Performance of different approaches for predicting the subcellular locations of proteins: A review

Subcellular location of a protein is closely related to its function. Knowing the subcellular localization of proteins is important in molecular cell biology, proteomics, and system biology and drug discovery. Different predictors have been developed that predict presence, location and interaction of molecules using various computational techniques including probabilistic models of Machine Learning and artificial intelligence algorithms. These predictors partially cover the different aspects of exploration of subcellular locations. Some of them are equally well applicable to many types of organisms (human, yeast, mouse, bacteria) while some are specific and focus on better performance in accuracy of the predicted results. Similarly some of the techniques cover “few” number of proteins but more accurately and on the other side some algorithms predict sub cellular locations of “many” proteins at the expense of prediction accuracy. This research is a review of most common and efficient techniques grouped in four in total, which are 1-amino acid composition and order-based predictors 2-sorting signal predictors 3- homology-based predictors and 4-hybrid methods that use several sources of information to predict localization. The work Elucidate the performance and coverage comparisons among the subcellular locations predictors.