用联合生成模型同时设计蛋白序列与结构，提升功能与折叠性能。
Co-Generative De Novo Functional Protein Design
- 联合解码序列与结构令牌，同步优化功能与折叠性。
- 功能一致性提升6.1%，折叠性提升3.2%，优于最强基线。
- 适合蛋白质工程与新药研发人员快速生成功能性蛋白。
从头功能蛋白设计旨在不依赖进化模板的情况下生成具备指定生化功能的蛋白序列，广泛应用于生物技术和医学领域。现有方法或采用直接的功能-序列映射，或采取结构-序列解耦生成策略，但难以同时实现功能性和可折叠性。为此，我们提出CodeFP——一种用于从头功能蛋白设计的协同生成蛋白语言模型，能够同时解码序列与结构令牌，从而更优地实现功能与折叠性的统一。CodeFP利用功能局部结构增强功能语义编码，克服扁平编码向结构令牌转换不佳的问题；同时引入辅助功能监督，缓解因结构到令牌的一对多映射带来的训练歧义。大量实验表明，CodeFP在功能一致性上平均提升6.1%，在折叠性上平均提升3.2%，优于最强基线。
原文摘要 · Abstract (English)
De novo functional protein design aims to generate protein sequences that realize specified biochemical functions without relying on evolutionary templates, enabling broad applications in biotechnology and medicine. Existing approaches adopt either direct function-to-sequence mapping or decoupled structure-sequence generation strategies but often fail to achieve functionality and foldability simultaneously. To address this, we propose CodeFP, a Co-generative protein language model for de novo Functional Protein design that simultaneously decodes sequence and structure tokens, thereby enabling superior simultaneous realization of functionality and foldability. CodeFP utilizes functional local structures to enrich functional semantic encodings, overcoming the suboptimal translation of flat encodings into structure tokens, while introducing auxiliary functional supervision to alleviate training ambiguity stemming from the one-to-many structure-to-token mapping. Extensive experiments show that CodeFP consistently achieves average improvements of 6.1% in functional consistency and 3.2% in foldability over the strongest baseline.
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