XA-Novo: high-throughput mass spectrometry-based de novo sequencing technology for monoclonal antibodies and antibody mixtures
Elucidating antibody sequences by mass spectrometry-based de novo sequencing is essential but remains technically challenging. Here we present XA-Novo, an accurate and high-throughput de novo sequencing solution that integrates a single-pot multi-enzymatic gradient digestion method with a beam search-based assembler (Fusion) to reconstruct full-length antibody sequences directly from bottom-up mass spectrometry data. Benchmarking across well-characterized antibodies from multiple species demonstrates that XA-Novo outperforms commercial solutions in identification sensitivity, sequence completeness, and reconstruction accuracy. Furthermore, XA-Novo successfully reconstructs six immunotherapeutic antibodies with unknown sequences, and in vitro/vivo assays validate that these generated antibodies exhibit functionality equivalent to their commercial counterparts. Moreover, XA-Novo achieves over 99.54% accurate sequence coverage in distinguishing mixed COVID-19 neutralizing antibodies, exceeding the performance of current assemblers reported for single-antibody sequencing. Overall, XA-Novo establishes a reliable, scalable, and broadly applicable workflow for routine antibody sequencing, thereby accelerating both fundamental antibody research and therapeutic antibody development.
Authors
- Zhenjian Jiang
- Ningshao Xia (ORCID: https://orcid.org/0000-0003-0179-5266)
- Yueting Xiong (ORCID: https://orcid.org/0009-0008-8471-9800)
- Rongshan Yu
- Xiaoqing Chen (ORCID: https://orcid.org/0000-0001-5225-4676)
- Qingfang Bu
- Wenbin Jiang
- Yangtao Wu
- Ling Luo
- Quan Yuan
- Fan Liu
- Yijie Qiu
- Jingyi Wang
- Jin Xiao
Institutions
- Xiamen University (CN)
- Zhongshan Hospital of Xiamen University (CN)
Publication Details
- Journal
- Nature Communications
- Published
- 2026-03-12
- DOI
- https://doi.org/10.1038/s41467-026-70496-y
- Primary Topic
- Monoclonal and Polyclonal Antibodies Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Fujian Province
- National Science and Technology Major Project