Understanding how proteins interact with membrane surfaces is important for drug discovery studies in which a drug may target a membrane protein. One of the main proteins of interest for COVID-19 antibodies is the ACE2 protein that binds to the neutral amino acid transporter B0AT1. B0AT1 sits in the membrane and understanding how movement or perturbation of that membrane might after the binding sites available on the ACE2 protein is important in creating effective antibodies to prevent viral infection. To study the dynamics associated with membranes, LLNL scientists created MemSurfer. MemSurfer is an efficient and versatile tool to compute and analyze membrane surfaces found in a wide variety of large-scale molecular simulations.
In order to identify new, unknown proteins associated with viruses, such as COVID-19, it is easiest to start by identifying structurally related proteins. LLNL scientists have created tools that identify structurally related proteins and their relevant residues, called cSpan. The cSpan (sequence conservation in structurally conserved “span” regions) calculation is a quantitative measure of residue conservation in local structure context. It is used to identify residues on a protein that are conserved with respect to a set of structurally related proteins.
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).
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.