In tandem mass spectrometry (MS/MS)-based proteomics, search engines rely on comparison between an experimental MS/MS spectrum and the theoretical spectra of the candidate peptides. Hence, accurate prediction of the theoretical spectra of peptides appears to be particularly important. Here, we present pDeep, a deep neural network-based model for the spectrum prediction of peptides. Using the bidirectional long short-term memory (BiLSTM), pDeep can predict higher-energy collisional dissociation, electron-transfer dissociation, and electron-transfer and higher-energy collision dissociation MS/MS spectra of peptides with >0.9 median Pearson correlation coefficients. Further, we showed that intermediate layer of the neural network could reveal physicochemical properties of amino acids, for example the similarities of fragmentation behaviors between amino acids. We also showed the potential of pDeep to distinguish extremely similar peptides (peptides that contain isobaric amino acids, for example, GG = N, AG = Q, or even I = L), which were very difficult to distinguish using traditional search engines.

中文翻译：

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pDeep：通过深度学习预测肽的MS / MS光谱

在基于串联质谱（MS / MS）的蛋白质组学中，搜索引擎依靠实验性MS / MS光谱与候选肽的理论光谱之间的比较。因此，准确预测肽的理论光谱显得尤为重要。在这里，我们介绍了pDeep，这是一种基于深度神经网络的肽光谱预测模型。使用双向长短期记忆（BiLSTM），pDeep可以预测中值Pearson相关系数> 0.9的肽的高能碰撞解离，电子转移解离以及电子转移和高能碰撞解离MS / MS光谱。此外，我们发现神经网络的中间层可以揭示氨基酸的理化特性，例如氨基酸之间断裂行为的相似性。我们还显示了pDeep可以区分极其相似的肽（包含等压氨基酸的肽，例如GG = N，AG = Q或什至I = L）的潜力，而使用传统搜索引擎很难区分这些肽。