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. 2019 Dec 31;15(12):863-868.
doi: 10.6026/97320630015863. eCollection 2019.

SubmitoLoc: Identification of Mitochondrial Sub Cellular Locations of Proteins Using Support Vector Machine

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Free PMC article

SubmitoLoc: Identification of Mitochondrial Sub Cellular Locations of Proteins Using Support Vector Machine

Varadharaju Nithya. Bioinformation. .
Free PMC article

Abstract

Mitochondria are important sub-cellular organelles in eukaryotes. Defects in mitochondrial system lead to a variety of disease. Therefore, detailed knowledge of mitochondrial proteome is vital to understand mitochondrial system and their function. Sequence databases contain large number of mitochondrial proteins but they are mostly not annotated. In this study, we developed a support vector machine approach, SubmitoLoc, to predict mitochondrial sub cellular locations of proteins based on various sequence derived properties. We evaluated the predictor using 10-fold cross validation. Our method achieved 88.56 % accuracy using all features. Average sensitivity and specificity for four-subclass prediction is 85.37% and 87.25% respectively. High prediction accuracy suggests that SubmitoLoc will be useful for researchers studying mitochondrial biology and drug discovery.

Keywords: SVM; protein prediction; sub mitochondrial.

Figures

Figure 1
Figure 1
Flowchart representing various steps involved in SubmitoLoc method; Inner- inner membrane, inter-inter membrane space, outer - outer membrane space, matrix- mitochondrial matrix
Figure 2
Figure 2
ROC curves of multi-class SVM classification (4 subclasses) on test dataset

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