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.

叶绿体是绿色植物和藻类中的一种亚细胞器。它是进行光合作用的主要亚细胞器。定位于叶绿体中的蛋白质负责分子水平的光合作用过程。叶绿体可以进一步分为几个部分。不同区室中的蛋白质与光合作用过程中的不同步骤有关。由于蛋白质的分子功能与确切的细胞定位高度相关，因此准确了解叶绿体蛋白质的亚叶绿体位置是了解其在光合作用过程中的作用的重要步骤。确定蛋白质亚叶绿体位置的实验过程始终是昂贵且费时的。因此，开发了计算方法以从一级序列预测蛋白质亚叶绿体的位置。在过去的几十年中，超过十项研究尝试使用机器学习方法预测蛋白质亚叶绿体的位置。本研究主题介绍了各种序列特征和各种机器学习算法。在这篇综述中，我们收集了有关蛋白质叶绿体位置预测的所有现有研究的综合信息。我们在基准数据集，序列特征，机器学习算法，预测性能和实现可用性方面比较这些研究。我们总结了该专题研究的进展和现状。我们还尝试找出预测蛋白质亚叶绿体位置的最可能的未来工作。我们希望这篇综述不仅列出所有现有作品，而且还可以为读者提供有用的资源，以帮助他们快速掌握该研究主题的概况。我们还希望这项审查工作可以成为有关蛋白质叶绿体位置预测的未来方法学研究的起点。