CFP-Gen: Combinatorial Functional Protein Generation via Diffusion Language Models

• URL: http://arxiv.org/abs/2505.22869v1
• Date: Wed, 28 May 2025 21:05:46 GMT
• Title: CFP-Gen: Combinatorial Functional Protein Generation via Diffusion Language Models
• Abstract summary: CFP-Gen is a novel diffusion language model for Combinatorial Functional Protein GEN.<n>It enables the de novo protein design by integrating multimodal conditions with functional, sequence, and structural constraints.
• Score: 16.17372298740389
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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