ArticleBMC medical genomics2014
MutaCYP: Classification of missense mutations in human cytochromes P450.
Article in BMC medical genomics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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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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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.
- The Clinical Pharmacogenetics Implementation Consortium's consensus-based framework for assigning allele function.American journal of human genetics · 2025Guideline
- IDRMutPred: predicting disease-associated germline nonsynonymous single nucleotide variants (nsSNVs) in intrinsically disordered regions.Bioinformatics (Oxford, England) · 2020Article
- Variation benchmark datasets: update, criteria, quality and applications.Database : the journal of biological databases and curation · 2020Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
backgroundCytochrome P450 monooxygenases (CYPs) represent a large and diverse family of enzymes involved in various biological processes in humans. Individual genome sequencing has revealed multiple mutations in human CYPs, and many missense mutations have been associated with variety of diseases. Since 3D structures are not resolved for most human CYPs, there is a need for a reliable sequence-based prediction that discriminates benign and disease causing mutations.
methodsA new prediction method (MutaCYP) has been developed for scoring de novo missense mutations to have a deleterious effect. The method utilizes only five features, all of which are sequence-based: predicted relative solvent accessibility (RSA), variance of predicted RSA among the residues in close sequence proximity, Z-score of Shannon entropy for a given position, difference in similarity scores and weighted difference in size between wild type and new amino acids. The method is based on a single neural network.
resultsMutaCYP achieves MCC = 0.70, Q2 = 88.52%, Recall = 93.40% with Precision = 91.09%, and AUC = 0.909. Comparative evaluation with other existing methods indicates that MutaCYP outperforms SIFT and PolyPhen-2. Predictions by MutaCYP appear to be orthogonal to predictions by the evaluated methods. Potential issues on reliability of annotations of mutations in the existing databases are discussed.
conclusionsA new accurate method, MutaCYP, for classification of missense mutations in human CYPs is presented. The prediction model consists of only five sequence-based features, including a real-valued predicted relative solvent accessibility. The method is publicly available at http://research.cchmc.org/MutaSense/.
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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.