ArticleBioinformatics (Oxford, England)2012
Performance of computational tools in evaluating the functional impact of laboratory-induced amino acid mutations.
Article in Bioinformatics (Oxford, England), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
30 citing papers in PubMed, 57 citations in OpenAlex.
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- Rheostats, toggles, and neutrals, Oh my! A new framework for understanding how amino acid changes modulate protein function.The Journal of biological chemistry · 2024Review
- Rheostat functional outcomes occur when substitutions are introduced at nonconserved positions that diverge with speciation.Protein science : a publication of the Protein Society · 2021Article
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- Rheostat positions: A new classification of protein positions relevant to pharmacogenomics.Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agents · 2020Article
- A Novel Missense Mutation in Human Receptor Roundabout-1Journal of clinical research in pediatric endocrinology · 2020Article
- Functional tunability from a distance: Rheostat positions influence allosteric coupling between two distant binding sites.Scientific reports · 2019Article
- What went wrong with variant effect predictor performance for the PCM1 challenge.Human mutation · 2019Article
- Clinical application of next-generation sequencing to the practice of neurology.The Lancet. Neurology · 2019Review
- RheoScale: A tool to aggregate and quantify experimentally determined substitution outcomes for multiple variants at individual protein positions.Human mutation · 2018Article
- Coevolving residues inform protein dynamics profiles and disease susceptibility of nSNVs.PLoS computational biology · 2018Article
- Genome-wide sequencing technologies: A primer for paediatricians.Paediatrics & child health · 2018Article
- Article
- Computational predictors fail to identify amino acid substitution effects at rheostat positions.Scientific reports · 2017Article
- The Loss and Gain of Functional Amino Acid Residues Is a Common Mechanism Causing Human Inherited Disease.PLoS computational biology · 2016Article
- Using Evolution to Guide Protein Engineering: The Devil IS in the Details.Biophysical journal · 2016Review
- Clinical exome sequencing in neurologic disease.Neurology. Clinical practice · 2016Review
- Clinical exome sequencing in neurogenetic and neuropsychiatric disorders.Annals of the New York Academy of Sciences · 2016Review
- Single-Nucleotide Polymorphisms on the RYD5 Gene in Nasal Polyposis.DNA and cell biology · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Site-directed mutagenesis is frequently used by scientists to investigate the functional impact of amino acid mutations in the laboratory. Over 10,000 such laboratory-induced mutations have been reported in the UniProt database along with the outcomes of functional assays. Here, we explore the performance of state-of-the-art computational tools (Condel, PolyPhen-2 and SIFT) in correctly annotating the function-altering potential of 10,913 laboratory-induced mutations from 2372 proteins. We find that computational tools are very successful in diagnosing laboratory-induced mutations that elicit significant functional change in the laboratory (up to 92% accuracy). But, these tools consistently fail in correctly annotating laboratory-induced mutations that show no functional impact in the laboratory assays. Therefore, the overall accuracy of computational tools for laboratory-induced mutations is much lower than that observed for the naturally occurring human variants. We tested and rejected the possibilities that the preponderance of changes to alanine and the presence of multiple base-pair mutations in the laboratory were the reasons for the observed discordance between the performance of computational tools for natural and laboratory mutations. Instead, we discover that the laboratory-induced mutations occur predominately at the highly conserved positions in proteins, where the computational tools have the lowest accuracy of correct prediction for variants that do not impact function (neutral). Therefore, the comparisons of experimental-profiling results with those from computational predictions need to be sensitive to the evolutionary conservation of the positions harboring the amino acid change.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.