In recent work we reported the vibrational spectrum of more than 100 000 known protein structures, and a self-consistent sonification method to render the spectrum in the audible range of frequencies (Qin and Buehler 2019 Extreme Mech. Lett . 100460). Here we present a method to transform these molecular vibrations into materialized vibrations of thin water films using acoustic actuators, leading to complex patterns of surface waves, and using the resulting macroscopic images in further processing using deep convolutional neural networks. Specifically, the patterns of water surface waves for each protein structure is used to build training sets for neural networks, aimed to classify and further process the patterns. Once trained, the neural network model is capable of discerning different proteins solely by analyzing the macroscopic surface wave patterns in the water film. Not only can the method distinguish different types of proteins (e.g. alpha-helix vs. hybrids of alph...

中文翻译：

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使用深度神经网络的液化蛋白质振动，分类和交叉范式从头生成图像

在最近的工作中，我们报告了超过10万个已知蛋白质结构的振动光谱，以及一种自洽的超声处理方法，以使该光谱处于可听见的频率范围内（Qin and Buehler 2019 Extreme Mech.Lett.100460）。在这里，我们提出了一种使用声学致动器将这些分子振动转换为水薄膜的物化振动的方法，从而导致表面波的复杂模式，并在深度卷积神经网络的进一步处理中使用所得的宏观图像。具体来说，每种蛋白质结构的水表面波模式用于构建神经网络的训练集，旨在对模式进行分类和进一步处理。一旦训练，神经网络模型能够仅通过分析水膜中的宏观表面波型来识别不同的蛋白质。该方法不仅可以区分不同类型的蛋白质（例如，α-螺旋与α-杂种的杂种...