YAPP-CD provides a rich dataset for protein-peptide interaction prediction and design, suggesting enhanced research capabilities.
Recent advances in artificial intelligence have transformed numerous scientific fields, with structural biology standing out as a major beneficiary, highlighted by AlphaFold.Although many AI-based approaches now address core challenges such as protein structure prediction and protein design, peptide-related problems remain less explored.Peptides exhibit conformational flexibility, resulting in distinctive structural and functional behaviors.Moreover, the lack of standardized, high-quality datasets has forced researchers to assemble task-specific collections, slowing progress and limiting comparability.In this work, we present YAPP-CD, a protein-peptide complex database designed to support diverse computational studies, including training deep learning models for protein-peptide interaction prediction and peptide design.The dataset also facilitates classical approaches such as template-based peptide docking.
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Joon-Sang Park (2026) studied this question.
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