MutaCYP: Classification of Missense Mutations in Human Cytochromes P450

Overview

Journal BMC Med Genomics

Publisher Biomed Central

Specialty Genetics

Date 2014 Jul 31

PMID 25073475

Citations 2

Authors

Kenneth Fechter

Aleksey Porollo

Affiliations

Soon will be listed here.

Abstract

Background: Cytochrome 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.

Methods: A 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.

Results: MutaCYP 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.

Conclusions: A 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/.

Citing Articles

IDRMutPred: predicting disease-associated germline nonsynonymous single nucleotide variants (nsSNVs) in intrinsically disordered regions.

Zhou J, Xiong Y, An K, Ye Z, Wu Y Bioinformatics. 2020; 36(20):4977-4983.

PMID: 32756939 PMC: 7755418. DOI: 10.1093/bioinformatics/btaa618.

Variation benchmark datasets: update, criteria, quality and applications.

Sarkar A, Yang Y, Vihinen M Database (Oxford). 2020; 2020.

PMID: 32016318 PMC: 6997940. DOI: 10.1093/database/baz117.

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