Identifying Antioxidant Proteins by Using Amino Acid Composition and Protein-Protein Interactions

Frontiers in Cell and Developmental Biology
Yixiao ZhaiYuming Zhao

Abstract

Excessive oxidative stress responses can threaten our health, and thus it is essential to produce antioxidant proteins to regulate the body's oxidative responses. The low number of antioxidant proteins makes it difficult to extract their representative features. Our experimental method did not use structural information but instead studied antioxidant proteins from a sequenced perspective while focusing on the impact of data imbalance on sensitivity, thus greatly improving the model's sensitivity for antioxidant protein recognition. We developed a method based on the Composition of k-spaced Amino Acid Pairs (CKSAAP) and the Conjoint Triad (CT) features derived from the amino acid composition and protein-protein interactions. SMOTE and the Max-Relevance-Max-Distance algorithm (MRMD) were utilized to unbalance the training data and select the optimal feature subset, respectively. The test set used 10-fold crossing validation and a random forest algorithm for classification according to the selected feature subset. The sensitivity was 0.792, the specificity was 0.808, and the average accuracy was 0.8.

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Citations

May 1, 2021·Computational and Mathematical Methods in Medicine·Qilemuge XiYongchun Zuo
Apr 20, 2021·Frontiers in Genetics·Yuanyuan LvBo Gao
May 26, 2021·Methods : a Companion to Methods in Enzymology·Chunyan AoLiang Yu
Jul 9, 2021·Frontiers in Genetics·Lei XuRong Song

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Methods Mentioned

BETA
feature extraction

Software Mentioned

gird
PRED
LIBSVM
Random
MRMD
ClusterCentroids
WEKA
LibD3C
SMOTE
PSI

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