近日，德国慕尼黑工业大学Bernhard Kuster与Mathias Wilhelm研究团队合作研发了借助深度学习来分析质谱肽段从而预测蛋白质组学的人工智能软件——Prosit。 2019年6月出版的《Nature Methods》发表了这项成果。

首先，该课题组人员将合成肽段库扩充至55万条并带有2100万个高质量的光谱数据。然后，他们通过深度网络神经算法训练了一个叫做Prosit的软件，能够实现对色谱保留时间和碎片离子强度进行高质量预测。借助于Prosit，传统数据库搜索匹配蛋白的错误率降低了10倍以上。研究人员表示，Prosit还可于非胰酶消化处理样品的光谱预测分析，从而可提升对元蛋白质组学的分析效率。Prosit现在已经整合到蛋白质组学数据库（ProteomicsDB）中，这将使得质谱数据的分析不再受限于物种的类型，而只依赖检测肽段本身的序列。

在基于质谱的蛋白质组学研究中，多肽和蛋白质的鉴定和定量主要依赖于序列数据库搜索或光谱库匹配。由于缺乏精准的预测分析工具，质谱的潜能并未完全开发。

附：英文原文

Title: Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning

Author: Siegfried Gessulat, Tobias Schmidt, Daniel Paul Zolg, Patroklos Samaras, Karsten Schnatbaum, Johannes Zerweck, Tobias Knaute, Julia Rechenberger, Bernard Delanghe, Andreas Huhmer, Ulf Reimer, Hans-Christian Ehrlich, Stephan Aiche, Bernhard Kuster, Mathias Wilhelm

Issue&Volume: Volume 16 Issue 6, June 2019

Abstract: In mass-spectrometry-based proteomics, the identification and quantification of peptides and proteins heavily rely on sequence database searching or spectral library matching. The lack of accurate predictive models for fragment ion intensities impairs the realization of the full potential of these approaches. Here, we extended the ProteomeTools synthetic peptide library to 550,000 tryptic peptides and 21million high-quality tandem mass spectra. We trained a deep neural network, termed Prosit, resulting in chromatographic retention time and fragment ion intensity predictions that exceed the quality of the experimental data. Integrating Prosit into database search pipelines led to more identifications at >10 lower false discovery rates. We show the general applicability of Prosit by predicting spectra for proteases other than trypsin, generating spectral libraries for data-independent acquisition and improving the analysis of metaproteomes. Prosit is integrated into ProteomicsDB, allowing search result re-scoring and custom spectral library generation for any organism on the basis of peptide sequence alone.

DOI: 10.1038/s41592-019-0426-7

Source: https://www.nature.com/articles/s41592-019-0426-7