De novo peptide sequencing from mass spectrometry (MS) data is a critical
task in proteomics research. Traditional de novo algorithms have encountered a
bottleneck in accuracy due to the inherent complexity of proteomics data. While
deep learning-based methods have shown progress, they reduce the problem to a
translation task, potentially overlooking critical nuances between spectra and
peptides. In our research, we present ContraNovo, a pioneering algorithm that
leverages contrastive learning to extract the relationship between spectra and
peptides and incorporates the mass information into peptide decoding, aiming to
address these intricacies more efficiently. Through rigorous evaluations on two
benchmark datasets, ContraNovo consistently outshines contemporary
state-of-the-art solutions, underscoring its promising potential in enhancing
de novo peptide sequencing. The source code is available at
this https URL

ContraNovo 算法是一种创新算法，通过对质谱和肽段之间的关系进行对比学习，并将质量信息纳入肽段解码，从而更高效地解决肽段测序问题，并在两个基准数据集上得到了可靠的结果，显示出其在提升新肽段测序中的潜力。