Domain-Aware Protein Tokenization for Structure Generation

De novo protein backbone generation is a central task in protein design, and discrete structural representations provide a multimodal generative paradigm for this task by enabling the unified modeling of protein structure, sequence, and function. However, existing methods focus primarily on residue-level local geometry and rarely use evolutionarily conserved substructures such as domains as training signals, limiting their ability to capture the modular organization of proteins. We introduce DPAR, a domain-aware discrete framework that encodes backbone geometry and domain segmentation as a sequence of discrete symbols. DPAR combines region-aware tokenization that captures both domain and global content with autoregressive generation of structure tokens and domain boundaries, using the resulting layout to guide segment-wise conditioning of a flow-matching decoder. We validate DPAR on reconstruction and generation tasks and demonstrate that it matches or outperforms existing models based on residue-level protein structure tokenizers. We show how domain layouts enable segment-wise conditioning during inference, which boosts backbone designability. Together, these properties establish DPAR as a promising framework for domain-aware discrete protein structure generation.