PepRAE: Adapting Protein Representations for Target-Conditioned Peptide Design

Designing peptides for protein targets requires jointly generating amino-acid sequences and their bound three-dimensional conformations. Protein sequence and structure models provide protein-model representations, and the shared amino-acid alphabet of proteins and peptides makes these representations a natural starting point for peptide design. Yet these representations are not learned to serve as generative variables for target-conditioned peptide generation. This raises the question of how they can be adapted for this task. We introduce PepRAE, which performs this adaptation by learning feature latents from protein-model representations, while C$\alpha$-based spatial latents explicitly represent the peptide's bound geometry. The feature latents are learned through two reconstruction objectives. Representation reconstruction encourages the feature latents to support reconstruction of the protein-model representations. Peptide reconstruction provides downstream supervision by decoding peptide sequence and bound conformation from reconstructed features and spatial latents. After reconstruction training, target-conditioned latent diffusion jointly models peptide feature and spatial latents. Reverse diffusion followed by decoding generates peptide sequences and bound conformations. With Boltz-2 as the representation source, PepRAE achieves the highest amino-acid recovery, the lowest structural RMSDs, and the lowest Rosetta interface score ($\Delta G$) among the evaluated baselines on the LNR benchmark. To assess dependence on the representation source, we instantiate PepRAE with the pretrained sequence model ESM-2 and with LightEnc, our lightweight sequence encoder trained from scratch. Across the three separately trained variants, peptide reconstruction remains accurate and generation performance is broadly comparable. Together, these results show that the same formulation can learn feature latents from distinct protein-model representations for target-conditioned peptide design.