Protein Embeddings

Protein embeddings are numerical representations of protein sequences and structures, aiming to capture their essential biological information for various predictive tasks. Current research heavily utilizes deep learning, employing architectures like transformers, graph neural networks, and diffusion models to generate these embeddings, often incorporating both sequence and 3D structural data, sometimes even integrating functional annotations. This work is significantly impacting protein engineering, drug discovery, and our fundamental understanding of protein function by enabling more accurate predictions of protein properties, interactions, and mutational effects. The development of scalable and efficient methods for generating and utilizing these embeddings is a key focus.