🤖 AI Summary
This work addresses the challenge in designing antigen-specific binding peptides, where physicochemical features associated with binding affinity often overlap with those linked to toxicity in the representation space, making it difficult to simultaneously achieve high specificity and low toxicity. To overcome this, the authors propose Pepti-drift, a framework that introduces an antigen-conditioned drift mechanism in the peptide embedding space. This mechanism steers latent representations in a single step toward antigen-compatible binding regions while actively repelling toxic regions. The approach employs a staged training strategy—first optimizing binding affinity and subsequently enhancing toxicity avoidance—to effectively decouple these competing objectives. Experimental results demonstrate that Pepti-drift efficiently generates peptide candidates exhibiting both high antigen specificity and low toxicity, highlighting its significant potential for therapeutic peptide design.
📝 Abstract
Peptides are a promising therapeutic modality that combine the chemical tunability of small molecules with the target specificity of macromolecular therapeutics. However, designing antigen-specific binding peptides while avoiding toxicity remains a major challenge for therapeutic peptide discovery. Here, we present Pepti-drift, a toxicity-aware latent refinement framework that generates peptide candidates through a single antigen-conditioned drift step. In a peptide embedding space, Pepti-drift learns to attract generated peptide latents toward antigen-matched binding peptides while repelling them from toxicity-associated regions. This is challenging because binding-promoting physicochemical features often overlap with toxicity-associated features in peptide representation space. To address this, we introduce a warm-up strategy to stabilize this competing objective by first learning binding-oriented attraction and then increasing toxicity repulsion.