Multiplex assessment of protein variant abundance by massively parallel sequencing

Abstract

Determining the pathogenicity of genetic variants is a critical challenge, and functional assessment is often the only option. Experimentally characterizing millions of possible missense variants in thousands of clinically important genes requires generalizable, scalable assays. We describe Variant Abundance by Massively Parallel Sequencing (VAMP-seq), which measures the effects of thousands of missense variants of a protein on intracellular abundance simultaneously. We apply VAMP-seq to quantify the abundance of 7,801 single amino acid variants of PTEN and TPMT, proteins in which functional variants are clinically actionable. We identify 1,138 PTEN and 777 TPMT variants that result in low protein abundance, and may be pathogenic or alter drug metabolism, respectively. We observe selection for low-abundance PTEN variants in cancer, and reveal that p.Pro38Ser, which accounts for ~10% of PTEN missense variants in melanoma, functions via a dominant negative mechanism. Finally, we demonstrate that VAMP-seq is applicable to other genes, highlighting its generalizability.

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

“…However, if some of the BRCA1 and HRAS mutations acted via some mechanism other than by perturbing their interactions with specific binding partners (BARD1 or RasGAP, respectively), as is almost certainly the case, this can explain the relative underperformance of the DMS data: it simply is not suited to identify some pathogenic mutations. The phenotypic screen for PTEN measures protein abundance in the cell by fluorescence of EGFP bound to the protein (17). This technique called VAMP-seq identifies thermodynamically unstable variants; however, this may fail to capture disease mechanisms acting through interaction disruption and loss or gain of function unrelated to destabilisation.…”

“…DMS experiments allow direct identification of damaging human variants in a high-throughput manner (16,17). It is likely that the effects on function derived from DMS experiments will be better indicators of the clinical effect of mutations than any computational predictors.…”

Toward mechanistic models for genotype–phenotype correlations in phenylketonuria using protein stability calculations

“…(B) The functional PAH tetramer and a monomer illustrating the distribution of our selected variants across the structure (PDB: 5DEN). (C) Biophysical calculations with FoldX estimate the effect of each possible single amino acid change on protein stability (ΔΔG), with the results for residues 30-70 shown here, and those for all 420 structural-resolved residues available in the Supporting Information protein function, for example, by changes in the active or cofactorbinding sites, it has been shown that, in general, most missense variant proteins are less structurally stable than the corresponding wild-type protein (Matreyek et al, 2018;Tokuriki, Stricher, Schymkowitz, Serrano, & Tawfik, 2007). Thus, a likely mechanism for the loss of function of many missense variants is loss of stability (Casadio, Vassura, Tiwari, Fariselli, & Luigi, 2011;Yue, Li, & Moult, 2005), leading to increased proteasomal degradation and thus insufficient amounts of PAH protein.…”

Recommendations for application of the functional evidence PS3/BS3 criterion using the ACMG/AMP sequence variant interpretation framework

“…While typical functional assays cited as evidence in variant curations analyze relatively few variants [Kanavy, et al, submitted], new multiplexed assays can analyze thousands of variants in a single experiment [26][27][28]. This kind of increased throughput facilitates the reproducibility, replication, and assay calibration using many definitive pathogenic and benign variant controls.…”