Universal Biological Sequence Reranking for Improved De Novo Peptide
  Sequencing
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.
6834b5158c6b65c0506d12c6
Zijie Qiu
6834b5158c6b65c0506d12c7
Jiaqi Wei
6834b5158c6b65c0506d12c8
Xiang Zhang
6834b5158c6b65c0506d12c9
Sheng Xu
6834b5158c6b65c0506d12ca
Kai Zou
6834b5158c6b65c0506d12cb
Zhi Jin
6834b5158c6b65c0506d12cc
Zhiqiang Gao
6834b5158c6b65c0506d12cd
Nanqing Dong
6834b5158c6b65c0506d12ce
Siqi Sun
claimed_verified
2025-06-17T13:07:17.634Z
67d3a1a5943a965360fcae51
/avatars/165ed684b0750e7f57b9f2babfb47a8c.svg
Siqi Sun
user
siqisun
Universal Biological Sequence Reranking for Improved De Novo Peptide
  Sequencing
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.
RankNovo is a deep reranking framework that enhances de novo peptide sequencing using multiple models and axial attention, achieving superior performance and generalization.
RankNovo deep reranking framework de novo peptide sequencing axial attention PMD RMD zero-shot generalization