STUDY OF PROTEIN SUBCELLULAR LOCALIZATION PREDICTION: A REVIEW

Life Science - Bioinformatics

Abstract

Protein subcellular localization, an important study on cytobiology, proteomics and drug design, directly relates to the functions of proteins at their prescribed cellular positions. Prediction of the subcellular localizations based on the machine learning has shown a great interest. This article focuses on the current research on extraction of protein sequence, machine learning algorithms and methods based on sequence and annotation. It was observed that features such as gene ontology, functional domains could improve the accuracy of prediction. Study of cells proteins, proteomics provides the annotations between the interaction groups and their associated functions. Knowing the localization of individual protein is very vital. Transport across the eukaryotic cells, comprising of subcellular compartments, organelles is very highly regulated and complex. In-silico subcellular localization has been an area of active research for years. The openly available methods that are of importance diverge in four aspects the underlying biological motivation, the computational method used, localization coverage, and reliability. This review has a study on the main events in the protein sorting process and widely used methods.