A Comprehensive Evaluation of MS/MS Spectrum Prediction Tools for Shotgun Proteomics. Issue 21 (10th July 2020)

• Record Type: Journal Article
• Authors: Xu, Rui; Sheng, Jie; Bai, Mingze; Shu, Kunxian; Zhu, Yunping; Chang, Cheng
• Other Names: Wen Bo guestEditor.; Zhang Bing guestEditor.
• Is Part Of: Proteomics. Volume 20:Issue 21/22(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-07-10
• Subjects: Proteins -- Separation -- Periodicals; Bioinformatics -- Periodicals; Proteomics -- Periodicals; Genomes -- Periodicals; Molecular genetics -- Periodicals; 572.605
• DOI: 10.1002/pmic.201900345
• Languages: English
• ISSNs: 1615-9853

Abstract

Abstract: Spectrum prediction using machine learning or deep learning models is an emerging method in computational proteomics. Several deep learning‐based MS/MS spectrum prediction tools have been developed and showed their potentials not only for increasing the sensitivity and accuracy of data‐dependent acquisition search engines, but also for building spectral libraries for data‐independent acquisition analysis. Different tools with their unique algorithms and implementations may result in different performances. Hence, it is necessary to systematically evaluate these tools to find out their preferences and intrinsic differences. In this study, multiple datasets with different collision energies, enzymes, instruments, and species, are used to evaluate the performances of the deep learning‐based MS/MS spectrum prediction tools, as well as, the machine learning‐based tool MS2PIP. The evaluations may provide helpful insights and guidelines of spectrum prediction tools for the corresponding researchers.