Eukaryotic cells consist of numerous membrane-bound organelles,which compartmentalize cellular materials to fulfil a variety of vital functions.In the post-genomic era,it is widely recognized that identification of th...Eukaryotic cells consist of numerous membrane-bound organelles,which compartmentalize cellular materials to fulfil a variety of vital functions.In the post-genomic era,it is widely recognized that identification of the subcellular organelle localization and transport mechanisms of the encoded proteins are necessary for a fundamental understanding of their biological functions and theorganization of cellular activity.Multiple experimental approaches are now available to determine the subcellular localizations and dynamics of proteins.In this review,we provide an overview of the current methods and organelle markers for protein subcellular localization and trafficking studies in plants,with a focus on the organelles of the endomembrane system.We also discuss the limitations of each method in terms of protein colocalization studies.展开更多
As one of the essential topics in proteomics and molecular biology, protein subcellular localization has been extensively studied in previous decades. However, most of the methods are limited to the prediction of sing...As one of the essential topics in proteomics and molecular biology, protein subcellular localization has been extensively studied in previous decades. However, most of the methods are limited to the prediction of single-location proteins. In many studies, multi-location proteins are either not considered or assumed not existing. This paper proposes a novel multi-label subcellular-localization predictor based on the semantic similarity between Gene Ontology (GO) terms. Given a protein, the accession numbers of its homologs are obtained via BLAST search. Then, the homologous accession numbers of the protein are used as keys to search against the gene ontology annotation database to obtain a set of GO terms. The semantic similarity between GO terms is used to formulate semantic similarity vectors for classification. A support vector machine (SVM) classifier with a new decision scheme is proposed to classify the multi-label GO semantic similarity vectors. Experimental results show that the proposed multi-label predictor significantly outperforms the state-of-the-art predictors such as iLoc-Plant and Plant-mPLoc.展开更多
基金This work was supported by the National Natural Science Foundation of China(31970181)the Zhejiang Provincial Natural Science Foundation of China(R20C020001)+1 种基金the National Key Research and Development Program of China(2018YFD1000604)the Zhejiang Agricultural and Forestry University Starting Funding(2018FR029).
文摘Eukaryotic cells consist of numerous membrane-bound organelles,which compartmentalize cellular materials to fulfil a variety of vital functions.In the post-genomic era,it is widely recognized that identification of the subcellular organelle localization and transport mechanisms of the encoded proteins are necessary for a fundamental understanding of their biological functions and theorganization of cellular activity.Multiple experimental approaches are now available to determine the subcellular localizations and dynamics of proteins.In this review,we provide an overview of the current methods and organelle markers for protein subcellular localization and trafficking studies in plants,with a focus on the organelles of the endomembrane system.We also discuss the limitations of each method in terms of protein colocalization studies.
文摘As one of the essential topics in proteomics and molecular biology, protein subcellular localization has been extensively studied in previous decades. However, most of the methods are limited to the prediction of single-location proteins. In many studies, multi-location proteins are either not considered or assumed not existing. This paper proposes a novel multi-label subcellular-localization predictor based on the semantic similarity between Gene Ontology (GO) terms. Given a protein, the accession numbers of its homologs are obtained via BLAST search. Then, the homologous accession numbers of the protein are used as keys to search against the gene ontology annotation database to obtain a set of GO terms. The semantic similarity between GO terms is used to formulate semantic similarity vectors for classification. A support vector machine (SVM) classifier with a new decision scheme is proposed to classify the multi-label GO semantic similarity vectors. Experimental results show that the proposed multi-label predictor significantly outperforms the state-of-the-art predictors such as iLoc-Plant and Plant-mPLoc.
