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Identification of protein-nucleotide binding residues via graph regularized k-local hyperplane distance nearest neighbor model

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Abstract

Accurate identification of protein-nucleotide binding residues is crucial for the study of drug structure and protein functional annotation. The study of protein-nucleotide binding residues is a typical problem of sample imbalance. The minority class (binding residues) are far less than the majority class (non-binding residues). The traditional machine learning algorithm is not universal for this kind of research, the results will be seriously biased to majority class. To deal with the serious imbalance problem, we propose a new computational method to identify protein-nucleotide binding residues via Graph Regularized k-local Hyperplane Distance Nearest Neighbor (GHKNN). On the training set, we compare the performance of the basic classifier, the ensemble classifier and the single classifier. On the independent test sets, we compare the performance with other existing models. The experimental results prove that our proposed method has higher accuracy in the identification of protein-nucleotide binding residues and is more prominent than other existing models. The data and material are freely available at https://github.com/guofei-tju/GHKNN.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (NSFC 61902271, 61772362 and 61972280), the Natural Science Research of Jiangsu Higher Education Institutions of China (19KJB520014) and the National Key R&D Program of China (2020YFA0908400).

The author would like to thank Professor Dong-jun Yu for providing the dataset, which helped improve the quality of this paper.

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Authors and Affiliations

  1. Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, 324000, China

    Yijie Ding

  2. School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China

    Chao Yang & Jijun Tang

  3. Department of Computer Science and Engineering, University of South Carolina, Columbia, SC, 29208, USA

    Jijun Tang

  4. School of Computer Science and Engineering, Central South University, Changsha, 410083, China

    Fei Guo

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  1. Yijie Ding
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  2. Chao Yang
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Correspondence to Yijie Ding or Fei Guo.

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Yijie Ding and Chao Yang have the same contribution, they are joint first authors.

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Ding, Y., Yang, C., Tang, J. et al. Identification of protein-nucleotide binding residues via graph regularized k-local hyperplane distance nearest neighbor model. Appl Intell 52, 6598–6612 (2022). https://doi.org/10.1007/s10489-021-02737-0

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