Journal
FRONTIERS IN CELL AND DEVELOPMENTAL BIOLOGY
Volume 9, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fcell.2021.707938
Keywords
2-oxoglutarate/Fe (II)-dependent oxygenase; reduced amino acid cluster; machine learning; anova; incremental feature selection; 10-fold cross-validation test
Categories
Funding
- National Natural Scientific Foundation of China [62061034, 61861036]
- Program for Young Talents of Science and Technology in Universities of Inner Mongolia Autonomous Region [NJYT-18-B01]
- Fund for Excellent Young Scholars of Inner Mongolia [2017JQ04]
- Science and Technology Major Project of Inner Mongolia Autonomous Region of China [2019ZD031]
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A prediction model OGFE_RAAC was developed to identify 2OG oxygenases, achieving an accuracy of 91.04% with excellent generalization and robustness. This model is expected to become an effective tool for the identification of 2OG oxygenases.
The 2-oxoglutarate/Fe (II)-dependent (2OG) oxygenase superfamily is mainly responsible for protein modification, nucleic acid repair and/or modification, and fatty acid metabolism and plays important roles in cancer, cardiovascular disease, and other diseases. They are likely to become new targets for the treatment of cancer and other diseases, so the accurate identification of 2OG oxygenases is of great significance. Many computational methods have been proposed to predict functional proteins to compensate for the time-consuming and expensive experimental identification. However, machine learning has not been applied to the study of 2OG oxygenases. In this study, we developed OGFE_RAAC, a prediction model to identify whether a protein is a 2OG oxygenase. To improve the performance of OGFE_RAAC, 673 amino acid reduction alphabets were used to determine the optimal feature representation scheme by recoding the protein sequence. The 10-fold cross-validation test showed that the accuracy of the model in identifying 2OG oxygenases is 91.04%. Besides, the independent dataset results also proved that the model has excellent generalization and robustness. It is expected to become an effective tool for the identification of 2OG oxygenases. With further research, we have also found that the function of 2OG oxygenases may be related to their polarity and hydrophobicity, which will help the follow-up study on the catalytic mechanism of 2OG oxygenases and the way they interact with the substrate. Based on the model we built, a user-friendly web server was established and can be friendly accessed at http://bioinfor.imu.edu.cn/ogferaac.
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