Evidence map›Paper›PMID 41988819›Full record

ArticleMicrobial biotechnology2026

Deep Learning-Enabled Engineering of a Hyper-Stable and Soluble MPB70-83 Antigen for Sensitive Bovine Tuberculosis Surveillance.

Wen-Hao Wang, Jia-Zhen Ge, Ying-Ying Xie, Guo-Dong Song, Yijian Liu, Yin-Juan Song, Ren-Ge Li, Rui-Shuang Li, Zi-Qing Wang, Xin-Miao Liu and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Microbial biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

16 authors.

Wen-Hao WangState Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou University, Lanzhou, China.
Jia-Zhen GeState Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou University, Lanzhou, China.
Ying-Ying XieState Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou University, Lanzhou, China.
Guo-Dong SongState Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou University, Lanzhou, China.
Yijian LiuState Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou University, Lanzhou, China.
Yin-Juan SongState Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou University, Lanzhou, China.
Ren-Ge LiState Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou University, Lanzhou, China.
Rui-Shuang LiState Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou University, Lanzhou, China.
Zi-Qing WangState Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou University, Lanzhou, China.
Xin-Miao LiuState Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou University, Lanzhou, China.
Shuang-Shuang GuoState Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou University, Lanzhou, China.
Jie LiCollege of Animal Science and Technology, Shihezi University, Shihezi, China.
Shengli ChenState Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou University, Lanzhou, China.
Na LiCollege of Animal Medicine, Xinjiang Agricultural University, Urumqi, China.
Fuying ZhengState Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou University, Lanzhou, China.
Yuefeng ChuState Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou University, Lanzhou, China.

Funding

Innovation Program of the Chinese Academy of Agricultural Sciences CAAS-ZDRW-202410Joint Scientific Research Fund Major Project of Gansu Province 25JRRA1085Key Research and Development Program of Xinjiang 2025B02022Major Science and Technology Project of Gansu Province 23ZDNA007Major Science and Technology Project of Gansu Province 24ZD13NA008
6 · The paper itself

Abstract

Bovine tuberculosis (bTB) diagnostics are frequently hindered by the structural instability and insolubility of recombinant multi-epitope proteins in prokaryotic expression systems. To address this, we developed an integrated strategy combining immunoinformatics with the ProteinMPNN deep learning framework. We constructed a fusion protein utilizing B-cell epitopes from MPB70 and MPB83, where ProteinMPNN was employed to redesign non-epitope scaffolds for optimized thermodynamic stability. The structural integrity was verified via 200-ns molecular dynamics (MD) simulations. MD analysis revealed the redesigned construct transitions into a rigid, compact native state with a stable hydrophobic core. Validating this in silico design, the optimized protein achieved high-yield soluble expression in Escherichia coli, eliminating inclusion body formation. The resulting indirect ELISA demonstrated 96.30% sensitivity and 98.61% specificity, showing excellent concordance (Cohen's κ > 0.9) with standard assays. This study demonstrates that deep learning-based sequence redesign effectively resolves solubility bottlenecks in antigen engineering, providing a robust, scalable tool for precision bTB surveillance.

Indexed as

Antigens, BacterialBacterial ProteinsDeep LearningMembrane ProteinsProtein EngineeringTuberculosis, BovineAnimalsCattleEnzyme-Linked Immunosorbent AssayEpitopes, B-LymphocyteEscherichia coliImmunoinformaticsMolecular Dynamics SimulationRecombinant Fusion ProteinsSensitivity and SpecificitySolubilityAntigens, BacterialBacterial ProteinsEpitopes, B-LymphocyteMembrane ProteinsMPB83 protein, MycobacteriumRecombinant Fusion Proteinsbovine tuberculosis diagnosismolecular dynamics simulationmulti‐epitope fusion proteinMycobacterium bovisProteinMPNNsoluble expression

Identifiers

PMID41988819
PMCPMC13084532

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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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 OpenQuestion graph.