De novo peptide sequencing is the key technology for finding novel peptides from mass spectra. The overall quality of sequencing results depends on the de novo peptide sequencing algorithm as well as the quality of mass spectra. Over the past decade, the resolution and accuracy of mass spectrometers have improved by orders of magnitude and higher-resolution mass spectra have been generated. How to effectively take advantage of those high-resolution data without substantially increasing the computational complexity remains a challenge for de novo peptide sequencing tools. Here we present PointNovo, a neural network-based de novo peptide sequencing model that can robustly handle any resolution levels of mass spectrometry data while keeping the computational complexity unchanged. Our extensive experiment results show PointNovo outperforms existing de novo peptide sequencing tools by capitalizing on the ultra-high resolution of the latest mass spectrometers.

从头肽测序是从质谱中寻找新肽的关键技术。测序结果的整体质量取决于从头肽测序算法以及质谱的质量。在过去的十年中，质谱仪的分辨率和准确度提高了几个数量级，并产生了更高分辨率的质谱。如何在不显着增加计算复杂性的情况下有效利用这些高分辨率数据仍然是从头肽测序工具面临的挑战。在这里，我们介绍了 PointNovo，这是一种基于神经网络的从头肽测序模型，可以稳健地处理任何分辨率级别的质谱数据，同时保持计算复杂性不变。