Biophysics Graduate Program, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States.
Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States.
J Phys Chem B. 2024 Oct 24;128(42):10397-10407. doi: 10.1021/acs.jpcb.4c05109. Epub 2024 Oct 12.
Solid-state NMR spectroscopy (SSNMR) is a powerful technique to probe structural and dynamic properties of biomolecules at an atomic level. Modern SSNMR methods employ multidimensional pulse sequences requiring data collection over a period of days to weeks. Variations in signal intensity or frequency due to environmental fluctuation introduce artifacts into the spectra. Therefore, it is critical to actively monitor instrumentation subject to fluctuations. Here, we demonstrate a method rooted in the unsupervised machine learning algorithm principal component analysis (PCA) to evaluate the impact of environmental parameters that affect sensitivity, resolution and peak positions (chemical shifts) in multidimensional SSNMR protein spectra. PCA loading spectra illustrate the unique features associated with each drifting parameter, while the PCA scores quantify the magnitude of parameter drift. This is demonstrated both for double (HC) and triple resonance (HCN) experiments. Furthermore, we apply this methodology to identify magnetic field drift, and leverage PCA to "denoise" multidimensional SSNMR spectra of the membrane protein, EmrE, using several spectra collected over several days. Finally, we utilize PCA to identify changes in (CP and decoupling) and fields in a manner that we envision could be automated in the future. Overall, these approaches enable improved objectivity in monitoring NMR spectrometers, and are also applicable to other forms of spectroscopy.
固态核磁共振波谱学（SSNMR）是一种强大的技术，可以在原子水平上探测生物分子的结构和动态特性。现代 SSNMR 方法采用多维脉冲序列，需要数天到数周的时间来收集数据。由于环境波动导致的信号强度或频率变化会在光谱中引入伪影。因此，积极监测易受波动影响的仪器设备至关重要。在这里，我们展示了一种基于无监督机器学习算法主成分分析（PCA）的方法，用于评估影响多维 SSNMR 蛋白质光谱灵敏度、分辨率和峰位置（化学位移）的环境参数的影响。PCA 加载光谱说明了与每个漂移参数相关的独特特征，而 PCA 得分则量化了参数漂移的幅度。这在双（HC）和三共振（HCN）实验中都得到了证明。此外，我们应用这种方法来识别磁场漂移，并利用 PCA 对跨几天收集的多个光谱进行“降噪”，以处理膜蛋白 EmrE 的多维 SSNMR 光谱。最后，我们利用 PCA 以一种我们设想在未来可以实现自动化的方式来识别 CP 和去耦场以及 场的变化。总的来说，这些方法可以提高监测 NMR 光谱仪的客观性，并且也适用于其他形式的光谱学。
