GTExome: Modeling commonly expressed missense mutations in the human genome

Abstract

A web application, GTExome, is described that quickly identifies, classifies, and models missense mutations in commonly expressed human proteins. GTExome can be used to categorize genomic mutation data with tissue specific expression data from the Genotype-Tissue Expression (GTEx) project. Commonly expressed missense mutations in proteins from a wide range of tissue types can be selected and assessed for modeling suitability. Information about the consequences of each mutation is provided to the user including if disulfide bonds, hydrogen bonds, or salt bridges are broken, buried prolines introduced, buried charges are created or lost, charge is swapped, a buried glycine is replaced, or if the residue that would be removed is a proline in the cis configuration. Also, if the mutation site is in a binding pocket the number of pockets and their volumes are reported. The user can assess this information and then select from available experimental or computationally predicted structures of native proteins to create, visualize, and download a model of the mutated protein using Fast and Accurate Side-chain Protein Repacking (FASPR). For AlphaFold modeled proteins, confidence scores for native proteins are provided. Using this tool, we explored a set of 9,666 common missense mutations from a variety of tissues from GTEx and show that most mutations can be modeled using this tool to facilitate studies of proteinprotein and protein-drug interactions. The open-source tool is freely available at https://pharmacogenomics.clas.ucdenver.edu/gtexome/ Author SummaryGTExome greatly simplifies the process of studying the three-dimensional structures of proteins containing missense mutations that are critical to understanding human health. In contrast to current state-of-the-art methods, users with no external software or specialized training can rapidly produce three-dimensional structures of any possible mutation in nearly any protein in the human exome. Accomplishing this requires reliance on AlphaFold based structural models. We therefore compared the protein models created by GTExome where possible to known experimental structures. We find that by avoiding specific mutations, the majority of proteins are amenable to being modeled by GTExome with similar results to the direct use of AlphaFold.