CFP-Gen: Combinatorial Functional Protein Generation via Diffusion Language Models
This addresses the challenge of de novo protein design for researchers in computational biology, enabling more efficient generation of multifunctional proteins, though it appears incremental by building on existing diffusion models.
The paper tackles the problem of generating protein sequences that simultaneously satisfy multiple constraints across different modalities, introducing CFP-Gen, a diffusion language model that integrates multimodal conditions to design novel proteins with functionality comparable to natural ones, achieving a high success rate in multifunctional protein design.
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.