ArticleGenome biology and evolution2022
Fitness Effects of Mutations: An Assessment of PROVEAN Predictions Using Mutation Accumulation Data.
Article in Genome biology and evolution, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- In silico prioritisation of EDN1 missense variants identifies mature endothelin-1 substitutions with predicted receptor-binding effects.BMC genomics · 2026Article
- Long runs of homozygosity are reliable genomic markers of inbreeding depression.Trends in ecology & evolution · 2025Review
- Analyzing 6211 unique variants in the upgraded interactive FVIII web database reveals novel insights into hemophilia A.Blood vessels, thrombosis & hemostasis · 2025Article
- Guidelines for releasing a variant effect predictor.Genome biology · 2025Article
- Mutations in yeast are deleterious on average regardless of the degree of adaptation to the testing environment.Proceedings. Biological sciences · 2024Article
- Using Computational Simulations to Model Deleterious Variation and Genetic Load in Natural Populations.The American naturalist · 2023Review
- PSnpBind-ML: predicting the effect of binding site mutations on protein-ligand binding affinity.Journal of cheminformatics · 2023Article
- Deleterious Variation in Natural Populations and Implications for Conservation Genetics.Annual review of animal biosciences · 2023Review
- Genomic Underpinnings of Population Persistence in Isle Royale Moose.Molecular biology and evolution · 2023Article
- Are mutations usually deleterious? A perspective on the fitness effects of mutation accumulation.Evolutionary ecology · 2022Article
- Recent insights into the evolution of mutation rates in yeast.Current opinion in genetics & development · 2022Review
- Article
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2 authors.
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Abstract
Predicting fitness in natural populations is a major challenge in biology. It may be possible to leverage fast-accumulating genomic data sets to infer the fitness effects of mutant alleles, allowing evolutionary questions to be addressed in any organism. In this paper, we investigate the utility of one such tool, called PROVEAN. This program compares a query sequence with existing data to provide an alignment-based score for any protein variant, with scores categorized as neutral or deleterious based on a pre-set threshold. PROVEAN has been used widely in evolutionary studies, for example, to estimate mutation load in natural populations, but has not been formally tested as a predictor of aggregate mutational effects on fitness. Using three large published data sets on the genome sequences of laboratory mutation accumulation lines, we assessed how well PROVEAN predicted the actual fitness patterns observed, relative to other metrics. In most cases, we find that a simple count of the total number of mutant proteins is a better predictor of fitness than the number of proteins with variants scored as deleterious by PROVEAN. We also find that the sum of all mutant protein scores explains variation in fitness better than the number of mutant proteins in one of the data sets. We discuss the implications of these results for studies of populations in the wild.
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