Multiplex assessment of protein variant abundance by massively parallel sequencing

Matreyek, Kenneth A.; Starita, Lea M.; Stephany, Jason J.; Martin, Beth; Chiasson, Melissa A; Gray, Vanessa E.; Kircher, Martin; Khechaduri, Arineh; Dines, Jennifer N.; Hause, Ronald J.; Bhatia, Smita; Evans, William E.; Relling, Mary V.; Yang, Wenjian; Shendure, Jay; Fowler, Douglas M.

doi:10.1038/s41588-018-0122-z

Cited by 387 publications

(521 citation statements)

References 61 publications

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“…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.…”

Section: Identification Of Pathogenic Human Mutations Using Dms Datamentioning

confidence: 99%

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

Livesey

Marsh

2019

Preprint

106

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In order to deal with the huge number of novel protein-coding variants being identified by genome and exome sequencing studies, many computational phenotype predictors have been developed. Unfortunately, such predictors are often trained and evaluated on different protein variant datasets, making a direct comparison between predictors very difficult. Moreover, training and testing datasets may also overlap, introducing training bias. In this study, we use 29 previously published deep mutational scanning (DMS) experiments, which provide quantitative, unbiased phenotypic measurements for large numbers of single amino acid substitutions, in order to benchmark and compare 31 different computational phenotype predictors. We also evaluate the ability of DMS measurements and computational phenotype predictors to discriminate between pathogenic and benign missense variants. We find that DMS experiments based upon competitive growth assays tend to be superior to the top-ranking computational predictors, demonstrating the tremendous potential of DMS for identifying novel human disease mutations. Among the computational phenotype predictors, DeepSequence clearly stood out, showing both the strongest correlations with DMS data and having the best ability to predict pathogenic mutations, which is especially remarkable given that it has not been trained against human mutations. Other predictors we recommend that showed good results when tested against DMS data and human mutations include SNAP2, SNPs&GO, DEOGEN2, VEST4 and REVEL; they also benefit from being much easier for end users than DeepSequence.

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Section: Identification Of Pathogenic Human Mutations Using Dms Datamentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Livesey

Marsh

2019

Preprint

106

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“…(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.…”

Section: Computational Saturation Mutagenesis and Thermodynamic Stabimentioning

confidence: 99%

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

et al. 2019

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Phenylketonuria (PKU) is a genetic disorder caused by variants in the gene encoding phenylalanine hydroxylase (PAH), resulting in accumulation of phenylalanine to neurotoxic levels. Here, we analyzed the cellular stability, localization, and interaction with wild‐type PAH of 20 selected PKU‐linked PAH protein missense variants. Several were present at reduced levels in human cells, and the levels increased in the presence of a proteasome inhibitor, indicating that proteins are proteasome targets. We found that all the tested PAH variants retained their ability to associate with wild‐type PAH, and none formed aggregates, suggesting that they are only mildly destabilized in structure. In all cases, PAH variants were stabilized by the cofactor tetrahydrobiopterin (BH4), a molecule known to alleviate symptoms in certain PKU patients. Biophysical calculations on all possible single‐site missense variants using the full‐length structure of PAH revealed a strong correlation between the predicted protein stability and the observed stability in cells. This observation rationalizes previously observed correlations between predicted loss of protein destabilization and disease severity, a correlation that we also observed using new calculations. We thus propose that many disease‐linked PAH variants are structurally destabilized, which in turn leads to proteasomal degradation and insufficient amounts of cellular PAH protein.

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“…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.…”

Section: Multiplexed Functional Assaysmentioning

confidence: 99%

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

Brnich

Tayoun

Couch³

et al. 2019

Preprint

Self Cite

193

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Background:The American College of Medical Genetics and Genomics (ACMG)/Association for Molecular Pathology (AMP) clinical variant interpretation guidelines established criteria (PS3/BS3) for functional assays that specified a "strong" level of evidence. However, they did not provide detailed guidance on how functional evidence should be evaluated, and differences in the application of the PS3/BS3 codes is a contributor to variant interpretation discordance between laboratories. This recommendation seeks to provide a more structured approach to the assessment of functional assays for variant interpretation and guidance on the use of various levels of strength based on assay validation. Methods: The Clinical Genome Resource (ClinGen) Sequence Variant Interpretation (SVI)Working Group used curated functional evidence from ClinGen Variant Curation Expert Paneldeveloped rule specifications and expert opinions to refine the PS3/BS3 criteria over multiple inperson and virtual meetings. We estimated odds of pathogenicity for assays using various numbers of variant controls to determine the minimum controls required to reach moderate level evidence. Feedback from the ClinGen Steering Committee and outside experts were incorporated into the recommendations at multiple stages of development. Results:The SVI Working Group developed recommendations for evaluators regarding the assessment of the clinical validity of functional data and a four-step provisional framework to determine the appropriate strength of evidence that can be applied in clinical variant interpretation. These steps are: 1. Define the disease mechanism; 2. Evaluate applicability of general classes of assays used in the field; 3. Evaluate validity of specific instances of assays; 4.Apply evidence to individual variant interpretation. We found that a minimum of eleven total 4 pathogenic and benign variant controls are required to reach moderate-level evidence in the absence of rigorous statistical analysis. Conclusions:The recommendations and approach to functional evidence evaluation described here should help clarify the clinical variant interpretation process for functional assays. Further, we hope that these recommendations will help develop productive partnerships with basic scientists who have developed functional assays that are useful for interrogating the function of a variety of genes.

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Multiplex assessment of protein variant abundance by massively parallel sequencing

Cited by 387 publications

References 61 publications

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

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

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

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

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