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

• DOI: 10.48550/arxiv.2312.11584
• Publication Date: 2023-01-01
• Authors: Zhi Jin; Sheng Xu; Xiang Zhang; Tianze Ling; Nanqing Dong; Wanli Ouyang; Zhiqiang Gao; Cheng Chang; Siqi Sun
• Keywords: Benchmark (surveying)

De novo peptide sequencing from mass spectrometry (MS) data is a critical task in proteomics research. Traditional de algorithms have encountered bottleneck accuracy due to the inherent complexity of data. While deep learning-based methods shown progress, they reduce problem translation task, potentially overlooking nuances between spectra and peptides. In our research, we present ContraNovo, pioneering algorithm that leverages contrastive learning extract relationship peptides incorporates information into decoding, aiming 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 enhancing sequencing. The source code available at https://github.com/BEAM-Labs/ContraNovo.