Engineering an AI-based forward-reverse platform for the design of cross-ribosome binding sites of a transcription factor biosensor

Ding, Nana; Zhang, Guangkun; Zhang, Linpei; Shen, Ziyun; Yin, Lianghong; Zhou, Shenghu; Deng, Yu

doi:10.1016/j.csbj.2023.04.026

Cited by 7 publications

(2 citation statements)

References 39 publications

(80 reference statements)

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“…While previous biosensors have been able to achieve the engineering of BenM towards AA to some success, the operating range of detection may be limited due to the uptake of the molecule, which can be ameliorated by the implementation of a dicarboxylic acid transporter as was done for PcaR in P. putida [23] , [24] , [25] . Targeting biosensor circuit components such as the ribosomal binding site for optimization could also be used to improve biosensor performance [16] , [48] . Alternatively, cell-free systems allow implementation of the biosensor outside of cellular contexts that may impact biosensor response.…”

Section: Discussionmentioning

confidence: 99%

Computation-guided transcription factor biosensor specificity engineering for adipic acid detection

Pham,

Stogios,

Savchenko

et al. 2024

Computational and Structural Biotechnology Journal

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Section: Discussionmentioning

confidence: 99%

Computation-guided transcription factor biosensor specificity engineering for adipic acid detection

Pham,

Stogios,

Savchenko

et al. 2024

Computational and Structural Biotechnology Journal

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“…Despite the fact that employing promoters of varying strengths is one of the most crucial methods for regulating gene expressions, the integration of diverse regulatory elements such as Ribosome Binding Sites (RBS), terminators, insulators, and others, offers expanded options for gene expression modulation (39–41). Furthermore, synthetic promoters, which establish a predetermined expression intensity, present challenges in dynamically regulating gene expression during growth.…”

Section: Discussionmentioning

confidence: 99%

Species-specific design of artificial promoters by transfer-learning based generative deep-learning model

Xia,

Du,

Liu

et al. 2023

Preprint

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Native prokaryotic promoters share common sequence patterns, but are species dependent. For understudied species with limited data, it is challenging to predict the strength of existing promoters and generate novel promoters. Here, we developed PromoGen, a collection of nucleotide language models to generate species-specific functional promoters, across dozens of species in a data and parameter efficient way. Twenty-seven species-specific models in this collection were finetuned from the pretrained model which was trained on multi-species promoters. When systematically compared with native promoters, theEscherichia coli-andBacillus subtilis-specific artificial PromoGen-generated promoters (PGPs) were demonstrated to hold all essential features and distribution patterns of native promoters. A regression model was developed to score generated either by PromoGen or by another competitive neural network, and the overall score of PGPs is higher. Encouraged byin silicoanalysis, we further experimentally characterized twenty-twoB. subtilisPGPsin vivo, results showed that four of tested PGPs reached the strong promoter level while all were active. Furthermore, we developed a user-friendly website to generate species-specific promoters for 27 different species by PromoGen. This work presented an efficient deep-learning strategy forde novospecies-specific promoter generation even with limited datasets, providing valuable promoter toolboxes especially for the metabolic engineering of understudied microorganisms.Abstract Figure

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SGBGAN: minority class image generation for class-imbalanced datasets

Wan,

Guo,

Wang

2024

Machine Vision and Applications

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Engineering an AI-based forward-reverse platform for the design of cross-ribosome binding sites of a transcription factor biosensor

Cited by 7 publications

References 39 publications

Computation-guided transcription factor biosensor specificity engineering for adipic acid detection

Computation-guided transcription factor biosensor specificity engineering for adipic acid detection

Species-specific design of artificial promoters by transfer-learning based generative deep-learning model

SGBGAN: minority class image generation for class-imbalanced datasets

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