Structure-aware language modeling enhances peptide conditional generation

Recent studies have demonstrated that protein language models can effectively drive receptor-conditioned peptide generation. However, existing language-model-based approaches rely almost exclusively on sequence context to learn compatibility between peptide binders and their target proteins. It remains unclear whether incorporating target protein structural information can yield additional benefits, particularly with respect to the rationality of spatial conformations. Furthermore, most language-model-based methods do not explicitly account for the receptor site where peptides are expected to bind, yet such binding sites are critical for functional peptides. To bridge this gap, we investigate how structure-aware protein language models contribute to receptor-conditioned peptide design and introduce binding-site constraints within the language-model paradigm to enable site-specific generation. Experimental results show that combining receptor sequences with structural conditions effectively improves the spatial conformational rationality of peptide–protein complexes and the complementarity of binding interfaces. At the local scale, introducing binding-site constraints shifts generated candidate peptides toward the target region, indicating that binding-site prompts exert a soft directional bias. Code and data will be released at https://github.com/anonymous/VersaPep.