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.

中文翻译：

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对用于 Shotgun 蛋白质组学的 MS/MS 光谱预测工具的综合评估。

使用机器学习或深度学习模型进行频谱预测是计算蛋白质组学中的一种新兴方法。已经开发了几种基于深度学习的 MS/MS 光谱预测工具，它们不仅在提高数据相关采集搜索引擎的灵敏度和准确性方面具有潜力，而且在构建用于数据独立采集分析的光谱库方面也具有潜力。具有独特算法和实现的不同工具可能会导致不同的性能。因此，有必要系统地评估这些工具以找出它们的偏好和内在差异。在本研究中，使用具有不同碰撞能量、酶、仪器和物种的多个数据集来评估基于深度学习的 MS/MS 谱预测工具的性能，以及，基于机器学习的工具 MS2PIP。评估可以为相应的研究人员提供有用的见解和频谱预测工具的指导。