CFP-Gen：基于扩散语言模型的组合功能蛋白质生成
CFP-Gen: Combinatorial Functional Protein Generation via Diffusion Language Models
AI总结：
针对现有蛋白质语言模型难以同时满足跨模态多约束的问题，提出扩散语言模型CFP-Gen，通过AGFM和RCFE模块及3D结构编码器整合多模态条件，实现了高成功率的多功能蛋白质高通量生成。
摘要：
现有的PLM基于特定模态的单一条件约束生成蛋白质序列，难以同时满足跨不同模态的多个约束。在本研究中，我们提出了CFP-Gen，一种用于组合功能蛋白质生成（Combinatorial Functional Protein GENeration）的新型扩散语言模型。CFP-Gen通过将多模态条件与功能、序列和结构约束相结合，促进了从头蛋白质设计。具体而言，引入了标注引导特征调制（AGFM）模块，根据可组合的功能标注（例如GO术语、IPR结构域和EC编号）动态调整蛋白质特征分布。同时，残基控制功能编码（RCFE）模块捕获残基级相互作用以确保更精确的控制。此外，现成的3D结构编码器可以无缝集成以施加几何约束。我们证明CFP-Gen能够高通量生成功能可与天然蛋白质相媲美的新型蛋白质，并在设计多功能蛋白质方面实现了高成功率。代码和数据可在https://github.com/yinjunbo/cfpgen获取。
英文摘要：
Existing PLMs generate protein sequences based on a single-condition constraint from a specific modality, struggling to simultaneously satisfy multiple constraints across different modalities. In this work, we introduce CFP-Gen, a novel diffusion language model for Combinatorial Functional Protein GENeration. CFP-Gen facilitates the de novo protein design by integrating multimodal conditions with functional, sequence, and structural constraints. Specifically, an Annotation-Guided Feature Modulation (AGFM) module is introduced to dynamically adjust the protein feature distribution based on composable functional annotations, e.g., GO terms, IPR domains and EC numbers. Meanwhile, the Residue-Controlled Functional Encoding (RCFE) module captures residue-wise interaction to ensure more precise control. Additionally, off-the-shelf 3D structure encoders can be seamlessly integrated to impose geometric constraints. We demonstrate that CFP-Gen enables high-throughput generation of novel proteins with functionality comparable to natural proteins, while achieving a high success rate in designing multifunctional proteins. Code and data available at https://github.com/yinjunbo/cfpgen.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
