Waring, A. | bioRxiv Science

bioRxiv2024

Transforming chemigenetic bimolecular fluorescence complementation systems into chemical dimerizers using chemistry

Chemigenetic tags are versatile labels for fluorescence microscopy that combine some of the advantages of genetically encoded tags with small molecule fluorophores. The Fluorescence Activating and absorbance Shifting Tags (FASTs) bind a series of highly fluorogenic and cell-permeable chromophores. Furthermore, FASTs can be used in complementation-based systems for detecting or inducing protein-protein interactions, depending on the exact FAST protein variant chosen. In this study, we systematically explore substitution patterns on FAST fluorogens and generate a series of fluorogens that bind to FAST variants, thereby activating their fluorescence. This effort led to the discovery of a novel fluorogen with superior properties, as well as a fluorogen that transforms splitFAST systems into a fluorogenic dimerizer, eliminating the need for additional protein engineering.

biochemistry↗

bioRxiv2019

Enhanced prediction of gene and missense rare-variant pathogenicity by joint analysis of gene burden and amino-acid residue position

BackgroundAlthough rare-missense variants in Mendelian disease-genes have been noted to cluster in specific regions of proteins, it is not clear how to consider this information when evaluating the pathogenicity of a gene or variant. Here we introduce methods for gene-association and variant-interpretation that utilise this powerful signal. MethodsWe present a case-control rare-variant association test, ClusterBurden, that combines information on both variant-burden and variant-clustering. We then introduce a data-driven modelling framework to estimate mutational hotspots in genes with missense variant-clustering and integrate further in-silico predictors into the models. ResultsWe show that ClusterBurden can increase statistical power to scan for putative disease-genes, driven by missense variants, in simulated data and a 34-gene panel dataset of 5,338 cases of hypertrophic cardiomyopathy. We demonstrate that data-driven models can allow quantitative application of the ACMG criteria PM1 and PP3, to resolve a wide range of pathogenicity potential amongst variants of uncertain significance. A web application (Pathogenicity_by_Position) is accessible for missense variant risk prediction of six sarcomeric genes and an R package is available for association testing using ClusterBurden. ConclusionThe inclusion of missense residue position enhances the power of disease-gene association and improves rare-variant pathogenicity interpretation.

genetics↗