SeqSVM: A Sequence-Based Support Vector Machine Method for Identifying Antioxidant Proteins

Abstract

Antioxidant proteins can be beneficial in disease prevention. More attention has been paid to the functionality of antioxidant proteins. Therefore, identifying antioxidant proteins is important for the study. In our work, we propose a computational method, called SeqSVM, for predicting antioxidant proteins based on their primary sequence features. The features are removed to reduce the redundancy by max relevance max distance method. Finally, the antioxidant proteins are identified by support vector machine (SVM). The experimental results demonstrated that our method performs better than existing methods, with the overall accuracy of 89.46%. Although a proposed computational method can attain an encouraging classification result, the experimental results are verified based on the biochemical approaches, such as wet biochemistry and molecular biology techniques.

Keywords: antioxidant protein; feature selection; maximum relevance maximum distance; primary sequence; support vector machine.

Figure 1

Comparison of our features with g-gap using different classifiers on Sn.

Figure 2

Comparison of our features with g-gap using different classifiers on Sp.

Figure 3

Comparison of our features with g-gap using different classifiers on Acc.

Figure 4

Eight physicochemical property attributes.