The field of protein representation learning is rapidly advancing, with a focus on developing innovative methods to extract meaningful computational representations from protein data. This has led to significant improvements in deciphering the intricate structures and diverse functions of proteins, which is essential for advancing our understanding of life at the molecular level.

A key trend in this area is the integration of multiple modalities, such as 3D structural and 1D sequence data, to capture fine-grained structural representations of proteins and their interactions. This has enabled the development of more accurate models for predicting protein interactions and functions.

Noteworthy papers in this area include:

• Advances in Protein Representation Learning: Methods, Applications, and Future Directions, which provides a comprehensive review of protein representation learning research and outlines future directions.
• Multi-Modality Representation Learning for Antibody-Antigen Interactions Prediction, which introduces a novel framework for predicting antibody-antigen interactions using graph attention networks and normalized adaptive graph convolution networks.
• Discriminative protein sequence modelling with Latent Space Diffusion, which presents a new architecture for protein sequence representation learning using latent space diffusion models.