ArticleGigaScience2022
Integrating deep mutational scanning and low-throughput mutagenesis data to predict the impact of amino acid variants.
Article in GigaScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Machine learning framework for cost effective deep mutational scanning through targeted substitution profiling.BMC bioinformatics · 2026Article
- Supervised learning of protein variant effects across large-scale mutagenesis datasets.Protein science : a publication of the Protein Society · 2026Article
- Predicting interacting hotspots for nanobodies' binding using triplets of residues.Protein science : a publication of the Protein Society · 2025Article
- QAFI: a novel method for quantitative estimation of missense variant impact using protein-specific predictors and ensemble learning.Human genetics · 2025Article
- Integrating deep mutational scanning and low-throughput mutagenesis data to predict the impact of amino acid variants.GigaScience · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundEvaluating the impact of amino acid variants has been a critical challenge for studying protein function and interpreting genomic data. High-throughput experimental methods like deep mutational scanning (DMS) can measure the effect of large numbers of variants in a target protein, but because DMS studies have not been performed on all proteins, researchers also model DMS data computationally to estimate variant impacts by predictors.
resultsIn this study, we extended a linear regression-based predictor to explore whether incorporating data from alanine scanning (AS), a widely used low-throughput mutagenesis method, would improve prediction results. To evaluate our model, we collected 146 AS datasets, mapping to 54 DMS datasets across 22 distinct proteins.
conclusionsWe show that improved model performance depends on the compatibility of the DMS and AS assays, and the scale of improvement is closely related to the correlation between DMS and AS results.
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