Macromolecules often exchange between functional states on timescales that can be accessed with NMR spectroscopy and many NMR tools have been developed to characterise the kinetics and thermodynamics of the exchange processes, as well as the structure of the conformers that are involved. However, analysis of the NMR data that report on exchanging macromolecules often hinges on complex least-squares fitting procedures as well as human experience and intuition, which, in some cases, limits the widespread use of the methods. The applications of deep neural networks (DNNs) and artificial intelligence have increased significantly in the sciences, and recently, specifically, within the field of biomolecular NMR, where DNNs are now available for tasks such as the reconstruction of sparsely sampled spectra, peak picking, and virtual decoupling. Here we present a DNN for the analysis of chemical exchange saturation transfer (CEST) data reporting on two- or three-site chemical exchange involving sparse state lifetimes of between approximately 3–60 ms, the range most frequently observed via experiment. The work presented here focuses on the ¹H CEST class of methods that are further complicated, in relation to applications to other nuclei, by anti-phase features. The developed DNNs accurately predict the chemical shifts of nuclei in the exchanging species directly from anti-phase ¹H^N CEST profiles, along with an uncertainty associated with the predictions. The performance of the DNN was quantitatively assessed using both synthetic and experimental anti-phase CEST profiles. The assessments show that the DNN accurately determines chemical shifts and their associated uncertainties. The DNNs developed here do not contain any parameters for the end-user to adjust and the method therefore allows for autonomous analysis of complex NMR data that report on conformational exchange.

大分子经常在可以通过 NMR 光谱访问的时间尺度上在功能状态之间交换，并且已经开发了许多 NMR 工具来表征交换过程的动力学和热力学，以及所涉及的构象异构体的结构。然而，对报告交换大分子的 NMR 数据的分析通常取决于复杂的最小二乘拟合程序以及人类经验和直觉，在某些情况下，这限制了这些方法的广泛使用。深度神经网络 (DNN) 和人工智能在科学领域的应用显着增加，最近特别是在生物分子 NMR 领域，DNN 现在可用于重建稀疏采样光谱、峰值拾取​​、和虚拟解耦。在这里，我们提出了一个用于分析化学交换饱和转移 (CEST) 数据的 DNN，该数据报告涉及大约 3-60 ms 的稀疏状态寿命，这是通过实验最常观察到的范围。这里介绍的工作重点是¹ H CEST 类方法，相对于其他原子核的应用，通过反相特征更加复杂。开发的 DNN 直接从反相¹ H ^N CEST 剖面准确预测交换物种中原子核的化学位移，以及与预测相关的不确定性。使用合成和实验反相 CEST 配置文件对 DNN 的性能进行了定量评估。评估表明，DNN 准确地确定了化学位移及其相关的不确定性。这里开发的 DNN 不包含任何供最终用户调整的参数，因此该方法允许对报告构象交换的复杂 NMR 数据进行自主分析。