Institute for NanoBioTechnology, Department of Materials Science and Engineering, and Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD 21218, United States of America.
Division of Cardiology, Department of Medicine, Duke University School of Medicine, Durham, NC 27710, United States of America.
Biochim Biophys Acta Gen Subj. 2023 Oct;1867(10):130428. doi: 10.1016/j.bbagen.2023.130428. Epub 2023 Jul 23.
The current methods for quantifying ligand bias involve the construction of bias plots and the calculations of bias coefficients that can be compared using statistical methods. However, widely used bias coefficients can diverge in their abilities to identify ligand bias and can give false positives. As the empirical bias plots are considered the most reliable tools in bias identification, here we develop an analytical description of bias plot trajectories and introduce a bias coefficient, kappa, which is calculated from these trajectories. The new bias coefficient complements the tool-set in ligand bias identification in cell signaling research.
目前,量化配体偏向的方法涉及构建偏向图和计算可以使用统计方法进行比较的偏向系数。然而,广泛使用的偏向系数在识别配体偏向的能力上可能存在差异,并可能产生假阳性。由于经验偏向图被认为是识别偏向的最可靠工具,因此我们在这里对偏向图轨迹进行了分析描述,并引入了一个新的偏向系数 kappa,该系数是从这些轨迹中计算得出的。该新的偏向系数补充了细胞信号研究中配体偏向识别的工具集。