ANPrAod: Identify Antioxidant Proteins by Fusing Amino Acid Clustering Strategy and N-Peptide Combination
文献类型： 外文期刊
作者： Xi, Qilemuge 1 ; Wang, Hao 1 ; Yi, Liuxi 2 ; Zhou, Jian 1 ; Liang, Yuchao 1 ; Zhao, Xiaoqing 3 ; Zuo, Yongchun 1 ;
作者机构： 1.Inner Mongolia Univ, Coll Life Sci, State Key Lab Reprod Regulat & Breeding Grassland, Hohhot 010070, Peoples R China
2.Inner Mongolia Agr Univ, Agron Coll, Hohhot 010019, Inner Mongolia, Peoples R China
3.Inner Mongolia Acad Agr & Anim Husb Sci, Biotechnol Res Ctr, Hohhot 010021, Peoples R China
期刊名称：COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE （ 影响因子：1.77； 五年影响因子：1.793 ）
ISSN： 1748-670X
年卷期： 2021 年 2021 卷
页码：
收录情况： SCI
摘要： Antioxidant proteins perform significant functions in disease control and delaying aging which can prevent free radicals from damaging organisms. Accurate identification of antioxidant proteins has important implications for the development of new drugs and the treatment of related diseases, as they play a critical role in the control or prevention of cancer and aging-related conditions. Since experimental identification techniques are time-consuming and expensive, many computational methods have been proposed to identify antioxidant proteins. Although the accuracy of these methods is acceptable, there are still some challenges. In this study, we developed a computational model called ANPrAod to identify antioxidant proteins based on a support vector machine. In order to eliminate potential redundant features and improve prediction accuracy, 673 amino acid reduction alphabets were calculated by us to find the optimal feature representation scheme. The final model could produce an overall accuracy of 87.53% with the ROC of 0.7266 in five-fold cross-validation, which was better than the existing methods. The results of the independent dataset also demonstrated the excellent robustness and reliability of ANPrAod, which could be a promising tool for antioxidant protein identification and contribute to hypothesis-driven experimental design.
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