Automating protein classification via structural similarity has been a technique employeed by researchers for a while. The current methods generally only assess structure similarity using a single metric (e.g., Z-score) and only evaluate similar conformations of secondary structure elements. In order to accurately access structure similarity, LLNL scientists created a method called STRucture ALigment-based Clustering of Proteins (STRALCP). STRALCP is a structure alignment-based approach invented for the purpose of automated protein structure classification. For a given set of proteins, STRALCP generates detailed information about global and local similarities between pairs of protein structures, identifies the fragments that are structurally conserved among the proteins, and uses these fragments to classify the structures accordingly. This new method overcomes the limitations of previous methods and more accurately classes proteins which is a key step in studying proteins that might lead to vaccine development.
US patent 8,467,971 "Structure based alignment and clustering of proteins (STRALCP) " (LLNL internal case # IL11696)