Jiang Yuexu, Wang Duolin, Wang Weiwei, Xu Dong
Department of Electrical Engineering and Computer Science, Bond Life Sciences Center, University of Missouri, Columbia, MO, USA.
Comput Struct Biotechnol J. 2021 Oct 19;19:5834-5844. doi: 10.1016/j.csbj.2021.10.023. eCollection 2021.
The accurate annotation of protein localization is crucial in understanding protein function in tandem with a broad range of applications such as pathological analysis and drug design. Since most proteins do not have experimentally-determined localization information, the computational prediction of protein localization has been an active research area for more than two decades. In particular, recent machine-learning advancements have fueled the development of new methods in protein localization prediction. In this review paper, we first categorize the main features and algorithms used for protein localization prediction. Then, we summarize a list of protein localization prediction tools in terms of their coverage, characteristics, and accessibility to help users find suitable tools based on their needs. Next, we evaluate some of these tools on a benchmark dataset. Finally, we provide an outlook on the future exploration of protein localization methods.
准确注释蛋白质定位对于结合广泛应用（如病理分析和药物设计）来理解蛋白质功能至关重要。由于大多数蛋白质没有通过实验确定的定位信息，蛋白质定位的计算预测在二十多年来一直是一个活跃的研究领域。特别是，最近机器学习的进展推动了蛋白质定位预测新方法的发展。在这篇综述论文中，我们首先对用于蛋白质定位预测的主要特征和算法进行分类。然后，我们根据蛋白质定位预测工具的覆盖范围、特点和可获取性总结了一份列表，以帮助用户根据自身需求找到合适的工具。接下来，我们在一个基准数据集上对其中一些工具进行评估。最后，我们对蛋白质定位方法的未来探索进行展望。
