Protein Space

Protein space encompasses the vast universe of possible protein sequences and structures, and research aims to understand and navigate this space to design proteins with desired properties. Current efforts focus on developing generative models, including diffusion models and transformer-based architectures, to create novel protein sequences and predict their structures and interactions, often leveraging protein language models and graph neural networks to incorporate sequence and structural information. These advancements are significantly impacting protein engineering, drug discovery, and our fundamental understanding of biological processes by enabling efficient design of proteins with tailored functions and improved prediction of protein-protein interactions.