Identifying Antioxidant Proteins by Using Optimal Dipeptide Compositions

In this study, a support vector machine-based predictor called AodPred was developed for identifying antioxidant proteins. In this predictor, the sequence was formulated by using the optimal 3-gap dipeptides obtained by using feature selection method. It was observed by jackknife cross-validation test that AodPred can achieve an overall accuracy of 74.79 % in identifying antioxidant proteins. As a user-friendly tool, AodPred is freely accessible at http://lin.uestc.edu.cn/server/AntioxiPred. To maximize the convenience of the vast majority of experimental scientists, a step-by-step guide is provided on how to use the web server to obtain the desired results.
Source: Interdisciplinary Sciences, Computational Life Sciences - Category: Bioinformatics Source Type: research