AOP-Pred Sequence Based Prediction of Antioxidant Proteins Using a Classifier Selection Strategy

Introduction

AOP-Pred is a user-friendly web server that employs an ensemble method with comprehensive sequence descriptors features to predict antioxidant proteins(AOPs) from primary protein sequences. Antioxidant proteins perform significant functions in maintaining oxidation/antioxidation balance and have potential therapies for some diseases. Accurate identification of antioxidant proteins could contribute to revealing physiological processes of oxidation/antioxidation balance and developing novel antioxidation based drugs. In this study, an ensemble method is presented to predict antioxidant proteins with hybrid features, incorporating SSI (Secondary Structure Information), PSSM (Position Specific Scoring Matrix), RSA (Relative Solvent Accessibility), and CTD (Composition, Transition, Distribution). The prediction results of the ensemble predictor are determined by an average of prediction results of multiple base classifiers. Based on a classifier selection strategy, we obtain an optimal ensemble classifier composed of RF (Random Forest), SMO (Sequential Minimal Optimization), NNA (Nearest Neighbor Algorithm), and J48 with an accuracy of 0.925. A Relief combined with IFS (Incremental Feature Selection) method is adopted to obtain optimal features from hybrid features. With the optimal features, the ensemble method achieves improved performance with a sensitivity of 0.95, a specificity of 0.93, an accuracy of 0.94, and an MCC (Matthew's Correlation Coefficient) of 0.880, far better than the existing method. To evaluate the prediction performance objectively, the proposed method is compared with existing methods on the same independent testing dataset. Encouragingly, our method performs better than previous studies. In addition, our method achieves more balanced performance with a sensitivity of 0.878 and a specificity of 0.860. These results suggest that the proposed ensemble method can be a potential candidate for antioxidant protein prediction.

Funding

This research is supported by the NSFC under grant 61174044, 61473335, and 61174218, Natural Science Foundation of Shandong Province of China under Grant No. ZR2015PG004, and the Doctoral Foundation of University of Jinan under Grant No. XBS1334.

Citation

L.N. Zhang, C.J. Zhang, R. Gao, R.T. Yang. Sequence Based Prediction of Antioxidant Proteins Using a Classifier Selection Strategy, 2016.

Contact us

Name Email Address
Lina Zhang zlnabc2010@163.com School of Control Science and Engineering, Shandong University, Jingshi Road No.17923, Jinan, Shandong Province, China.
Chengjin Zhang cjzhang@sdu.edu.cn
Rui Gao gaorui@sdu.edu.cn
Runtao Yang runtao-sd@163.com