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Protein subcellular localization prediction (or just protein localization prediction) involves the computational prediction of where a protein resides in a cell. In general, it is a computational method to take as input information about a protein, such as a protein sequence, and produce a prediction of a subcellular localization as output. Prediction of protein subcellular localization is an important component of bioinformatics based prediction of protein function and genome annotation, and it can aid the identification of drug targets. == Background == Most eukaryotic proteins are encoded in the nuclear genome and synthesized in the cytosol, but many need to be further sorted before they reach their final destination. For prokaryotes, proteins are synthesized in the cytoplasm and some must be targeted to other locations such as to a cell membrane or the extracellular environment. Proteins must be localized at their appropriate subcellular compartment to perform their desired function. Experimentally determining the subcellular localization of a protein is a laborious and time consuming task. Through the development of new approaches in computer science, coupled with an increased dataset of proteins of known localization, computational tools can now provide fast and accurate localization predictions for many organisms. This has resulted in subcellular localization prediction becoming one of the challenges being successfully aided by bioinformatics. Many protein subcellular localization prediction methods now exceed the accuracy of some high-throughput laboratory methods for the identification of protein subcellular localization. Particularly, some predictors have been developed that can be used to deal with proteins that may simultaneously exist, or move between, two or more different subcellular locations. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Protein subcellular localization prediction」の詳細全文を読む スポンサード リンク
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