论文标题
使用深神经网络液化的蛋白质振动，分类和跨范式从头产生
Liquified protein vibrations, classification and cross-paradigm de novo image generation using deep neural networks
论文作者
论文摘要
在最近的工作中，我们报道了超过100,000种已知蛋白质结构的振动光谱，以及一种自洽的超声型方法，可在可听见的频率范围内呈现频谱（Extreme Mechanics Letters，2019）。在这里，我们提出了一种将这些分子振动转化为使用声学器的薄膜的物质振动的方法，从而导致表面波的复杂模式，并使用所得的宏观图像在使用深卷积神经网络进行进一步处理中。具体而言，每种蛋白质结构的水表面波的模式用于为神经网络建立训练集，旨在对模式进行分类和处理。一旦受过训练，神经网络模型就可以通过分析水膜中的宏观表面波模式来辨别不同的蛋白质。该方法不仅可以区分不同类型的蛋白质（例如α-螺旋与α-螺旋和β-片的杂种），而且还能够确定相同蛋白质的不同折叠状态，或蛋白质与配体的蛋白质的结合事件。使用Deepdream算法，可以在一系列图像中看到深神经网络的关键特征实例，从而使我们能够探索蛋白质表面波浪模式神经网络的内部工作，以及通过查找和突出显示蛋白质分子光谱的特征在照片输入的范围内找到蛋白质分子光谱的特征。以水为中心的cymatics的实现，结合了神经网络，尤其是生成方法，为实现材料综合的“实体主义”作为纳米启发的艺术的可能方向提供了一个新的方向。该方法可以应用于检测不同蛋白质结构，突变的作用或在医学成像和诊断中的用途，并在纳米到麦克罗过渡中产生广泛影响。
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 (Extreme Mechanics Letters, 2019). 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 alpha-helices and beta-sheets), but it is also capable of determining different folding states of the same protein, or the binding events of proteins to ligands. Using the DeepDream algorithm, instances of key features of the deep neural network can be made visible in a range of images, allowing us to explore the inner workings of protein surface wave patter neural networks, as well as the creation of new images by finding and highlighting features of protein molecular spectra in a range of photographic input. The integration of the water-focused realization of cymatics, combined with neural networks and especially generative methods, offer a new direction to realize materiomusical "Inceptionism" as a possible direction in nano-inspired art. The method could have applications for detecting different protein structures, the effect of mutations, or uses in medical imaging and diagnostics, with broad impact in nano-to-macro transitions.
