CreoPep: A Universal Deep Learning Framework for Target-Specific Peptide Design and Optimization
Journal:
arXiv
Published Date:
May 5, 2025
Abstract
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 $\alpha$7 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.
