Predicting protein subchloroplast locations: the 10th anniversary

doi:10.1007/s11704-020-9507-0

Front. Comput. Sci.

2021, Vol. 15

Issue (2) : 152901 https://doi.org/10.1007/s11704-020-9507-0

REVIEW ARTICLE

Predicting protein subchloroplast locations: the 10th anniversary

Jian SUN, Pu-Feng DU(

)

College of Intelligence and Computing, Tianjin University, Tianjin 300350, China

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Abstract

Chloroplast is a type of subcellular organelle in green plants and algae. It is the main subcellular organelle for conducting photosynthetic process. The proteins, which localize within the chloroplast, are responsible for the photosynthetic process at molecular level. The chloroplast can be further divided into several compartments. Proteins in different compartments are related to different steps in the photosynthetic process. Since the molecular function of a protein is highly correlated to the exact cellular localization, pinpointing the subchloroplast location of a chloroplast protein is an important step towards the understanding of its role in the photosynthetic process. Experimental process for determining protein subchloroplast location is always costly and time consuming. Therefore, computational approaches were developed to predict the protein subchloroplast locations from the primary sequences. Over the last decades, more than a dozen studies have tried to predict protein subchloroplast locations with machine learning methods. Various sequence features and various machine learning algorithms have been introduced in this research topic. In this review, we collected the comprehensive information of all existing studies regarding the prediction of protein subchloroplast locations. We compare these studies in the aspects of benchmarking datasets, sequence features, machine learning algorithms, predictive performances, and the implementation availability. We summarized the progress and current status in this special research topic. We also try to figure out the most possible future works in predicting protein subchloroplast locations. We hope this review not only list all existing works, but also serve the readers as a useful resource for quickly grasping the big picture of this research topic.We also hope this review work can be a starting point of future methodology studies regarding the prediction of protein subchloroplast locations.

Keywords subchloroplast locations sequence features performance measures online services machine learning

Corresponding Author(s): Pu-Feng DU

Just Accepted Date: 25 August 2020 Issue Date: 19 October 2020

Cite this article:

Jian SUN,Pu-Feng DU. Predicting protein subchloroplast locations: the 10th anniversary[J]. Front. Comput. Sci., 2021, 15(2): 152901.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-020-9507-0
https://academic.hep.com.cn/fcs/EN/Y2021/V15/I2/152901

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