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De novo peptide sequencing from mass spectrometry (MS) data is a criticaltask in proteomics research. Traditional de novo algorithms have encountered abottleneck in accuracy due to the inherent complexity of proteomics data. Whiledeep learning-based methods have shown progress, they reduce the problem to atranslation task, potentially overlooking critical nuances between spectra andpeptides. In our research, we present ContraNovo, a pioneering algorithm thatleverages contrastive learning to extract the relationship between spectra andpeptides and incorporates the mass information into peptide decoding, aiming toaddress these intricacies more efficiently. Through rigorous evaluations on twobenchmark datasets, ContraNovo consistently outshines contemporarystate-of-the-art solutions, underscoring its promising potential in enhancingde novo peptide sequencing. The source code is available athttps://github.com/BEAM-Labs/ContraNovo.

ContraNovo: A Contrastive Learning Approach to Enhance De Novo Peptide Sequencing