PDF(Pubmed)
Abstract：
Methyl-TROSY nuclear magnetic resonance (NMR) spectroscopy is a powerful technique for characterising large biomolecules in solution. However, preparing samples for these experiments is demanding and entails deuteration, limiting its use. Here we demonstrate that NMR spectra recorded on protonated, uniformly 13C labelled samples can be processed using deep neural networks to yield spectra that are of similar quality to typical deuterated methyl-TROSY spectra, potentially providing information for proteins that cannot be produced in bacterial systems. We validate the methodology experimentally on three proteins with molecular weights in the range 42-360 kDa. We further demonstrate the applicability of our methodology to 3D NOESY spectra of Escherichia coli Malate Synthase G (81 kDa), where observed NOE cross-peaks are in good agreement with the available structure. The method represents an advance in the field of using deep learning to analyse complex magnetic resonance data and could have an impact on the study of large biomolecules in years to come.
摘要：
甲基-TrosY核磁共振（NMR）光谱是表征溶液中大型生物分子的强大技术。然而,为这些实验准备样品要求很高，需要氘代，限制其使用。在这里，我们证明了核磁共振波谱记录在质子化，可以使用深度神经网络处理均匀13C标记的样品，以产生与典型的氘代甲基-TROSY光谱质量相似的光谱，可能为细菌系统中无法产生的蛋白质提供信息。我们在分子量在42-360kDa范围内的三种蛋白质上实验验证了该方法。我们进一步证明了我们的方法对大肠杆菌苹果酸合成酶G（81kDa）的3DNOESY光谱的适用性，其中观察到的NOE交叉峰与可用结构非常吻合。该方法代表了使用深度学习分析复杂磁共振数据领域的进步，并可能在未来几年对大型生物分子的研究产生影响。
