BUDDY: An interactive web platform to accelerate the functional modeling of human missense variants using Saccharomyces cerevisiae orthologs

Abstract

The advent of next-generation sequencing has transformed clinical genetics, accelerating the identification of human genetic variants. However, the labor-intensive process of functional validation of Variants of Uncertain Significance (VUS) remains a significant diagnostic barrier. While expressing humanized genes in Saccharomyces cerevisiae is a common approach for modeling missense variants with functional assays, using native yeast orthologs provides a more streamlined approach that preserves species-specific interactions within multisubunit complexes. However, taking this approach relies on a fragmented workflow that requires bioinformatic experience. To address this issue, we developed the Bioinformatic Utility for Diagnostic Discovery in Yeast (BUDDY), an integrated web platform designed to streamline variant analysis by connecting bioinformatic tools and databases including ClinVar. Features include amino acid alignments, a protein structure viewer that displays a superposition of yeast and human proteins, pathogenicity prediction data, CRISPR-Cas9 reagent design, and yeast phenotypes to provide potential functional assays. The platform also contains an artificial intelligence (AI)-assisted experimental design feature that evaluates the utility of modeling specific variant and gene combinations in yeast and can assist with phenotype selection. BUDDY significantly accelerated the bioinformatic preparation time needed for ortholog variant modeling by an experienced biologist. Additionally, without prior bioinformatics training, a first-year medical student used BUDDY to develop a self-directed project and proposed a study of five Lynch syndrome-associated VUS using a forward mutation rate assay. BUDDY is designed to accelerate the investigation of human variants in yeast and has a high potential utility for use in resource-constrained settings such as course-based undergraduate research experiences (CUREs).