MaveRegistry: a collaboration platform for multiplexed assays of variant effect

Kuang, Da; Kishore, Nishka; Rubin, Alan F.; Fields, Stanley; Fowler, Douglas M.; Roth, Frederick P.

doi:10.1101/2020.10.14.339499

Cited by 5 publications

(4 citation statements)

References 17 publications

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“…The MAVE community is making progress in developing assays with high dynamic range and high predictive value. [37][38][39][40] However, addressing the lack of benign and pathogenic control variants for validation is more challenging. A perfect assay must have at least 19 control benign and pathogenic variants for validation to achieve strong benign and pathogenic functional evidence, whereas at least 11 benign and pathogenic variants are required for an assay to achieve moderate evidence.…”

Section: Discussionmentioning

confidence: 99%

Closing the gap: Systematic integration of multiplexed functional data resolves variants of uncertain significance in BRCA1, TP53, and PTEN

Fayer

Horton

Dines

et al. 2021

The American Journal of Human Genetics

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Section: Discussionmentioning

confidence: 99%

Closing the gap: Systematic integration of multiplexed functional data resolves variants of uncertain significance in BRCA1, TP53, and PTEN

Fayer

Horton

Dines

et al. 2021

The American Journal of Human Genetics

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“…DMS has been widely adopted to explore effects of amino acid variation using a variety of different experimental phenotypes, such as protein abundance, activity or general cellular fitness 5 . However, while the number of proteins that have been characterized through DMS grows constantly 7,8 , and use of these methodologies and coordination between groups is increasing through the Atlas of Variant Effects Alliance 9…”

Section: Introductionmentioning

confidence: 99%

Correspondence between functional scores from deep mutational scans and predicted effects on protein stability

Gerasimavicius

Livesey

Marsh

2023

Preprint

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Many methodologically diverse computational methods have been applied to the growing challenge of predicting and interpreting the effects of protein variants. As many pathogenic mutations have a perturbing effect on protein stability or intermolecular interactions, one highly interpretable approach is to use protein structural information to model the physical impacts of variants and predict their likely effects on protein stability and interactions. Previous efforts have assessed the accuracy of stability predictors in reproducing thermodynamically accurate values and evaluated their ability to distinguish between known pathogenic and benign mutations. Here, we take an alternate approach, and explore how well stability predictor scores correlate with functional impacts derived from deep mutational scanning (DMS) experiments. In this work, we compare the predictions of 9 protein stability-based tools against mutant protein fitness values from 45 independent DMS datasets, covering 161,441 unique single amino acid variants. We find that FoldX and Rosetta show the strongest correlations with DMS-based functional scores, similar to their previous top performance in distinguishing between pathogenic and benign variants. For both methods, performance is considerably improved when considering intermolecular interactions from protein complex structures, when available. Finally, we also highlight that predicted stability effects show consistently higher correlations with certain DMS experimental phenotypes, particularly those based upon protein abundance, and, in certain cases, can be competitive with other sequence-based variant effect prediction methodologies for predicting functional scores from DMS experiments.

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“…Well-established functional assays can provide strong evidence for classification of a variant as either pathogenic or benign 3,8 , but such results are typically unavailable for rare missense variants. Although "variant effect mapping" technologies have been established to proactively determine the functional effects of many variants in parallel [9][10][11] , a variant effect map is available for only ~1% of human disease-associated proteins 12 .…”

Section: Introductionmentioning

confidence: 99%

“…They also had limited ability to carry out unbiased evaluation for predictors (e.g., DeepSequence) that had used variant effect map data in model training. Although Livesey and Marsh found that variant effect maps were typically more accurate than predictors of pathogenicity, a variant effect map is currently available for only ~1% of human disease-associated proteins [26].…”

Section: Introductionmentioning

confidence: 99%

Empowering rare variant burden-based gene-trait association studies via optimized computational predictor choice

Kuang

et al. 2021

Preprint

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Computational predictors can help interpret pathogenicity of human genetic variants, especially for the majority of variants where no experimental data are available. However, because we lack a high-quality unbiased test set, identifying the best-performing predictors remains a challenge. To address this issue, we evaluated missense variant effect predictors using genotypes and traits from a prospective cohort. We considered 139 gene-trait combinations with rare-variant burden association based on at least one of four systematic studies using phenotypes and whole-exome sequences from ~200K UK Biobank participants. Using an evaluation set of 35,525 rare missense variants and the relevant associated traits, we assessed the correlation of participants' traits with scores derived from 20 computational variant effect predictors. We found that two predictors—VARITY and REVEL—outperformed all others according to multiple performance measures. We expect that this study will help in selecting variant effect predictors, for both research and clinical purposes, while providing an unbiased benchmarking strategy that can be applied to additional cohorts and predictors.

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MaveRegistry: a collaboration platform for multiplexed assays of variant effect

Cited by 5 publications

References 17 publications

Closing the gap: Systematic integration of multiplexed functional data resolves variants of uncertain significance in BRCA1, TP53, and PTEN

Closing the gap: Systematic integration of multiplexed functional data resolves variants of uncertain significance in BRCA1, TP53, and PTEN

Correspondence between functional scores from deep mutational scans and predicted effects on protein stability

Empowering rare variant burden-based gene-trait association studies via optimized computational predictor choice

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