Structure-aware Multi-task Collaborative Learning: a multi-task collaborative learning framework for peptide-protein interaction prediction based on structure-aware protein language models.

“Structure-aware Multi-task Collaborative Learning: a multi-task collaborative learning framework for peptide-protein interaction prediction based on structure-aware protein language models. He S(1)(2), Tang D(3), Zhu T(1)(2), Zhu Z(1)(2), Liu Y(4), Zhang J(1)(2). Author information: (1)School of Artificial Intelligence, Shenzhen University, 3688 Nanhai Avenue, 518060 Shenzhen, China. (2)National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, 3688 Nanhai Avenue, 518060 Shenzhen, China. (3)School of Computer Science, Guangdong University of Technology, 100 Waihuan West Road, 510006 Guangzhou, China. (4)School of Artificial Intelligence, Shenzhen Technology University, 3002 Lantian Road, 518118 Shenzhen, China. The peptide drug points to a promising new therapeutic. Precisely predicting the interaction between peptides and proteins is fundamental to the discovery and design of functional peptides. While various computational methods have been proposed for this purpose, constructing an accurate and robust prediction model remains a challenge. In this study, we introduce a structure-aware multi-task collaborative learning (SaMCL) framework for detecting the interaction between peptides and proteins. To the best of our knowledge, SaMCL is the first method capable of performing a multilevel, simultaneous prediction of binary interactions and binding domains in both peptides and proteins. Experimental results demonstrate that SaMCL outperforms several state-of-the-art methods in terms of both prediction accuracy and generalization. Provides a new paradigm for modeling biomolecular interactions. © The Author(s) 2026. Published by Oxford University Press.”