Physics Department, University of Michigan, 930 North University Avenue, Ann Arbor, Michigan 48109, United States.
Department of Chemistry, University of Michigan, 930 North University Avenue, Ann Arbor, Michigan 48109, United States.
J Chem Inf Model. 2020 Mar 23;60(3):1073-1078. doi: 10.1021/acs.jcim.9b01039. Epub 2020 Feb 14.
Here, we present PyShifts-a PyMOL plugin for chemical shift-based analysis of biomolecular ensembles. With PyShifts, users can compare and visualize differences between experimentally measured and computationally predicted chemical shifts. When analyzing multiple conformations of a biomolecule with PyShifts, users can also sort a set of conformations based on chemical shift differences and identify the conformers that exhibit the best agreement between measured and predicted chemical shifts. Although we have integrated PyShifts with the chemical shift predictors LARMOR and LARMOR, PyShifts can read in chemical shifts from any source, and so, users can employ PyShifts to analyze biomolecular structures using chemical shifts computed by any chemical shift predictor. We envision, therefore, that PyShifts (https://github.com/atfrank/PyShifts) will find utility as a general-purpose tool for exploring chemical shift-structure relationships in biomolecular ensembles.
在这里，我们介绍了 PyShifts，这是一个用于基于化学位移分析生物分子集合的 PyMOL 插件。使用 PyShifts，用户可以比较和可视化实验测量和计算预测的化学位移之间的差异。当使用 PyShifts 分析生物分子的多个构象时，用户还可以根据化学位移差异对构象进行排序，并确定在实验测量和预测化学位移之间具有最佳一致性的构象。尽管我们已经将 PyShifts 与化学位移预测器 LARMOR 和 LARMOR 集成在一起，但 PyShifts 可以读取任何来源的化学位移，因此，用户可以使用 PyShifts 来分析使用任何化学位移预测器计算的化学位移的生物分子结构。因此，我们设想 PyShifts（https://github.com/atfrank/PyShifts）将作为探索生物分子集合中化学位移-结构关系的通用工具找到实用价值。
