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

TL;DR

DiffuNovo introduces a diffusion-based model for de novo peptide sequencing that explicitly enforces mass constraints, significantly improving accuracy and physical plausibility over previous methods.

Contribution

It is the first to incorporate a diffusion model with explicit mass control in de novo peptide sequencing, enhancing prediction accuracy and physical plausibility.

Findings

Outperforms state-of-the-art DNPS methods in accuracy.

Reduces mass error significantly, producing more plausible peptides.

Employs a novel mass loss and guidance mechanism during training and inference.

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.