Secondary Structure-Guided Novel Protein Sequence Generation with Latent Graph Diffusion
This work addresses the problem of reliable and diverse protein design for researchers in computational biology, offering an incremental improvement over existing deep learning methods.
The paper tackled the challenge of generating diverse protein sequences with varied lengths and shapes while maintaining structural features, introducing CPDiffusion-SS, a latent graph diffusion model that uses secondary structure information to produce novel amino acid sequences, surpassing baseline methods on benchmarks.
The advent of deep learning has introduced efficient approaches for de novo protein sequence design, significantly improving success rates and reducing development costs compared to computational or experimental methods. However, existing methods face challenges in generating proteins with diverse lengths and shapes while maintaining key structural features. To address these challenges, we introduce CPDiffusion-SS, a latent graph diffusion model that generates protein sequences based on coarse-grained secondary structural information. CPDiffusion-SS offers greater flexibility in producing a variety of novel amino acid sequences while preserving overall structural constraints, thus enhancing the reliability and diversity of generated proteins. Experimental analyses demonstrate the significant superiority of the proposed method in producing diverse and novel sequences, with CPDiffusion-SS surpassing popular baseline methods on open benchmarks across various quantitative measurements. Furthermore, we provide a series of case studies to highlight the biological significance of the generation performance by the proposed method. The source code is publicly available at https://github.com/riacd/CPDiffusion-SS