title: Universal Biological Sequence Reranking for Improved De Novo Peptide
    Sequencing
summary: 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.
paper_authors._id: 6834b5158c6b65c0506d12c6
paper_authors.name: Zijie Qiu
paper_authors._id: 6834b5158c6b65c0506d12c7
paper_authors.name: Jiaqi Wei
paper_authors._id: 6834b5158c6b65c0506d12c8
paper_authors.name: Xiang Zhang
paper_authors._id: 6834b5158c6b65c0506d12c9
paper_authors.name: Sheng Xu
paper_authors._id: 6834b5158c6b65c0506d12ca
paper_authors.name: Kai Zou
paper_authors._id: 6834b5158c6b65c0506d12cb
paper_authors.name: Zhi Jin
paper_authors._id: 6834b5158c6b65c0506d12cc
paper_authors.name: Zhiqiang Gao
paper_authors._id: 6834b5158c6b65c0506d12cd
paper_authors.name: Nanqing Dong
paper_authors._id: 6834b5158c6b65c0506d12ce
paper_authors.name: Siqi Sun
paper_title: Universal Biological Sequence Reranking for Improved De Novo Peptide
    Sequencing
paper_summary: 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.
paper_ai_summary: RankNovo is a deep reranking framework that enhances de novo peptide sequencing using multiple models and axial attention, achieving superior performance and generalization.
paper_ai_keywords: RankNovo deep reranking framework de novo peptide sequencing axial attention PMD RMD zero-shot generalization