PepSeek: Receptor-Conditioned Representation Learning for Peptide Recognition

Modeling peptide recognition requires learning receptor-dependent interactions from limited affinity measurements while accounting for structural constraints that are expensive to evaluate at scale. We introduce PepSeek, a receptor-conditioned representation learning framework that couples sequence-derived structural priors with few-shot adaptation and coarse-to-fine structural evaluation. The central design principle is to separate reusable molecular representations from target-specific interaction learning, reserving detailed complex modeling for prioritized candidates. PepSeek integrates pretrained residue features, predicted contact topology, and atom-level connectivity into graph representations, and adapts cross-entity interaction layers using limited receptor-specific supervision. This enables structure-informed ranking without requiring an experimentally determined receptor structure at the initial stage. A subsequent retrieval-conditioned structural module shares receptor context across candidates, reducing redundant computation during complex evaluation. Experiments across a panel of G protein-coupled receptors show that target-specific adaptation improves within-receptor affinity ranking over the zero-shot model. In a food-derived peptide library, prioritized candidates exhibit 4.4-fold enrichment for high-confidence complex predictions among the top 50, while structural benchmarking demonstrates reduced receptor-side computation. Together, these findings support integrating receptor-conditioned learning with structural inference to translate sparse affinity measurements into structurally informed prioritization of peptide candidates. PepSeek thus provides a computational framework for translating sparse biochemical supervision into focused, structurally informed hypotheses for experimental testing.