Many proteins act on DNA for a wide range of processes, including DNA replication, DNA repair, and transcription. Their time spent on DNA can provide insight into these processes and their stability within complexes to which they may belong. 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 with tracking molecules for extended durations in cases of processes with slow kinetics. We developed a machine learning approach, 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.

中文翻译：

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Bound2Learn：一种通过单分子跟踪实验对DNA结合蛋白进行分类的机器学习方法

许多蛋白质可在多种过程中作用于DNA，包括DNA复制，DNA修复和转录。他们花费在DNA上的时间可以洞悉这些过程及其在它们可能属于的复合物中的稳定性。单粒子跟踪可以直接可视化蛋白质-DNA动力学，但是，鉴定一个分子是否与DNA结合可能并非易事。在动力学缓慢的过程中，随着追踪分子的延长持续时间，还会带来更多的复杂性。我们开发了一种机器学习方法，利用来自广泛使用的跟踪软件的输出来对轨道进行可靠的分类，以准确估算停留时间。我们在计算机上以及大肠杆菌和酿酒酵母的活细胞数据中验证了我们的方法。