’s Correlation Coefficient and an Area Under the Curve reached 87.3, 94.2, 92.0%, 0.815 and 0.972, respectively. In addition, AOPM still has excellent performance in predicting the catalytic enzymes of antioxidant drugs. This work proved the feasibility of virtual drug screening based on sequence information and provided new ideas and solutions for drug development.

Highlights

In the process of biological metabolism, reactive oxygen species (ROS) are produced
Where TP is the number of samples judged as positive for the positive class, FP is the number of samples judged as positive for the negative class, FN is the number of samples judged as negative for the positive class, and TN is the number of samples judged as negative for the negative class
The Area Under the Curve (AUC) value was obtained by calculating the area of the ROC curve and the area surrounded by the X- and Y-axes, where the X- and Y-axes of the ROC curve were (1-SP) and SN, respectively

Summary

Introduction

The antioxidant system in the organism can eliminate ROS, but there is a limit. Too high concentrations of ROS are not eliminated in time and will cause oxidative stress (OS) (Birben et al, 2012; Yang et al, 2020; Zhao S et al, 2021). OS response plays an important role in the pathogenesis of many diseases. Research by Azhwar Raghunath’s team (Raghunath et al, 2018) has shown that the protective effect against oxidative stress is a cis-acting element of antioxidant proteins in the regulation of Nrf target genes, which plays a key role in redox homeostasis. Antioxidant proteins have been used in the development and screening of antioxidant drugs, which can treat cancer, neurodegenerative diseases, cardiovascular, metabolic and other diseases with oxidative stress (Liguori et al, 2018; Eleutherio et al, 2021; Zia et al, 2021)

Methods

Results

Conclusion