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Incorporating Natural Language-Based and Sequence-Based Features to Predict Protein Sumoylation Sites

  • Conference paper
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The 12th Conference on Information Technology and Its Applications (CITA 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 734))

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Abstract

The incidence of thyroid cancer and breast cancer is increasing every year, and the specific pathogenesis is unclear. Post-translational modifications are an important regulatory mechanism that affects the function of almost all proteins. They are essential for a diverse and well-functioning proteome and can integrate metabolism with physiological and pathological processes. In recent years, post-translational modifications have become a research hotspot, with methylation, phosphorylation, acetylation and succinylation being the main focus. SUMOylated proteins are predominantly localized in the nucleus, and SUMO regulates nuclear processes, including cell cycle control and DNA repair. SUMOylated proteins are predominantly localized in the nucleus, and SUMO regulates nuclear processes, including cell cycle control and DNA repair. SUMOylation has been increasingly implicated in cancer, Alzheimer’s, and Parkinson’s diseases. Therefore, identification and characterization SUMOylation sites are essential for determining modification-specific proteomics. This study aims to propose a novel schema for predicting protein SUMOylation sites based on the incorporation of natural language features (Word2Vec) and sequence-based features. In addition, the novel model, called RSX_SUMO, is proposed for the prediction of protein SUMOylation sites. Our experiments reveal that the performance of RSX_SUMO model achieves the highest performance in both five-fold cross-validation and independent testing, obtain the performance on independent testing with acccuracy at 88.6% and MCC value of 0.743. In addition, the comparison with several existing prediction models show that our proposed model outperforms and obtains the highest performance. We hope that our findings would provide effective suggestions and be a great helpful for researchers related to their related studies.

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Acknowledgment

The authors sincerely thank to TUEBA for partly financial supported this research under the TNU-level project ID: ÐH2023-TN08-05.

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Authors and Affiliations

  1. University of Economics and Business Administration, Thai Nguyen, Vietnam

    Thi-Xuan Tran

  2. University of Information and Communication Technology, Thai Nguyen, Vietnam

    Van-Nui Nguyen

  3. Professional Master Program in Artificial Intelligence in Medicine, Taipei Medical University, New Taipei, Taiwan

    Nguyen Quoc Khanh Le

  4. Research Center for Artificial Intelligence in Medicine, Taipei Medical University, New Taipei, Taiwan

    Nguyen Quoc Khanh Le

Authors
  1. Thi-Xuan Tran
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  2. Van-Nui Nguyen
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  3. Nguyen Quoc Khanh Le
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Corresponding author

Correspondence to Van-Nui Nguyen .

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Editors and Affiliations

  1. Wroclaw University of Science and Technology, Wroclaw, Poland

    Ngoc Thanh Nguyen

  2. Northumbria University, Newcastle, UK

    Hoa Le-Minh

  3. The University of Danang – Vietnam-Korea University of Information and Communication Technology, Danang, Vietnam

    Cong-Phap Huynh

  4. The University of Danang – Vietnam-Korea University of Information and Communication Technology, Danang, Vietnam

    Quang-Vu Nguyen

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Tran, TX., Nguyen, VN., Le, N. (2023). Incorporating Natural Language-Based and Sequence-Based Features to Predict Protein Sumoylation Sites. In: Nguyen, N.T., Le-Minh, H., Huynh, CP., Nguyen, QV. (eds) The 12th Conference on Information Technology and Its Applications. CITA 2023. Lecture Notes in Networks and Systems, vol 734. Springer, Cham. https://doi.org/10.1007/978-3-031-36886-8_7

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