Target-specific peptides, such as conotoxins, exhibit exceptional binding affinity and selectivity toward ion channels and receptors. However, their therapeutic potential remains underutilized due to the limited diversity of natural variants and the labor-intensive nature of traditional optimization strategies. Here, we present CreoPep, a deep learning-based conditional generative framework that integrates masked language modeling with a progressive masking scheme to design high-affinity peptide mutants while uncovering novel structural motifs. CreoPep employs an integrative augmentation pipeline, combining FoldX-based energy screening with temperature-controlled multinomial sampling, to generate structurally and functionally diverse peptides that retain key pharmacological properties. We validate this approach by designing conotoxin inhibitors targeting the α7 nicotinic acetylcholine receptor, achieving submicromolar potency in electrophysiological assays. Structural analysis reveals that CreoPep-generated variants engage in both conserved and novel binding modes, including disulfide-deficient forms, thus expanding beyond conventional design paradigms. Overall, CreoPep offers a robust and generalizable platform that bridges computational peptide design with experimental validation, accelerating the discovery of next-generation peptide therapeutics.
@article{arxiv.2505.02887,
title = {CreoPep: A Universal Deep Learning Framework for Target-Specific Peptide Design and Optimization},
author = {Cheng Ge and Han-Shen Tae and Zhenqiang Zhang and Lu Lu and Zhijie Huang and Yilin Wang and Tao Jiang and Wenqing Cai and Shan Chang and David J. Adams and Rilei Yu},
journal= {arXiv preprint arXiv:2505.02887},
year = {2025}
}