The design of protein sequences with desired functionalities is a fundamental task in protein engineering. Deep generative methods, such as autoregressive models and diffusion models, have greatly accelerated the discovery of novel protein sequences. However, these methods mainly focus on local or shallow residual semantics and suffer from low inference efficiency, large modeling space and high training cost. To address these challenges, we introduce ProtFlow, a fast flow matching-based protein sequence design framework that operates on embeddings derived from semantically meaningful latent space of protein language models. By compressing and smoothing the latent space, ProtFlow enhances performance while training on limited computational resources. Leveraging reflow techniques, ProtFlow enables high-quality single-step sequence generation. Additionally, we develop a joint design pipeline for the design scene of multichain proteins. We evaluate ProtFlow across diverse protein design tasks, including general peptides and long-chain proteins, antimicrobial peptides, and antibodies. Experimental results demonstrate that ProtFlow outperforms task-specific methods in these applications, underscoring its potential and broad applicability in computational protein sequence design and analysis.

本研究解决了蛋白质序列设计中的功能性不足及效率低下的问题。提出的ProtFlow框架通过压缩和光滑潜在空间，提升了蛋白质序列生成的效率与质量，能够在有限的计算资源下实现高效的一步生成。实验结果表明，ProtFlow在多种蛋白质设计任务中优于任务特定的方法，展示了其广泛的应用潜力和价值。

ProtFlow：通过在压缩蛋白质语言模型嵌入上进行流匹配实现快速蛋白质序列设计