Universal Biological Sequence Reranking for Improved De Novo Peptide
  Sequencing
Authors: name: Zijie Qiu, name: Jiaqi Wei, name: Xiang Zhang, name: Sheng Xu, name: Kai Zou, name: Zhi Jin, name: Zhiqiang Gao, name: Nanqing Dong, name: Siqi Sun

De novo peptide sequencing is a critical task in proteomics. However, the
performance of current deep learning-based methods is limited by the inherent
complexity of mass spectrometry data and the heterogeneous distribution of
noise signals, leading to data-specific biases. We present RankNovo, the first
deep reranking framework that enhances de novo peptide sequencing by leveraging
the complementary strengths of multiple sequencing models. RankNovo employs a
list-wise reranking approach, modeling candidate peptides as multiple sequence
alignments and utilizing axial attention to extract informative features across
candidates. Additionally, we introduce two new metrics, PMD (Peptide Mass
Deviation) and RMD (residual Mass Deviation), which offer delicate supervision
by quantifying mass differences between peptides at both the sequence and
residue levels. Extensive experiments demonstrate that RankNovo not only
surpasses its base models used to generate training candidates for reranking
pre-training, but also sets a new state-of-the-art benchmark. Moreover,
RankNovo exhibits strong zero-shot generalization to unseen models whose
generations were not exposed during training, highlighting its robustness and
potential as a universal reranking framework for peptide sequencing. Our work
presents a novel reranking strategy that fundamentally challenges existing
single-model paradigms and advances the frontier of accurate de novo
sequencing. Our source code is provided on GitHub.