AbNovoBench: a resource and benchmarking platform for monoclonal antibody de novo sequencing

Abstract

Monoclonal antibodies (mAbs) are critical in disease diagnostics and therapeutics, yet the performance of mass spectrometry (MS)-based de novo sequencing remains incompletely characterized due to limited antibody-specific datasets and the absence of a standardized benchmark framework. Here we present AbNovoBench, a comprehensive framework for evaluating data analysis strategies for mAb de novo sequencing. It features the largest high-quality dataset to date, generated in-house, comprising 1,638,248 peptide-spectrum matches from 131 mAbs across six species and 11 proteases, supplemented by eight mAbs with known full-length sequence for end-to-end reconstruction assessment. Employing a unified training dataset, we systematically benchmarked 13 deep learning-based de novo peptide sequencing algorithms and three assembly strategies across peptide sequencing metrics (accuracy, robustness, efficiency, error types) and assembly metrics (coverage depth, assembly score). AbNovoBench (https://abnovobench.com) provides an online platform enriched with curated antibody MS resources and pre-trained models, enabling customizable antibody sequencing workflows, accelerating antibody-specific algorithms development, and improving reproducibility in proteomics.