Protein Surface

Protein surface analysis focuses on understanding the relationship between a protein's 3D surface features (geometry and chemical properties) and its biological function, particularly in protein-protein and protein-ligand interactions. Current research employs deep learning models, including graph neural networks and neural field transformers, to predict surface properties like hydrophobic patches and binding sites, often incorporating multi-scale representations that integrate sequence, structure, and surface information. These advancements improve the accuracy and efficiency of predicting protein function and designing proteins with desired characteristics, impacting drug discovery and protein engineering efforts.