Computational protein design has a wide variety of applications. Despite its remarkable success, designing a protein for a given structure and function is still a challenging task. On the other hand, the number of solved protein structures is rapidly increasing while the number of unique protein folds has reached a steady number, suggesting more structural information is being accumulated on each fold. Deep learning neural network is a powerful method to learn such big data set and has shown superior performance in many machine learning fields. In this study, we applied the deep learning neural network approach to computational protein design for predicting the probability of 20 natural amino acids on each residue in a protein. A large set of protein structures was collected and a multi-layer neural network was constructed. A number of structural properties were extracted as input features and the best network achieved an accuracy of 38.3%. Using the network output as residue type restraints improves the average sequence identity in designing three natural proteins using Rosetta. Moreover, the predictions from our network show ~3% higher sequence identity than a previous method. Results from this study may benefit further development of computational protein design methods.

中文翻译：

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深度学习神经网络的计算蛋白质设计。

计算蛋白设计具有广泛的应用。尽管取得了巨大的成功，但为给定的结构和功能设计蛋白质仍然是一项艰巨的任务。另一方面，已解决的蛋白质结构数量迅速增加，而独特的蛋白质折叠数已达到稳定数，这表明每个折叠上都积累了更多的结构信息。深度学习神经网络是学习这样的大数据集的有力方法，并且在许多机器学习领域中都表现出卓越的性能。在这项研究中，我们将深度学习神经网络方法应用于蛋白质的计算设计，以预测蛋白质中每个残基上20个天然氨基酸的可能性。收集了大量的蛋白质结构，并构建了多层神经网络。提取了许多结构特性作为输入特征，最佳网络达到了38.3％的精度。在使用Rosetta设计三种天然蛋白质时，将网络输出用作残基类型限制条件可改善平均序列同一性。而且，根据我们的网络进行的预测显示，序列同一性比以前的方法高约3％。这项研究的结果可能有益于计算蛋白设计方法的进一步发展。