ContraNovo: A Contrastive Learning Approach to Enhance De Novo Peptide
  Sequencing
Zhi Jin Sheng Xu Xiang Zhang Tianze Ling Nanqing Dong Wanli Ouyang Zhiqiang Gao Cheng Chang Siqi Sun
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
https://github.com/BEAM-Labs/ContraNovo.