Dataset Information

MSpectraAI: a powerful platform for deciphering proteome profiling of multi-tumor mass spectrometry data by using deep neural networks.

ABSTRACT:

Background

Mass spectrometry (MS) has become a promising analytical technique to acquire proteomics information for the characterization of biological samples. Nevertheless, most studies focus on the final proteins identified through a suite of algorithms by using partial MS spectra to compare with the sequence database, while the pattern recognition and classification of raw mass-spectrometric data remain unresolved.

Results

We developed an open-source and comprehensive platform, named MSpectraAI, for analyzing large-scale MS data through deep neural networks (DNNs); this system involves spectral-feature swath extraction, classification, and visualization. Moreover, this platform allows users to create their own DNN model by using Keras. To evaluate this tool, we collected the publicly available proteomics datasets of six tumor types (a total of 7,997,805 mass spectra) from the ProteomeXchange consortium and classified the samples based on the spectra profiling. The results suggest that MSpectraAI can distinguish different types of samples based on the fingerprint spectrum and achieve better prediction accuracy in MS1 level (average 0.967).

Conclusion

This study deciphers proteome profiling of raw mass spectrometry data and broadens the promising application of the classification and prediction of proteomics data from multi-tumor samples using deep learning methods. MSpectraAI also shows a better performance compared to the other classical machine learning approaches.

SUBMITTER: Wang S

PROVIDER: S-EPMC7539376 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Publications

MSpectraAI: a powerful platform for deciphering proteome profiling of multi-tumor mass spectrometry data by using deep neural networks.

Wang Shisheng S Zhu Hongwen H Zhou Hu H Cheng Jingqiu J Yang Hao H

BMC bioinformatics 20201007 1

<h4>Background</h4>Mass spectrometry (MS) has become a promising analytical technique to acquire proteomics information for the characterization of biological samples. Nevertheless, most studies focus on the final proteins identified through a suite of algorithms by using partial MS spectra to compare with the sequence database, while the pattern recognition and classification of raw mass-spectrometric data remain unresolved.<h4>Results</h4>We developed an open-source and comprehensive platform, ...[more]

PMID: 33028193

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Project description:Histones are highly posttranslationally modified proteins that regulate gene expression by modulating chromatin structure and function. Acetylation and methylation are the most abundant histone modifications, with methylation occurring on lysine (mono-, di-, and trimethylation) and arginine (mono- and dimethylation) predominately on histones H3 and H4. In addition, arginine dimethylation can occur either symmetrically (SDMA) or asymmetrically (ADMA) conferring different biological functions. Despite the importance of histone methylation on gene regulation, characterization and quantitation of this modification have proven to be quite challenging. Great advances have been made in the analysis of histone modification using both bottom-up and top-down mass spectrometry (MS). However, MS-based analysis of histone posttranslational modifications (PTMs) is still problematic, due both to the basic nature of the histone N-terminal tails and to the combinatorial complexity of the histone PTMs. In this report, we describe a simplified MS-based platform for histone methylation analysis. The strategy uses chemical acetylation with d0-acetic anhydride to collapse all the differently acetylated histone forms into one form, greatly reducing the complexity of the peptide mixture and improving sensitivity for the detection of methylation via summation of all the differently acetylated forms. We have used this strategy for the robust identification and relative quantitation of H4R3 methylation, for which stoichiometry and symmetry status were determined, providing an antibody-independent evidence that H4R3 is a substrate for both Type I and Type II PRMTs. Additionally, this approach permitted the robust detection of H4K5 monomethylation, a very low stoichiometry methylation event (0.02% methylation). In an independent example, we developed an in vitro assay to profile H3K27 methylation and applied it to an EZH2 mutant xenograft model following small-molecule inhibition of the EZH2 methyltransferase. These specific examples highlight the utility of this simplified MS-based approach to quantify histone methylation profiles.

Dataset Information

MSpectraAI: a powerful platform for deciphering proteome profiling of multi-tumor mass spectrometry data by using deep neural networks.

Background

Results

Conclusion

Publications

MSpectraAI: a powerful platform for deciphering proteome profiling of multi-tumor mass spectrometry data by using deep neural networks.

Similar Datasets

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