Machine learning model for identifying antioxidant proteins using features calculated from primary sequences

Luu Ho Thanh Lam, Ngoc Hoang Le, Le Van Tuan, Ho Tran Ban, Truong Nguyen Khanh Hung, Ngan Thi Kim Nguyen, Luong Huu Dang, Nguyen Quoc Khanh Le

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

Abstract

Antioxidant proteins are involved importantly in many aspects of cellular life activities. They protect the cell and DNA from oxidative substances (such as peroxide, nitric oxide, oxygen-free radicals, etc.) which are known as reactive oxygen species (ROS). Free radical generation and antioxidant defenses are opposing factors in the human body and the balance between them is necessary to maintain a healthy body. An unhealthy routine or the degeneration of age can break the balance, leading to more ROS than antioxidants, causing damage to health. In general, the antioxidant mechanism is the combination of antioxidant molecules and ROS in a one-electron reaction. Creating computational models to promptly identify antioxidant candidates is essential in supporting antioxidant detection experiments in the laboratory. In this study, we proposed a machine learning-based model for this prediction purpose from a benchmark set of sequencing data. The experiments were conducted by using 10-fold cross-validation on the training process and validated by three different independent datasets. Different machine learning and deep learning algorithms have been evaluated on an optimal set of sequence features. Among them, Random Forest has been identified as the best model to identify antioxidant proteins with the highest performance. Our optimal model achieved high accuracy of 84.6%, as well as a balance in sensitivity (81.5%) and specificity (85.1%) for antioxidant protein identification on the training dataset. The performance results from different independent datasets also showed the significance in our model compared to previously published works on antioxidant protein identification.

Original languageEnglish
Article number325
Pages (from-to)1-13
Number of pages13
JournalBiology
Volume9
Issue number10
DOIs
Publication statusPublished - Oct 2020

Keywords

  • Antioxidant proteins
  • Computational modeling
  • Feature selection
  • Machine learning
  • Protein sequencing
  • Random Forest

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)

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