Predicting Protein Submitochondrial Locations Using a K-Nearest Neighbors Method Based on the Bit-Score Weighted Euclidean Distance

摘要

Mitochondria are essential subcellular organelles found in eukaryotic cells. Knowing information on a protein's subcellular or sub-subcellular location provides in-depth insights about the microenvironment where it interacts with other molecules and is crucial for inferring the protein's function. Therefore, it is important to predict the submitochondrial localization of mitochondrial proteins. In this study, we introduced MitoBSKnn, a K-nearest neighbor method based on a bit-score weighted Euclidean distance, which is calculated from an extended version of pseudo-amino acid composition. We then improved the method by applying a heuristic feature selection process. Using the selected features, the final method achieved a 93% overall accuracy on the benchmarking dataset, which is higher than or comparable to other state-of-art methods. On a larger recently curated dataset, the method also achieved a consistent performance of 90% overall accuracy. MitoBSKnn is available at http://edisk.fandm.edu/jing.hu/mitobsknn/mitobsknn.html.