Proteins are shaped by gradual evolution under biophysical and functional constraints. Protein language models learn rich evolutionary constraints from large-scale sequences, and discrete diffusion-based protein language models~(\eg, DPLMs) are promising for both understanding and generation. However, existing DPLMs typically rely on masking-based absorbing diffusion that contradicts a simple biological intuition: proteins evolve through accumulated edits, not by emerging from masks. Consequently, these frameworks lack explicit pretraining objectives for substitution and insertion/deletion (indel) operations, limiting both optimization-style post-editing and flexible guided generation. To address these limitations, we present DPLM-Evo, an evolutionary discrete diffusion framework that explicitly predicts substitution, insertion, and deletion operations during denoising. DPLM-Evo decouples an upsampled-length latent alignment space from the variable-length observed sequence space, which makes indel-aware generation tractable and enables adaptive scaffold growth throughout the process with negligible computational overhead. To better align substitutions with real evolution, we further introduce a contextualized evolutionary noising kernel that produces biologically informed, context-dependent mutation patterns. Across tasks, DPLM-Evo improves sequence understanding and achieves state-of-the-art mutation effect prediction performance on ProteinGym in the single-sequence setting. It also enables variable-length simulated evolution, and post-editing/optimization of existing proteins via explicit edit trajectories.

翻译：蛋白质在生物物理与功能性约束下通过渐进进化塑造而成。蛋白质语言模型从大规模序列中学习丰富的进化约束，而基于离散扩散的蛋白质语言模型（如DPLM）在理解与生成方面均展现出潜力。然而，现有DPLM通常依赖于基于掩码的吸收扩散，这与简单生物学直觉相悖：蛋白质通过累积编辑而非从掩码中涌现而进化。因此，这些框架缺乏针对替换与插入/缺失操作的显式预训练目标，限制了优化式后编辑与灵活引导生成。为解决上述局限，我们提出DPLM-Evo，一种进化离散扩散框架，其在去噪过程中显式预测替换、插入与缺失操作。DPLM-Evo将上采样长度下的潜在对齐空间与可变长度的观测序列空间解耦，使可感知插入缺失的生成易于处理，并能在整个过程中以可忽略的额外计算开销实现自适应支架增长。为更好地将替换与真实进化对齐，我们进一步引入一种上下文化的进化噪声核，该核可产生具有生物学信息的上下文相关突变模式。在多项任务中，DPLM-Evo提升了序列理解能力，并在单序列设置下于ProteinGym基准上达到突变效应预测的最优性能。该模型还支持可变长度的模拟进化，以及通过显式编辑轨迹对现有蛋白质进行后编辑与优化。