David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, N2L 3G1, Canada.
Bioinformatics Solutions Inc., Waterloo, ON, N2L 6J2, Canada.
Sci Rep. 2019 Nov 20;9(1):17168. doi: 10.1038/s41598-019-52954-4.
Liquid chromatography with tandem mass spectrometry (LC-MS/MS) based quantitative proteomics provides the relative different protein abundance in healthy and disease-afflicted patients, which offers the information for molecular interactions, signaling pathways, and biomarker identification to serve the drug discovery and clinical research. Typical analysis workflow begins with the peptide feature detection and intensity calculation from LC-MS map. We are the first to propose a deep learning based model, DeepIso, that combines recent advances in Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) to detect peptide features of different charge states, as well as, estimate their intensity. Existing tools are designed with limited engineered features and domain-specific parameters, which are hardly updated despite a huge amount of new coming proteomic data. On the other hand, DeepIso consisting of two separate deep learning based modules, learns multiple levels of representation of high dimensional data itself through many layers of neurons, and adaptable to newly acquired data. The peptide feature list reported by our model matches with 97.43% of high quality MS/MS identifications in a benchmark dataset, which is higher than the matching produced by several widely used tools. Our results demonstrate that novel deep learning tools are desirable to advance the state-of-the-art in protein identification and quantification.
基于液相色谱-串联质谱（LC-MS/MS）的定量蛋白质组学提供了健康和患病患者之间相对不同的蛋白质丰度信息，为分子相互作用、信号通路和生物标志物鉴定提供了信息，以服务于药物发现和临床研究。典型的分析工作流程始于从 LC-MS 图谱中检测肽特征和计算强度。我们是第一个提出基于深度学习的模型 DeepIso 的，该模型结合了卷积神经网络（CNN）和递归神经网络（RNN）的最新进展，用于检测不同电荷状态的肽特征，并估计其强度。现有的工具设计具有有限的工程特征和特定于领域的参数，尽管有大量新的蛋白质组学数据，但几乎没有更新。另一方面，由两个独立的深度学习模块组成的 DeepIso 通过多层神经元自行学习高维数据的多个表示层次，并适应新获取的数据。我们的模型报告的肽特征列表与基准数据集 97.43%的高质量 MS/MS 鉴定相匹配，高于几个广泛使用的工具生成的匹配。我们的结果表明，新的深度学习工具是推进蛋白质鉴定和定量的最先进技术所必需的。
