Protein Representation

Protein representation learning aims to create computationally efficient and informative representations of proteins, crucial for diverse applications like drug discovery and understanding biological processes. Current research heavily utilizes graph neural networks (GNNs) and protein language models (PLMs), often integrating multiple data modalities such as amino acid sequences, 3D structures, and functional annotations to capture complex relationships. These advanced models are improving predictions of protein flexibility, function, and interactions, leading to more accurate and efficient analyses in biomedicine and related fields. The development of robust and versatile protein representations is transforming biological research by enabling more powerful computational tools for analyzing and predicting protein behavior.