MutaCYP: Classification of missense mutations in human cytochromes P450

BMC Medical Genomics

Aleksey Porollo²  Kenneth Fechter¹ 

[1] Department of Biomedical Engineering, University of Cincinnati, Cincinnati, OH, USA;Center for Autoimmune Genomics and Etiology and Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH 45229, USA

关键词: Evolutionary information;    Relative solvent accessibility;    Classification of missense mutations;    Machine learning based prediction;    Human CYP polymorphism;    Human CYP variants;   

Others  :  1090707
DOI  :  10.1186/1755-8794-7-47

 received in 2014-03-04, accepted in 2014-07-24,  发布年份 2014

【 摘 要 】

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/ webcite.

【 授权许可】

   
2014 Fechter and Porollo; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

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