Towards autonomous analysis of chemical exchange saturation transfer experiments using deep neural networks.
Towards autonomous analysis of chemical exchange saturation transfer experiments using deep neural networks.
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DOI：
10.1007/s10858-022-00395-z
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发表时间：
2022-06
影响因子：
2.7
通讯作者：
Hansen, D. Flemming
中科院分区：
文献类型：
--
作者：
Karunanithy, Gogulan;Yuwen, Tairan;Kay, Lewis E.;Hansen, D. Flemming
大分子经常在时间尺度上的功能状态之间进行交换，可以通过核磁共振光谱进行访问，并且已经开发了许多核磁共振工具来表征交换过程的动力学和热力学，以及所涉及的构象的结构。然而，对报告交换大分子的核磁共振数据的分析往往取决于复杂的最小二乘拟合程序以及人类的经验和直觉，这在某些情况下限制了该方法的广泛使用。深度神经网络（dnn）和人工智能在科学领域的应用已经显著增加，最近，特别是在生物分子核磁共振领域，其中dnn现在可用于诸如稀疏采样光谱重建，峰值拾取和虚拟解耦等任务。在这里，我们提出了一个深度神经网络，用于分析化学交换饱和转移（CEST）数据，这些数据报告了两个或三个位点的化学交换，涉及大约3-60毫秒之间的稀疏状态寿命，这是通过实验最常观察到的范围。这里提出的工作集中在1H CEST类的方法，这是进一步复杂的，相对于应用到其他核，通过反相特征。开发的dnn直接从反相1HN CEST谱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。DNN的性能通过合成和实验反相CEST谱进行定量评估。评估表明DNN准确地确定了化学变化及其相关的不确定性。这里开发的深度神经网络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核磁共振数据进行自主分析。在线版本包含补充材料，可在10.1007/s10858-022-00395-z获得。
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 1HN 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. The online version contains supplementary material available at 10.1007/s10858-022-00395-z.
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影响因子：
64.8
作者：
Bouvignies, Guillaume;Vallurupalli, Pramodh;Hansen, D. Flemming;Correia, Bruno E.;Lange, Oliver;Bah, Alaji;Vernon, Robert M.;Dahlquist, Frederick W.;Baker, David;Kay, Lewis E.
通讯作者：
Kay, Lewis E.
影响因子：
64.8
作者：
Faust, Ofrah;Abayev-Avraham, Meital;Rosenzweig, Rina
通讯作者：
Rosenzweig, Rina
影响因子：
2.7
作者：
Karunanithy G;Hansen DF
通讯作者：
Hansen DF
DOI：
10.1073/pnas.2016917118
发表时间：
2021-02-02
影响因子：
11.1
作者：
Amey JL;Keeley J;Choudhury T;Kuprov I
通讯作者：
Kuprov I
通过深度神经网络快速重建非均匀采样多维核磁共振波谱
影响因子：
2.2
作者：
Luo, Jie;Zeng, Qing;Lin, Yanqin
通讯作者：
Lin, Yanqin