期刊
ANALYTICAL CHEMISTRY
卷 92, 期 11, 页码 7778-7785出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.analchem.0c00903
关键词
-
资金
- National Institute of General Medical Sciences, National Institutes of Health (NIH) [R01GM118470, R01GM125991]
Top-down mass spectrometry has become the main method for intact proteoform identification, characterization, and quantitation. Because of the complexity of top-down mass spectrometry data, spectral deconvolution is an indispensable step in spectral data analysis, which groups spectral peaks into isotopic envelopes and extracts monoisotopic masses of precursor or fragment ions. The performance of spectral deconvolution methods relies heavily on their scoring functions, which distinguish correct envelopes from incorrect ones. A good scoring function increases the accuracy of deconvoluted masses reported from mass spectra. In this paper, we present EnvCNN, a convolutional neural network-based model for evaluating isotopic envelopes. We show that the model outperforms other scoring functions in distinguishing correct envelopes from incorrect ones and that it increases the number of identifications and improves the statistical significance of identifications in top-down spectral interpretation.
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