ArticleBMC bioinformatics2020
Variant effect predictions capture some aspects of deep mutational scanning experiments.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Engineering selective amyloid precursor protein inhibitors by machine learning and deep mutational scanning.Protein science : a publication of the Protein Society · 2026Article
- Strain-specific epistasis shapes fitness landscapes of APOBEC3G antagonism by HIV-1 Vif proteins.Science advances · 2026Article
- Fitness Landscapes of APOBEC3G Antagonism by HIV-1 Vif proteins.bioRxiv : the preprint server for biology · 2025Article
- Variant effect predictor correlation with functional assays is reflective of clinical classification performance.Genome biology · 2025Article
- QAFI: a novel method for quantitative estimation of missense variant impact using protein-specific predictors and ensemble learning.Human genetics · 2025Article
- Language Modelling Techniques for Analysing the Impact of Human Genetic Variation.Bioinformatics and biology insights · 2025Review
- Expert-guided protein language models enable accurate and blazingly fast fitness prediction.Bioinformatics (Oxford, England) · 2024Article
- Machine Learning-Guided Protein Engineering.ACS catalysis · 2023Review
- DeMAG predicts the effects of variants in clinically actionable genes by integrating structural and evolutionary epistatic features.Nature communications · 2023Article
- VariBench, new variation benchmark categories and data sets.Frontiers in bioinformatics · 2023Article
- High-throughput approaches to functional characterization of genetic variation in yeast.Current opinion in genetics & development · 2022Review
- Embeddings from protein language models predict conservation and variant effects.Human genetics · 2022Article
- Interpreting protein variant effects with computational predictors and deep mutational scanning.Disease models & mechanisms · 2022Review
- Clinical validation of genomic functional screen data: Analysis of observedHGG advances · 2022Article
- Computational and experimental methods for classifying variants of unknown clinical significance.Cold Spring Harbor molecular case studies · 2022Article
- The properties of human disease mutations at protein interfaces.PLoS computational biology · 2022Article
- Intelligent host engineering for metabolic flux optimisation in biotechnology.The Biochemical journal · 2021Review
- Understanding the Origins of Loss of Protein Function by Analyzing the Effects of Thousands of Variants on Activity and Abundance.Molecular biology and evolution · 2021Article
- Article
- Protein-protein and protein-nucleic acid binding residues important for common and rare sequence variants in human.BMC bioinformatics · 2020Article
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Authors and funding
3 authors.
Funding
Abstract
backgroundDeep mutational scanning (DMS) studies exploit the mutational landscape of sequence variation by systematically and comprehensively assaying the effect of single amino acid variants (SAVs; also referred to as missense mutations, or non-synonymous Single Nucleotide Variants - missense SNVs or nsSNVs) for particular proteins. We assembled SAV annotations from 22 different DMS experiments and normalized the effect scores to evaluate variant effect prediction methods. Three trained on traditional variant effect data (PolyPhen-2, SIFT, SNAP2), a regression method optimized on DMS data (Envision), and a naïve prediction using conservation information from homologs.
resultsOn a set of 32,981 SAVs, all methods captured some aspects of the experimental effect scores, albeit not the same. Traditional methods such as SNAP2 correlated slightly more with measurements and better classified binary states (effect or neutral). Envision appeared to better estimate the precise degree of effect. Most surprising was that the simple naïve conservation approach using PSI-BLAST in many cases outperformed other methods. All methods captured beneficial effects (gain-of-function) significantly worse than deleterious (loss-of-function). For the few proteins with multiple independent experimental measurements, experiments differed substantially, but agreed more with each other than with predictions.
conclusionsDMS provides a new powerful experimental means of understanding the dynamics of the protein sequence space. As always, promising new beginnings have to overcome challenges. While our results demonstrated that DMS will be crucial to improve variant effect prediction methods, data diversity hindered simplification and generalization.
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