| Literature DB >> 31957607 |
Meng-Lu Liu1, Wei Su1, Zheng-Xing Guan1, Dan Zhang1, Wei Chen1, Li Liu2, Hui Ding1.
Abstract
The chloroplast is a type of subcellular organelle of green plants and eukaryotic algae, which plays an important role in the photosynthesis process. Since the function of a protein correlates with its location, knowing its subchloroplast localization is helpful for elucidating its functions. However, due to a large number of chloroplast proteins, it is costly and time-consuming to design biological experiments to recognize subchloroplast localizations of these proteins. To address this problem, during the past ten years, twelve computational prediction methods have been developed to predict protein subchloroplast localization. This review summarizes the research progress in this area. We hope the review could provide important guide for further computational study on protein subchloroplast localization. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.Entities:
Keywords: Protein; dataset; feature selection; machine learning method; protein sequence properties; subchloroplast localization
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Year: 2020 PMID: 31957607 DOI: 10.2174/1389203721666200117153412
Source DB: PubMed Journal: Curr Protein Pept Sci ISSN: 1389-2037 Impact factor: 3.272