Identification of Protein Subcellular Localization With Network and Functional Embeddings

The functions of proteins are mainly determined by their subcellular localizations in cells. Currently, many computational methods for predicting the subcellular localization of proteins have been proposed. However, these methods require further improvement, especially when used in protein represent...

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Published inFrontiers in genetics Vol. 11; p. 626500
Main Authors Pan, Xiaoyong, Li, Hao, Zeng, Tao, Li, Zhandong, Chen, Lei, Huang, Tao, Cai, Yu-Dong
Format Journal Article
LanguageEnglish
Published Switzerland Frontiers Media S.A 20.01.2021
Subjects
functional embedding
gene ontology
Genetics
KEGG pathway
network embedding
protein subcellular localization
protein subcellular localization
gene ontology
KEGG pathway
network embedding
functional embedding
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Summary:The functions of proteins are mainly determined by their subcellular localizations in cells. Currently, many computational methods for predicting the subcellular localization of proteins have been proposed. However, these methods require further improvement, especially when used in protein representations. In this study, we present an embedding-based method for predicting the subcellular localization of proteins. We first learn the functional embeddings of KEGG/GO terms, which are further used in representing proteins. Then, we characterize the network embeddings of proteins on a protein-protein network. The functional and network embeddings are combined as novel representations of protein locations for the construction of the final classification model. In our collected benchmark dataset with 4,861 proteins from 16 locations, the best model shows a Matthews correlation coefficient of 0.872 and is thus superior to multiple conventional methods.
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Edited by: Wei Lan, Guangxi University, China
This article was submitted to Computational Genomics, a section of the journal Frontiers in Genetics
These authors have contributed equally to this work
Reviewed by: Peng Zhang, Shanghai University of Medicine and Health Sciences, China; Yun Li, University of Pennsylvania, United States
ISSN:1664-8021
1664-8021
DOI:10.3389/fgene.2020.626500