Title:Antioxidant Proteins’ Identification Based on Support Vector Machine
Volume: 23
Issue: 4
Author(s): Yuanke Xu, Yaping Wen and Guosheng Han*
Affiliation:
- School of Mathematics and Computational Science, Xiangtan University, Hunan,China
Keywords:
9-gap dipeptide, antioxidant proteins, non-antioxidant proteins, principal component analysis, SVM, 5-fold crossvalidation.
Abstract:
Background: Evidence have increasingly indicated that for human disease, cell
metabolism are deeply associated with proteins. Structural mutations and dysregulations of these
proteins contribute to the development of the complex disease. Free radicals are unstable molecules
that seek for electrons from the surrounding atoms for stability. Once a free radical binds to an
atom in the body, a chain reaction occurs, which causes damage to cells and DNA. An antioxidant
protein is a substance that protects cells from free radical damage. Accurate identification of
antioxidant proteins is important for understanding their role in delaying aging and preventing and
treating related diseases. Therefore, computational methods to identify antioxidant proteins have
become an effective prior-pinpointing approach to experimental verification.
Methods: In this study, support vector machines was used to identify antioxidant proteins, using
amino acid compositions and 9-gap dipeptide compositions as feature extraction, and feature
reduction by Principal Component Analysis.
Results: The prediction accuracy Acc of this experiment reached 98.38%, the recall rate Sn of the
positive sample was found to be 99.27%, the recall rate Sp of the negative sample reached 97.54%,
and the MCC value was 0.9678. To evaluate our proposed method, the predictive performance of
20 antioxidant proteins from the National Center for Biotechnology Information(NCBI) was
studied. As a result, 20 antioxidant proteins were correctly predicted by our method. Experimental
results demonstrate that the performance of our method is better than the state-of-the-art methods
for identification of antioxidant proteins.
Conclusion: We collected experimental protein data from Uniport, including 253 antioxidant
proteins and 1552 non-antioxidant proteins. The optimal feature extraction used in this paper is
composed of amino acid composition and 9-gap dipeptide. The protein is identified by support
vector machine, and the model evaluation index is obtained based on 5-fold cross-validation.
Compared with the existing classification model, it is further explained that the SVM recognition
model constructed in this paper is helpful for the recognition of antioxidized proteins.
