Department of Biology, McGill University, 3649 Sir William Osler, Montreal, QC H3G 0B1 Canada.
Nucleic Acids Res. 2021 Aug 20;49(14):e79. doi: 10.1093/nar/gkab186.
DNA-bound proteins are essential elements for the maintenance, regulation, and use of the genome. The time they spend bound to DNA provides useful information on their stability within protein complexes and insight into the understanding of biological processes. Single-particle tracking allows for direct visualization of protein-DNA kinetics, however, identifying whether a molecule is bound to DNA can be non-trivial. Further complications arise when tracking molecules for extended durations in processes with slow kinetics. We developed a machine learning approach, termed Bound2Learn, using output from a widely used tracking software, to robustly classify tracks in order to accurately estimate residence times. We validated our approach in silico, and in live-cell data from Escherichia coli and Saccharomyces cerevisiae. Our method has the potential for broad utility and is applicable to other organisms.
与 DNA 结合的蛋白质是维持、调节和利用基因组所必需的元素。它们与 DNA 结合的时间为了解其在蛋白质复合物中的稳定性以及深入理解生物过程提供了有用的信息。单颗粒跟踪可直接可视化蛋白质-DNA 动力学，但确定分子是否与 DNA 结合并不简单。当在动力学较慢的过程中长时间跟踪分子时，会出现更多的复杂情况。我们开发了一种机器学习方法，称为 Bound2Learn，该方法使用广泛使用的跟踪软件的输出，对轨道进行稳健分类，以准确估计停留时间。我们在计算机模拟和来自大肠杆菌和酿酒酵母的活细胞数据中验证了我们的方法。我们的方法具有广泛的适用性，可适用于其他生物体。
