cs.LGSep 24, 2026

GyroNovo: Error-Guided Fragment Imputation with Mass-Aware Attention for De Novo Peptide Sequencing

Authors: Abdellah El Mekki, Laks V. S. Lakshmanan, Muhammad Abdul-Mageed

Organizations: The University of British Columbia · Canada Research Chair in NLP and ML

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Abstract

De novo peptide sequencing from tandem mass spectra is essential for identifying peptides without relying on reference databases. Despite advances in deep learning, accurate sequencing remains challenging because experimental spectra are often sparse, noisy, and incomplete, leaving informative b- and y-ion fragments unobserved. Existing methods attempt to recover this missing evidence via latent-space imputation before autoregressive decoding. However, they typically treat imputation as a fixed reconstruction task, without considering which missing fragments are most relevant to decoder errors. Moreover, existing peak representations do not explicitly model mass differences between peaks, despite their fundamental importance. We introduce GyroNovo, a framework with two main contributions. First, we use decoder errors observed during training to adapt the imputation objective, prioritizing fragments associated with frequent decoding errors. We further use the decoder error distribution to construct easy and hard augmented views of each spectrum, enabling the decoder to learn under varying degrees of spectral corruption and missing-fragment severity. Second, we introduce a mass-aware inductive bias into self-attention by using rotary embeddings to encode pairwise mass differences between spectral peaks. Together, these components align missing-fragment recovery with decoder behavior while explicitly incorporating the mass relationships that underlie peptide fragmentation. At inference time, GyroNovo retains a standard encoder-imputer-decoder architecture and requires neither additional inputs nor auxiliary search procedures. Experiments on NovoBench show gains of about 9 percentage points in peptide-level precision and 7 percentage points in amino-acid-level precision over the state-of-the-art baseline. Code: https://github.com/UBC-NLP/gyronovo.

Figures & tables

Figure 1: Overview of GyroNovo ’s error-guided imputation and difficulty-aware multi-view training framework.
Figure 2: Adjacent ion-ladder neighbor retrieval performance of vanilla and mass-aware rotary attention on Nine-Species dataset.
ConfigurationAmino Acid LevelPeptide Level
PrecisionRecallPrecisionAUC
Baseline0.7940.7960.5830.550
++ Imputation-loss reweighting0.7980.7980.5860.549
++ Augmented Views0.8290.8290.6390.607
++ Mass Rotary Attention0.8480.8480.6740.646
Table 3: Cumulative ablation study of GyroNovo on the Nine-Species dataset. Components are added sequentially to the reproduced baseline. Best results are shown in bold.
 TrainingInference
ModelThrpt. ↑\uparrowMem. ↓\downarrowThrpt. ↑\uparrowMem. ↓\downarrow
Baseline1.00 ×\times1.00 ×\times1.00 ×\times1.00 ×\times
GyroNovo-Pairwise0.27 ×\times4.02 ×\times1.00 ×\times3.86 ×\times
GyroNovo (ours)0.30 ×\times2.08 ×\times1.00 ×\times1.01 ×\times
Table 4: Training and inference efficiency of the baseline, GyroNovo-Pairwise, and our GyroNovo. Throughput and peak memory usage are normalized to the baseline; higher throughput and lower memory usage are better.
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