Enhancing the identification of malonylation sites using AlphaFold2 and ensemble learning.

Journal: Molecular diversity
Published Date: Oct 5, 2025

Abstract

Malonylation modification of proteins is closely related to many diseases, such as diabetes and cancer. Therefore, accurate identification of malonylation modification sites is crucial for elucidating the molecular mechanisms underlying these diseases. Traditional experimental methods suffer from the problems of high cost, long cycle time, difficulty, etc. With advancements in artificial intelligence, the prediction of protein post-translational modification sites through computational methods has emerged as a vital complement to experimental approaches. In this paper, we present a malonylation site prediction model, Catsoft_Kmalsite, the core innovation of which lies in its integration of complementary information from protein three-dimensional structural features and sequence/physicochemical features, coupled with a soft voting ensemble strategy based on Bayesian-optimized base classifiers. Specifically, we utilize AlphaFold2 to acquire protein tertiary structural information and employ CTDC, EAAC, and EGAAC methods to extract protein sequence and physicochemical features. Subsequently, two base classifiers are constructed using the CatBoost algorithm based on these two distinct feature sets, respectively. Following parameter fine-tuning of the base classifiers via Bayesian optimization, they are ultimately integrated using a soft voting strategy. All ablation experimental results show that the Catsoft_Kmalsite model exhibited good robustness and generalization ability. Across six metrics, including AUC, ACC, Sen, Pre, F1, and MCC, the model achieved average performances of 94.03%, 87.91%, 89.15%, 86.91%, 88.00%, and 0.7585, respectively, in fivefold cross-validation and specific performance of 95.18%, 89.55%, 90.87%, 88.79%, 89.82%, and 0.7912 on the independent test set; Catsoft_Kmalsite also outperformed other state-of-the-art studies in all evaluated metrics. Furthermore, we have developed a website for users to use ( http://1.94.102.146:8501/Catsoft_Kmalsite ). The code and dataset of Catsoft_Kmalsite are available at https://github.com/flyinsky6/Catsoft_Kmalsite .

Authors

  • Linlin Xu
    Department of Laboratory Medicine, The Second Affiliated Hospital of Guilin Medical University, Guilin, China.
  • Yuting Qian
    Division of Health Policy and Economics, Department of Population Health Sciences, Weill Cornell Medical College, New York, New York, USA.
  • Jiayi Yang
    School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China.
  • Xiaowei Xu
    Department of Information Science, University of Arkansas, Little Rock, Arkansas, United States of America.
  • Zhiqiang Li
    The Affiliated Hospital of Qingdao University, The Biomedical Sciences Institute of Qingdao University (Qingdao Branch of SJTU Bio-X Institutes), Qingdao University, Qingdao, 266003, China.
  • Yanhan Wang
    School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, 221004, China.
  • Enhui Lv
    School of Medical Information & Engineering, Xuzhou Medical University, Xuzhou, Jiangsu, China.
  • Xingxing Kang
    School of Medical Information & Engineering, Xuzhou Medical University, Xuzhou, Jiangsu, China.
  • Hongwei Zhang
    Jiangsu Provincial Key Laboratory for TCM Evaluation and Translational Development, China Pharmaceutical University, Nanjing, Jiangsu 211198, China.
  • Yaping Lu
    Sinopharm Genomics Technology Co Ltd, Changzhou, Jiangsu, China.
  • Fei Wang
    Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, United States.
  • Xin Liu
    Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences, Weifang, Shandong, China.

Keywords

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