BertADP: a fine-tuned protein language model for anti-diabetic peptide prediction.

Journal: BMC biology
Published Date: Jul 15, 2025

Abstract

BACKGROUND: Diabetes is a global metabolic disease that urgently calls for the development of new and effective therapeutic agents. Anti-diabetic peptides (ADPs) have emerged as a research hotspot due to their therapeutic potential and natural safety, representing a promising class of functional peptides for diabetic management. However, conventional computational approaches for ADPs prediction mainly rely on manually extracted sequence features. These methods often lack generalizability and perform poorly on short peptides, thereby hindering effective ADPs discovery.

Authors

  • Xueqin Xie
    College of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
  • Changchun Wu
    Department of Mechanical Engineering, University of Hong Kong, Hong Kong, Hong Kong.
  • Yixuan Qi
    The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
  • Shanghua Liu
    The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.
  • Jian Huang
    Center for Informational Biology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, P. R. China.
  • Hao Lyu
    Department of Chemical Engineering, Stanford University, Stanford, CA 94305, USA.
  • Fuying Dao
    School of Biological Sciences, Nanyang Technological University, Singapore 639798, Singapore.
  • Hao Lin
    Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, Zhejiang, China.

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