Evidence map›Paper›PMID 41820386›Full record

ArticleNature communications2026

XA-Novo: high-throughput mass spectrometry-based de novo sequencing technology for monoclonal antibodies and antibody mixtures.

Yueting Xiong, Wenbin Jiang, Jin Xiao, Qingfang Bu, Jingyi Wang, Zhenjian Jiang, Ling Luo, Xiaoqing Chen, Yijie Qiu, Yangtao Wu and 4 more

Abstract read
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Yueting Xiong *State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, National Institute for Data Science in Health and Medicine, School of Public Health, Xiamen University, Fujian, China. ytxiong@xmu.edu.cn.ORCID http://orcid.org/0009-0008-8471-9800
Wenbin Jiang *State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, National Institute for Data Science in Health and Medicine, School of Public Health, Xiamen University, Fujian, China.ORCID http://orcid.org/0000-0003-2812-8519
Jin Xiao *State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, National Institute for Data Science in Health and Medicine, School of Public Health, Xiamen University, Fujian, China.
Qingfang BuState Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, National Institute for Data Science in Health and Medicine, School of Public Health, Xiamen University, Fujian, China.
Jingyi WangState Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, National Institute for Data Science in Health and Medicine, School of Public Health, Xiamen University, Fujian, China.
Zhenjian JiangDepartment of Pathology, Zhongshan Hospital, Fudan University (Xiamen Branch), Fujian, China.
Ling LuoState Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, National Institute for Data Science in Health and Medicine, School of Public Health, Xiamen University, Fujian, China.
Xiaoqing ChenState Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, National Institute for Data Science in Health and Medicine, School of Public Health, Xiamen University, Fujian, China.
Yijie QiuState Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, National Institute for Data Science in Health and Medicine, School of Public Health, Xiamen University, Fujian, China.
Yangtao WuState Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, National Institute for Data Science in Health and Medicine, School of Public Health, Xiamen University, Fujian, China.ORCID http://orcid.org/0000-0002-9429-266X
Fan LiuState Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, National Institute for Data Science in Health and Medicine, School of Public Health, Xiamen University, Fujian, China.
Rongshan YuState Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, National Institute for Data Science in Health and Medicine, School of Public Health, Xiamen University, Fujian, China. rsyu@xmu.edu.cn.ORCID http://orcid.org/0000-0003-2179-173X
Ningshao XiaState Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, National Institute for Data Science in Health and Medicine, School of Public Health, Xiamen University, Fujian, China. nsxia@xmu.edu.cn.ORCID http://orcid.org/0000-0003-0179-5266
Quan YuanState Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, National Institute for Data Science in Health and Medicine, School of Public Health, Xiamen University, Fujian, China. yuanquan@xmu.edu.cn.ORCID http://orcid.org/0000-0001-5487-561X

Funding

National Natural Science Foundation of China (National Science Foundation of China) 32401237Natural Science Foundation of Fujian Province (Fujian Provincial Natural Science Foundation) 2024J08358
6 · The paper itself

Abstract

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.

Indexed as

Antibodies, MonoclonalHigh-Throughput Nucleotide SequencingMass SpectrometrySequence Analysis, ProteinAnimalsAntibodies, NeutralizingCOVID-19HumansSARS-CoV-2Antibodies, MonoclonalAntibodies, Neutralizing

Identifiers

PMID41820386
PMCPMC13066000

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.