A framework for exhaustively mapping functional missense variants

Abstract

Although we now routinely sequence human genomes, we can confidently identify only a fraction of the sequence variants that have a functional impact. Here, we developed a deep mutational scanning framework that produces exhaustive maps for human missense variants by combining random codon mutagenesis and multiplexed functional variation assays with computational imputation and refinement. We applied this framework to four proteins corresponding to six human genes: UBE2I (encoding SUMO E2 conjugase), SUMO1 (small ubiquitin‐like modifier), TPK1 (thiamin pyrophosphokinase), and CALM1/2/3 (three genes encoding the protein calmodulin). The resulting maps recapitulate known protein features and confidently identify pathogenic variation. Assays potentially amenable to deep mutational scanning are already available for 57% of human disease genes, suggesting that DMS could ultimately map functional variation for all human disease genes.

Using deep mutational scanning to benchmark variant effect predictors and identify disease mutations

“…There was considerable variation in functional assays applied between the DMS projects. Growth rate of yeast was the most common technique, and was applied to several human proteins by knocking out the yeast orthologue and replacing it with the human gene that is capable of rescuing the null strain (25). Viral replication assays, performed by quantitative sequencing after a certain time point, were applied to all of the viral proteins.…”

“…As far as we can tell, this effect appears to be unrelated to protein coverage, dataset size or DMS methodology. For example, UBE2I, SUMO1, TPK1 and CALM1 were all studied by the same group using the same methodology (growth rate in yeast) (25), yet UBE2I and SUMO1 show markedly higher correlations with all predictors than the others. Viral proteins also showed low correlations, and in fact, the BLOSUM62 substitution matrix (33) was the most highly correlated with the HA-H3N2 dataset and second highest with env (highest for both when using Kendall's tau).…”

A resource of variant effect predictions of single nucleotide variants in model organisms

“…As additional data become available, mutfunc will be updated to improve coverage and future work could expand the set of mechanisms studied such as drug or small‐molecule binding sites, RNA‐binding interfaces, among others. The effects of variants on molecular and cellular phenotypes are increasingly being probed directly by large‐scale mutagenesis experiments (Fowler & Fields, ; Weile et al , ), which will likely result in improved variant effect prediction algorithms (Gray et al , ). The curation of such experimentally determined effects and the improved algorithms can be integrated in future iterations of mutfunc.…”

Predicting changes in protein stability caused by mutation using sequence‐and structure‐based methods in a CAGI5 blind challenge

“…One of the biggest factors limiting the performance of existing methods, as well as the application of modern machine learning techniques (such as deep learning), is the lack of large and high-quality training datasets of experimentally measured ΔΔG values. A number of highthroughput approaches for obtaining such measurements have been developed, but as of yet no consistent large-scale set has emerged (Findlay, Boyle, Hause, Klein, & Shendure, 2014;Fowler & Fields, 2014;Sahni et al, 2015;Weile et al, 2017).…”