This work introduces LEGOLAS, a fully open source TorchANI-based neural network model designed to predict NMR chemical shifts for protein backbone atoms. LEGOLAS has been designed to be fast, and without loss of accuracy, as our model is able to predict backbone chemical shifts with root-mean-square errors of 2.69 ppm for N, 0.95 ppm for Ca, 1.40 ppm for Cb, 1.06 ppm for C’, 0.52 ppm for amide protons, and 0.29 ppm for H. The program predicts chemical shifts at least one order of magnitude faster than the widely utilized SHIFTX2 model. This breakthrough allows us to predict NMR chemical shifts for a very large number of input structures, such as frames from a molecular dynamics trajectory. In our simulation of the protein BBL from E. coli, we observe that averaging the chemical shift predictions for a set of frames of an MD trajectory substantially improves the agreement with experiment with respect of using a single frame of the dynamics. We also show that LEGOLAS can be successfully applied to the problem of recognizing the native states of a protein among a set of decoys.

中文翻译：

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乐高积木：一种用于快速准确预测蛋白质 NMR 化学变化的机器学习方法。


这项工作介绍了 LEGOLAS，这是一个完全开源的基于 TorchANI 的神经网络模型，旨在预测蛋白质骨架原子的 NMR 化学位移。LEGOLAS 的设计速度快，而且不会损失准确性，因为我们的模型能够预测主链化学位移，N 的均方根误差为 2.69 ppm，Ca 为 0.95 ppm，Cb 为 1.40 ppm，C' 为 1.06 ppm，酰胺质子为 0.52 ppm，H 为 0.29 ppm。该程序预测化学位移的速度比广泛使用的 SHIFTX2 模型至少快一个数量级。这一突破使我们能够预测大量输入结构的 NMR 化学位移，例如分子动力学轨迹的框架。在我们对来自大肠杆菌的蛋白质 BBL 的模拟中，我们观察到，对 MD 轨迹的一组帧的化学位移预测求平均值大大提高了与使用动力学的单个帧的实验的一致性。我们还表明，LEGOLAS 可以成功地应用于在一组诱饵中识别蛋白质天然状态的问题。