用扩散模型同时满足多种功能结构约束，生成新型多功能蛋白。
CFP-Gen: Combinatorial Functional Protein Generation via Diffusion Language Models
- 通过可组合的功能注释动态调节蛋白特征分布。
- 生成蛋白功能接近天然蛋白，多任务设计成功率高。
- 适合需要精准控制功能与结构的蛋白质设计研究者。
现有蛋白质语言模型基于单一模态条件生成序列，难以同时满足多模态下的多重约束。本文提出CFP-Gen，一种用于组合式功能蛋白生成的扩散语言模型。该模型通过集成功能、序列和结构约束，实现从头设计。引入注释引导的特征调制（AGFM）模块，根据可组合的功能注释（如GO术语、IPR域、EC编号）动态调整蛋白特征分布；同时，残基可控的功能编码（RCFE）模块捕捉残基间相互作用，实现更精确控制。此外，可无缝集成现成的3D结构编码器以施加几何约束。实验表明，CFP-Gen能高效生成功能接近天然蛋白的新蛋白，并在设计多功能蛋白方面表现出高成功率。代码与数据见https://github.com/yinjunbo/cfpgen。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。