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Structure-aware Multi-task Collaborative Learning: a multi-task collaborative learning framework for peptide-protein interaction prediction based on structure-aware protein language models
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DOI：10.1093/bib/bbag178.png)
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Abstract
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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.
Keywords:
protein-peptide interaction
structure-aware protein language model
collaborative learning framework
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NUCLEIC ACIDS RESEARCH
OAAI
NUCLEIC ACIDS RESEARCHIF13.1
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