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Open Access

DMSS: An Attention-Based Deep Learning Model for High-Quality Mass Spectrometry Prediction

Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China, and with University of Chinese Academy of Sciences, Beijing 100049, China
Syneron Technology, Guangzhou 510000, China
College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
Insitute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China, and with Western Institute of Computing Technology, Chongqing 400000, China

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Abstract

Accurate prediction of peptide spectra is crucial for improving the efficiency and reliability of proteomic analysis, as well as for gaining insight into various biological processes. In this study, we introduce Deep MS Simulator (DMSS), a novel attention-based model tailored for forecasting theoretical spectra in mass spectrometry. DMSS has undergone rigorous validation through a series of experiments, consistently demonstrating superior performance compared to current methods in forecasting theoretical spectra. The superior ability of DMSS to distinguish extremely similar peptides highlights the potential application of incorporating our predicted intensity information into mass spectrometry search engines to enhance the accuracy of protein identification. These findings contribute to the advancement of proteomics analysis and highlight the potential of the DMSS as a valuable tool in the field.

Keywords

mass spectrometryproteomicsmachine learningdeep learning

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Big Data Mining and Analytics
Volume 7 Issue 3,
September 2024
Pages 577-589
DOI: 10.26599/BDMA.2024.9020006
Cite this article:
Ren Y, Wang Y, Han W, et al. DMSS: An Attention-Based Deep Learning Model for High-Quality Mass Spectrometry Prediction. Big Data Mining and Analytics, 2024, 7(3): 577-589. https://doi.org/10.26599/BDMA.2024.9020006

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Received: 07 November 2023
Revised: 25 December 2023
Accepted: 29 January 2024
Published: 28 August 2024
© The author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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