Wihelm, M., Zolg, D.P., Graber, M., et al. (2021) Deep Learning Boosts Sensitivity of Mass Spectrometry-Based Immunopeptidomics. Nature Communications, 12, Article No. 3346.
https://doi.org/10.1038/s41467-021-24263-w
https://www.nature.com/articles/s41467-021-23713-9

has been cited by the following article:

• TITLE: Research on the Upper Limit of Accuracy for Predicting Theoretical Tandem Mass Spectrometry

  AUTHORS: Changjiu He, Xiaoyu Wang, Mingming Lyu, Xinye Bian

  KEYWORDS: Tandem Mass Spectrometry, Spectral Prediction, Theoretical Limit

  JOURNAL NAME: Journal of Computer and Communications, Vol.12 No.3, March 27, 2024

  ABSTRACT: In recent years, numerous theoretical tandem mass spectrometry prediction methods have been proposed, yet a systematic study and evaluation of their theoretical accuracy limits have not been conducted. If the accuracy of current methods approaches this limit, further exploration of new prediction techniques may become redundant. Conversely, a need for more precise prediction methods or models may be indicated. In this study, we have experimentally analyzed the limits of accuracy at different numbers of ions and parameters using repeated spectral pairs and integrating various similarity metrics. Results show significant achievements in accuracy for backbone ion methods with room for improvement. In contrast, full-spectrum prediction methods exhibit greater potential relative to the theoretical accuracy limit. Additionally, findings highlight the significant impact of normalized collision energy and instrument type on prediction accuracy, underscoring the importance of considering these factors in future theoretical tandem mass spectrometry predictions.