Evidence map›Paper›PMID 40939890›Full record

ArticleJournal of advanced research2026

Design of strictly orthogonal biosensors for maximizing renewable biofuel overproduction.

Tong Wu, Dongli Yan, Sheng Lin, Ran Zhang, Yuhan Wang, Min Li, Shengzhu Yu, Xiaoyan Ma, Zhenya Chen, Yi-Xin Huo

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of advanced research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

10 authors.

Tong WuKey Laboratory of Molecular Medicine and Biotherapy, School of Life Science, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China; Tangshan Research Institute, BIT, No. 57, South Jianshe Road, Lubei District, Tangshan, Hebei 063000, China.
Dongli YanKey Laboratory of Molecular Medicine and Biotherapy, School of Life Science, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China.
Sheng LinBeijing Jingtai Technology Co., Ltd., Beijing 100083, China.
Ran ZhangKey Laboratory of Molecular Medicine and Biotherapy, School of Life Science, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China.
Yuhan WangKey Laboratory of Molecular Medicine and Biotherapy, School of Life Science, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China.
Min LiKey Laboratory of Molecular Medicine and Biotherapy, School of Life Science, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China.
Shengzhu YuKey Laboratory of Molecular Medicine and Biotherapy, School of Life Science, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China.
Xiaoyan MaKey Laboratory of Molecular Medicine and Biotherapy, School of Life Science, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China; Tangshan Research Institute, BIT, No. 57, South Jianshe Road, Lubei District, Tangshan, Hebei 063000, China.
Zhenya ChenKey Laboratory of Molecular Medicine and Biotherapy, School of Life Science, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China; Tangshan Research Institute, BIT, No. 57, South Jianshe Road, Lubei District, Tangshan, Hebei 063000, China. Electronic address: chenzhenya@bit.edu.cn.
Yi-Xin HuoKey Laboratory of Molecular Medicine and Biotherapy, School of Life Science, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China; Tangshan Research Institute, BIT, No. 57, South Jianshe Road, Lubei District, Tangshan, Hebei 063000, China. Electronic address: huoyixin@bit.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionTranscription factors (TFs) activate transcriptional initiation by binding specific signal molecules (SMs), yet designing TFs to precisely target non-natural SMs remains challenging.

objectivesUsing transcriptional activator BmoR as an example, a machine-learning based model named BT to predict three crucial residue regions (CRRs) was generated. This study aimed to achieve BmoR with strict SM orthogonality (SSO).

methodsRandom Forest Algorithm was used to generate a model BT that pinpointed CRRs. The BmoR-SM complexes in the prediction dataset of Model BT were batch-simulated using a computational pipeline via Discovery Studio. Semi-rational engineering of the residues in the CRRs generated BmoR mutants with SSO, validated through MicroScale Thermophoresis (MST) affinity assays. The SSO-enabled BmoR-based biosensor was used to screen microbial overproducers for 3-L fed-batch fermentation.

resultsThe transcription activation effects of 245 TF-SM complexes were experimentally verified, providing the training and test dataset to generate a machine-learning based Model BT with 88.5 % accuracy. The binding between 5,700 BmoR mutants and four SMs was simulated by Discovery Studio, generating 22,800 complexes to output BmoR-SM hydrogen bond (BSH) counts. BSH counts combined with supplementary parameters to form a prediction dataset. The CRRs containing totally 36 residues were successfully predicted by Model BT. The CRRs were semi-rational modified to obtain BmoR mutants with SSO. The SSO-enabled BmoR-based biosensor effectively screened a strain yielding 12.6 g/L isopentanol.

conclusionBy demonstrating the dominant role of the BSH counts in TF-SM interactions and establishing a machine learning-guided framework for TF evolution, this work advances rational design principles for engineering TFs with precise molecular recognition, offering broad applications in synthetic biology and metabolic engineering.

Indexed as

BiofuelsBiosensing TechniquesTranscription FactorsAlgorithmsEscherichia coliFermentationMachine LearningBiofuelsTranscription FactorsBiofuel overproductionHydrogen bondMachine learningStrict orthogonalityTranscription factor

Identifiers

PMID40939890
PMCPMC13227213

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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.