Understanding proteins, their structures, and how they may be similar is necessary for many applications from basic science to developing vaccines for COVID-19. Most computational models that predict protein structure similarity consider certain features at the expense of others. To get a holistic picture of protein structures, LLNL scientists developed the Local-Global Alignment (LGA) model. The model works by predicting protein structures by considering both local and global structures without compromising either feature. To do this, LGA can use data corresponding to clusters and/or fragments of proteins. The data can be inputted manually or for ease of use, uploaded from the Protein Data Bank (PDB). Not only can LGA predict protein structure similarity, it can also predict sequence similarity. It has been reduced to practice and shows better performance in comparison to known computational models for predicting protein structures such as DALI, CE, and ProSup.

US patent 8,024,127 "Local-global alignment for finding 3D similarities in protein structures " (LLNL internal case # IL11160)