CPSea: Large-scale cyclic peptide-protein complex dataset for machine learning in cyclic peptide design
Ziyi Yang, Hanyuan Xie, Yinjun Jia, Xiangzhe Kong, Jiqing Zheng, Ziting Zhang, Yang Liu, Lei Liu, Yanyan Lan
Cyclic peptides exhibit better binding affinity and proteolytic stability compared to their linear counterparts. However, the development of cyclic peptide design models is hindered by the scarcity of data. To address this, we introduce CPSea (Cyclic Peptide Sea), a dataset of 2.64 million cyclic peptide-receptor complexes, curated through systematic mining of the AlphaFold Database (AFDB). Our pipeline extracts compact domains from AFDB, identifies cyclization sites using the β-carbon (Cβ) atom distance thresholds, and applies multi-stage filtering to ensure structural validity and binding compatibility. Compared with existing data of cyclic peptides, CPSea shows similar property distributions in terms of structure validity and wet-lab compatibility. To our knowledge, CPSea is the largest cyclic peptide-receptor dataset to date, enabling end-to-end model training for the first time. Our method is scalable, as cyclic peptides with varied cyclization linkages can be obtained by adjusting (Cβ) distance criteria, making it possible to develop specialized models for different cyclization types.