Diffusion Decoding for Peptide De Novo Sequencing A new study investigates diffusion decoders for peptide de novo sequencing, addressing limitations of traditional autoregressive models like cascading errors and inefficient use of high-confidence regions. The research demonstrates that while simply replacing a transformer decoder with a diffusion decoder initially lowered performance, incorporating the DINOISER loss function significantly improved amino acid recall by 0.373 compared to the Casanovo baseline. This work highlights the potential of diffusion decoders to enhance model sensitivity and advance peptide de novo sequencing by allowing sequence generation to start from any peptide segment, leveraging high-confidence parts. Despite the improvements in amino acid recall, the models with diffusion decoders still showed limitations in peptide precision and coverage, often producing sequences longer than the ground truth with noise at the end. The study used Casanovo as a development framework due to its relatively lower computational demands and higher performance, training and evaluating models on a combined tryptic and non-tryptic dataset. The authors explored three different diffusion decoder designs (Casanovo-DS, Casanovo-DM1, and Casanovo-DM2) and evaluated various loss functions, finding DINOISER to be optimal for amino acid recall. Knapsack beam search was also investigated but resulted in worse metric performance and significantly increased training time, from approximately 1.5 hours to 35 hours for the tested models. Future work aims to address the issue of excess noise in predicted sequences, explore hyperparameter tuning for diffusion decoder models, and conduct fair comparisons with other state-of-the-art methods using consistent datasets. 📜Paper: arxiv.org/abs/2507.10955v1 #PeptideSequencing #DeepLearning #DiffusionModels #MassSpectrometry #Bioinformatics

Pairwise Attention: Leveraging Mass Differences to Enhance De Novo Sequencing of Mass Spectra 1. This study introduces Pairwise Attention (PA), a transformer-based model that explicitly encodes pairwise mass differences between spectrum peaks as a learned bias, significantly improving de novo peptide sequencing accuracy. 2. Inspired by how experts manually interpret MS2 spectra—by detecting consistent mass differences between peaks—PA integrates this inductive bias directly into the attention mechanism of the transformer encoder. 3. PA adds only 0.1% more parameters to the model while achieving a 14.2% (6.7 percentage point) improvement in peptide-level precision at 100% coverage over a baseline transformer, and a 7.2% (3.6 point) gain over the state-of-the-art Casanovo. 4. The learned pairwise mass difference bias allows the model to better capture fragmentation patterns like b- and y-ions, enhancing interpretability and boosting accuracy on both clean and noisy spectra. 5. Evaluation on the nine-species benchmark (versions V1 and V2) shows consistent gains for PA across all species, outperforming Casanovo on most tasks, especially for species with fewer spectra or lower data quality. 6. On an independent bacterial dataset, PA generalized better than both the base model and Casanovo, achieving an 8.5 percentage point precision gain—demonstrating robustness beyond benchmark data. 7. Despite the added memory and training time cost (~31% slower), PA’s improvements are robust across datasets, model configurations, and random seeds, suggesting the utility of domain-specific feature engineering in deep learning. 8. The PA model architecture retains the standard decoder from Casanovo but modifies the encoder with a Fourier-encoded pairwise m/z difference matrix, injected into the self-attention layers as a learnable bias. 9. Results show that the base transformer may struggle to fully learn pairwise spectral features from scratch, whereas explicitly encoding this structure improves convergence and final performance. 10. The authors provide an open-source implementation of the PA model, enabling integration with other transformer-based mass spec models, and setting a new standard in interpretable, domain-aware de novo sequencing. 💻Code: github.com/statisticalbiotec… 📜Paper: biorxiv.org/content/10.1101/… #MassSpectrometry #DeNovoSequencing #Proteomics #TransformerModels #DeepLearning #Bioinformatics #MS2 #PeptideIdentification #AttentionMechanisms #ComputationalBiology

AbNovoBench: A Resource and Benchmarking Platform for Monoclonal Antibody De Novo Sequencing 1 The field of monoclonal antibody de novo sequencing finally has a comprehensive benchmarking platform it desperately needed. AbNovoBench introduces the largest high-quality dataset to date with over 1.6 million peptide-spectrum matches from 131 antibodies across six species and 11 proteases. 2 What makes this resource truly valuable is the systematic evaluation of 13 deep learning-based de novo peptide sequencing algorithms using unified training data and standardized metrics, enabling fair comparison across methods that was previously impossible due to inconsistent evaluation protocols. 3 The study reveals that transformer-based architectures, particularly ContraNovo and Casanovo variants, consistently outperform recurrent neural network and graph-based models across amino acid, peptide, and post-translational modification accuracy metrics. 4 A surprising finding challenges the assumption that bigger training datasets always yield better results. Casanovo v1, trained on just 2 million PSMs with three common modifications, achieved comparable or superior PTM precision to ContraNovo trained on 30 million PSMs, suggesting domain-informed constraint can be more valuable than raw data volume. 5 The platform introduces antibody-specific evaluation metrics including coverage depth analysis for complementarity-determining regions, which are critical for assembly but often underrepresented in sequencing outputs compared to constant regions. 6 For full-length antibody reconstruction, the template-guided Fusion assembler achieved error-free reconstruction when combined with high-quality peptide predictions, while fully de novo assembly remains challenging for unknown sequences. 7 The analysis identifies key limiting factors affecting all algorithms: peptide recall drops sharply beyond 19-20 residues, missed cleavage sites significantly impair performance, and spectral noise shows strong negative correlation with accuracy across all models. 8 Cross-enzyme robustness testing reveals substantial performance variations, with transformer models maintaining stability across both tryptic and non-tryptic conditions while other architectures degrade significantly under challenging cleavage regimes like thermolysin and Asp-N. 9 The web platform at abnovobench.com provides pre-trained models, curated datasets, and customizable workflows, eliminating the need for researchers to retrain models from scratch and enabling real-time tracking of methodological advances in the field. 10 The study acknowledges current limitations including reliance on HCD fragmentation data and the need for expansion to EThcD, UVPD, and data-independent acquisition workflows in future platform updates. 💻Code: github.com/AbNovoBench 📜Paper: biorxiv.org/content/10.64898… #AbNovoBench #MonoclonalAntibody #DeNovoSequencing #MassSpectrometry #Proteomics #DeepLearning #Benchmarking #Bioinformatics #ComputationalBiology #AntibodyEngineering