Abstract
BackgroundSubcellular localization of a new protein sequence is very important and fruitful for understanding its function. As the number of new genomes has dramatically increased over recent years, a reliable and efficient system to predict protein subcellular location is urgently needed.ResultsEsub8 was developed to predict protein subcellular localizations for eukaryotic proteins based on amino acid composition. In this research, the proteins are classified into the following eight groups: chloroplast, cytoplasm, extracellular, Golgi apparatus, lysosome, mitochondria, nucleus and peroxisome. We know subcellular localization is a typical classification problem; consequently, a one-against-one (1-v-1) multi-class support vector machine was introduced to construct the classifier. Unlike previous methods, ours considers the order information of protein sequences by a different method. Our method is tested in three subcellular localization predictions for prokaryotic proteins and four subcellular localization predictions for eukaryotic proteins on Reinhardt's dataset. The results are then compared to several other methods. The total prediction accuracies of two tests are both 100% by a self-consistency test, and are 92.9% and 84.14% by the jackknife test, respectively. Esub8 also provides excellent results: the total prediction accuracies are 100% by a self-consistency test and 87% by the jackknife test.ConclusionsOur method represents a different approach for predicting protein subcellular localization and achieved a satisfactory result; furthermore, we believe Esub8 will be a useful tool for predicting protein subcellular localizations in eukaryotic organisms.
Highlights
Subcellular localization of a new protein sequence is very important and fruitful for understanding its function
Our method represents a different approach for predicting protein subcellular localization and achieved a satisfactory result
In this paper we developed a tool, Esub8, to predict eight subcellular localizations for eukaryotic proteins: the total accuracies are 100% by the self-consistency test and 87% by the jackknife test
Summary
Subcellular localization of a new protein sequence is very important and fruitful for understanding its function. As the number of new genomes has dramatically increased over recent years, a reliable and efficient system to predict protein subcellular location is urgently needed. Over recent years the number of new genomes and protein sequences has increased dramatically. Reliable and efficient sequence analysis tools are urgently needed. The native subcellular localization of a protein is important for understanding gene/protein function. Knowing the protein's localization will be one important step identifying its function. Even if we already know a protein's function, information about protein localization may provide us insights into the specific enzyme pathway [2,3,4,5]. Predicting subcellular localization has become one of the central problems in bioinformatics
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