论文标题
从元蛋白质组学数据集中识别肽识别的深度学习
Deep learning for peptide identification from metaproteomics datasets
论文作者
论文摘要
元蛋白质组学已广泛用于微生物组研究中，以了解对微生物群落的功能状态的见解。当前的元蛋白质组学研究通常基于与液相色谱法相连的高通量串联质谱（MS/MS）。从MS数据中鉴定肽和蛋白质涉及针对预定义蛋白序列数据库搜索MS/MS光谱的计算过程，并将最高得分的肽分配给光谱。现有的计算工具仍然无法从从元蛋白酶样本中获取的大型MS/MS数据集中提取所有信息。在本文中，我们提出了一种基于深度学习的算法，称为DeepFilter，以提高串联质谱集合中自信的肽识别率。与其他后处理工具相比，包括渗透剂，Q-ranker，PeptideProphet和Iprophet，DeepFilter分别在海洋微生物和土壤微生物元二磷酸蛋白质组上，分别鉴定出20％和10％的肽 - 光谱匹配和蛋白质，其发现率为1％。
Metaproteomics are becoming widely used in microbiome research for gaining insights into the functional state of the microbial community. Current metaproteomics studies are generally based on high-throughput tandem mass spectrometry (MS/MS) coupled with liquid chromatography. The identification of peptides and proteins from MS data involves the computational procedure of searching MS/MS spectra against a predefined protein sequence database and assigning top-scored peptides to spectra. Existing computational tools are still far from being able to extract all the information out of large MS/MS datasets acquired from metaproteome samples. In this paper, we proposed a deep-learning-based algorithm, called DeepFilter, for improving the rate of confident peptide identifications from a collection of tandem mass spectra. Compared with other post-processing tools, including Percolator, Q-ranker, PeptideProphet, and Iprophet, DeepFilter identified 20% and 10% more peptide-spectrum-matches and proteins, respectively, on marine microbial and soil microbial metaproteome samples with false discovery rate at 1%.
