Related Experiment Video
Updated: Oct 25, 2026
Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
Published on: January 26, 2024
Bilingua-DPP-IV: A Bimodal Fusion Convolutional Neural Network Model for Prediction of Dipeptidyl Peptidase IV
Zhixing Zhang1, Qiule Yu1, Chen Yang1
1Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China.
Motivation:
Dipeptidyl peptidase IV (DPP-IV) is a key therapeutic target for type 2 diabetes, and its inhibitory peptides are a promising class of therapeutic agents. However, the discovery of these promising drugs has been restricted, as many existing computational predictors are based on datasets that have remained static for several years.
Results:
Here, we first presented a newly curated and expanded benchmark dataset, then introduced Bilingua-DPP-IV, a bimodal deep learning framework that integrates features from both amino acid sequences and molecular structures. Our approach leveraged embeddings from the large-scale pretrained models ESM C and Uni-Mol2, processed through a convolutional neural network with an optimized gated fusion mechanism. On an independent test set, the Bilingua-DPP-IV ensemble model achieved a Matthews correlation coefficient of 0.764 and an area under the receiver operating characteristic curve of 0.963. On an external validation set, the model also demonstrated an accuracy of 0.898, exceeding the performance of existing state-of-the-art predictors, such as StructuralDPPIV.