Machine learning classifier for identification of damaging missense mutations exclusive to human mitochondrial DNA-encoded polypeptides

BMC Bioinformatics. 2017 Mar 7;18(1):158. doi: 10.1186/s12859-017-1562-7.


Background: Several methods have been developed to predict the pathogenicity of missense mutations but none has been specifically designed for classification of variants in mtDNA-encoded polypeptides. Moreover, there is not available curated dataset of neutral and damaging mtDNA missense variants to test the accuracy of predictors. Because mtDNA sequencing of patients suffering mitochondrial diseases is revealing many missense mutations, it is needed to prioritize candidate substitutions for further confirmation. Predictors can be useful as screening tools but their performance must be improved.

Results: We have developed a SVM classifier (Mitoclass.1) specific for mtDNA missense variants. Training and validation of the model was executed with 2,835 mtDNA damaging and neutral amino acid substitutions, previously curated by a set of rigorous pathogenicity criteria with high specificity. Each instance is described by a set of three attributes based on evolutionary conservation in Eukaryota of wildtype and mutant amino acids as well as coevolution and a novel evolutionary analysis of specific substitutions belonging to the same domain of mitochondrial polypeptides. Our classifier has performed better than other web-available tested predictors. We checked performance of three broadly used predictors with the total mutations of our curated dataset. PolyPhen-2 showed the best results for a screening proposal with a good sensitivity. Nevertheless, the number of false positive predictions was too high. Our method has an improved sensitivity and better specificity in relation to PolyPhen-2. We also publish predictions for the complete set of 24,201 possible missense variants in the 13 human mtDNA-encoded polypeptides.

Conclusions: Mitoclass.1 allows a better selection of candidate damaging missense variants from mtDNA. A careful search of discriminatory attributes and a training step based on a curated dataset of amino acid substitutions belonging exclusively to human mtDNA genes allows an improved performance. Mitoclass.1 accuracy could be improved in the future when more mtDNA missense substitutions will be available for updating the attributes and retraining the model.

Keywords: Classifier; Missense mutation; Mitochondrial DNA; Pathogenicity; Protein multiple sequence alignment; SVM.

MeSH terms

  • Computational Biology / methods
  • DNA Mutational Analysis / methods*
  • DNA, Mitochondrial*
  • Humans
  • Machine Learning*
  • Mitochondria / genetics
  • Mitochondria / metabolism*
  • Mutation, Missense*
  • Peptides / genetics*
  • Sensitivity and Specificity


  • DNA, Mitochondrial
  • Peptides