Multiplex Assessment of Protein Variant Abundance by Massively Parallel Sequencing

By Kenneth A. Matreyek, Lea M. Starita, Jason J. Stephany, Beth K. Martin, Melissa A. Chiasson, Vanessa E. Gray, Martin Kircher, Arineh Khechaduri, J. N. Dines, Ronald J. Hause and 6 more. Nature Genetics, 2018

402 citations recorded by the source corpus.

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 show 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. VAMP-seq is a scalable assay that measures the effects of missense variants on intracellular protein abundance. Applying VAMP-seq to thousands of PTEN and TPMT variants helps to classify them as pathogenic or benign.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/191ac90de9f354fc2af0b427d7f5af41e1fc67b3

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Citations in the corpus

• Accurate proteome-wide missense variant effect prediction with AlphaMissense (2023, background): “Variants located in structured regions, which may alter protein stability ( 35 , 36 ), are associated with higher pathogenicity scores than variants located in disordered regions (fig.”
• Large-scale clinical interpretation of genetic variants using evolutionary data and deep learning (2020, neutral): “We compared the performance of EVE to a number high-throughput experiments [7, 28, 29, 30, 31, 32, 33, 34, 35, 36], which”
• Using deep mutational scanning to benchmark variant effect predictors and identify disease mutations (2020, neutral): “In contrast, the phenotypic screen for PTEN(a) measures protein abundance in the cell by fluorescence of EGFP bound to the protein (Matreyek et al , 2018).”
• Deep mutational scanning of SARS-CoV-2 receptor binding domain reveals constraints on folding and ACE2 binding (2020, neutral): “, 2010), bottlenecked each library to ~100,000 barcoded variants, and linked each RBD variant to its barcode via long-read PacBio SMRT sequencing (Matreyek et al., 2018) (Figure S1A).”
• A saturation mutagenesis approach to understanding PTEN lipid phosphatase activity and genotype-phenotypes relationships (2018, background): “A concurrent functional screen assaying protein stability found 1/4 of mutations alter steady-state stability.(59) Six mutations that destabilized PTEN in breast cancer cell lines also decreased steady-state abundance in this yeast model,(31) suggesting that mutations affecting thermostability will ”