Multi-stage attention-based extraction and fusion of protein sequence and structural features for protein function prediction.

Journal: Bioinformatics (Oxford, England)
Published Date: Jun 26, 2025

Abstract

MOTIVATION: Protein function prediction is important for drug development and disease treatment. Recently, deep learning methods have leveraged protein sequence and structural information, achieving remarkable progress in the field of protein function prediction. However, existing methods ignore the complex multimodal interaction information between sequence and structural features. Since protein sequence and structural information reveal the functional characteristics of proteins from different perspectives, it is challenging to effectively fuse the information from these two modalities to portray protein functions more comprehensively. In addition, current methods have difficulty in effectively capturing long-range dependencies and global contextual information in protein sequences during feature extraction, thus limiting the ability of the model to recognize critical functional residues.

Authors

  • Meiling Liu
    Key Laboratory of Chemical Biology and Traditional Chinese Medicine Research (Ministry of Education), College of Chemistry and Chemical Engineering, Hunan Normal University, Changsha, 410081, China.
  • Shuangshuang Wang
    College of Computer and Control Engineering, Northeast Forestry University, Harbin, 150040, China.
  • Zeyu Luo
    Chongqing Key Laboratory of Vector Insects.
  • Guohua Wang
    School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.
  • Yuming Zhao

Keywords

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