Regressor-guided Diffusion Model for De Novo Peptide Sequencing with Explicit Mass Control

peer-reviewed · AAAI 2026 · 2026

peer-reviewed · AAAI 2026 · 2026. Shaorong Chen et al. The discovery of novel proteins relies on sensitive protein identification, for which de novo peptide…
Date 2026-03-14
Type peer-reviewed
Venue AAAI 2026
Publisher AAAI Press
Contribution algorithm
DOI 10.1609/aaai.v40i1.36968
Citations (OpenAlex) 0

Preprint version: Regressor-guided Diffusion Model for De Novo Peptide Sequencing with Explicit Mass Control (2026-02-23, arXiv)

Abstract

The discovery of novel proteins relies on sensitive protein identification, for which de novo peptide sequencing (DNPS) from mass spectra is a crucial approach. While deep learning has advanced DNPS, existing models inadequately enforce the fundamental mass consistency constraint—that a predicted peptide’s mass must match the experimental measured precursor mass. Previous DNPS methods often treat this critical information as a simple input feature or use it in post-processing, leading to numerous implausible predictions that do not adhere to this fundamental physical property. To address this limitation, we introduce DiffuNovo, a novel regressor-guided diffusion model for de novo peptide sequencing that provides explicit peptide-level mass control. Our approach integrates the mass constraint at two critical stages: during training, a novel peptide-level mass loss guides model optimization, while at inference, regressor-based guidance from gradient-based updates in the latent space steers the generation to compel the predicted peptide adheres to the mass constraint. Comprehensive evaluations on established benchmarks demonstrate that DiffuNovo surpasses state-of-the-art methods in DNPS accuracy. Additionally, as the first DNPS model to employ a diffusion model as its core backbone, DiffuNovo leverages the powerful controllability of diffusion architecture and achieves a significant reduction in mass error, thereby producing much more physically plausible peptides. These innovations represent a substantial advancement toward robust and broadly applicable DNPS. The source code is available in the supplementary material.

Authors

  1. Shaorong Chen · Westlake University, Zhejiang University
  2. Jingbo Zhou · Westlake University, Zhejiang University
  3. Jun Xia · The Hong Kong University of Science and Technology, The Hong Kong University of Science and Technology (Guangzhou), Westlake University

Methods and tools

Data used

  • ProteomeTools (HC-PT (NovoBench)) · no public address
  • Seven-species benchmark (NovoBench split) · no public address

Cites (8)

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