A Latent Diffusion Model for Protein Structure Generation
This work addresses the problem of protein design for synthetic biology applications like drug discovery, representing an incremental advancement in computational methods for protein structure generation.
The authors tackled the challenging problem of generating novel protein structures by proposing a latent diffusion model that reduces complexity and captures natural protein distributions in a condensed latent space, resulting in effective generation of novel protein backbone structures with high designability and efficiency.
Proteins are complex biomolecules that perform a variety of crucial functions within living organisms. Designing and generating novel proteins can pave the way for many future synthetic biology applications, including drug discovery. However, it remains a challenging computational task due to the large modeling space of protein structures. In this study, we propose a latent diffusion model that can reduce the complexity of protein modeling while flexibly capturing the distribution of natural protein structures in a condensed latent space. Specifically, we propose an equivariant protein autoencoder that embeds proteins into a latent space and then uses an equivariant diffusion model to learn the distribution of the latent protein representations. Experimental results demonstrate that our method can effectively generate novel protein backbone structures with high designability and efficiency. The code will be made publicly available at https://github.com/divelab/AIRS/tree/main/OpenProt/LatentDiff