Dataset Information

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

ABSTRACT: A cross-ribosome binding site (cRBS) adjusts the dynamic range of transcription factor-based biosensors (TFBs) by controlling protein expression and folding. The rational design of a cRBS with desired TFB dynamic range remains an important issue in TFB forward and reverse engineering. Here, we report a novel artificial intelligence (AI)-based forward-reverse engineering platform for TFB dynamic range prediction and de novo cRBS design with selected TFB dynamic ranges. The platform demonstrated superior in processing unbalanced minority-class datasets and was guided by sequence characteristics from trained cRBSs. The platform identified correlations between cRBSs and dynamic ranges to mimic bidirectional design between these factors based on Wasserstein generative adversarial network (GAN) with a gradient penalty (GP) (WGAN-GP) and balancing GAN with GP (BAGAN-GP). For forward and reverse engineering, the predictive accuracy was up to 98% and 82%, respectively. Collectively, we generated an AI-based method for the rational design of TFBs with desired dynamic ranges.

SUBMITTER: Ding N

PROVIDER: S-EPMC10781712 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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

Ding Nana N Zhang Guangkun G Zhang LinPei L Shen Ziyun Z Yin Lianghong L Zhou Shenghu S Deng Yu Y

Computational and structural biotechnology journal 20230428

A cross-ribosome binding site (cRBS) adjusts the dynamic range of transcription factor-based biosensors (TFBs) by controlling protein expression and folding. The rational design of a cRBS with desired TFB dynamic range remains an important issue in TFB forward and reverse engineering. Here, we report a novel artificial intelligence (AI)-based forward-reverse engineering platform for TFB dynamic range prediction and <i>de novo</i> cRBS design with selected TFB dynamic ranges. The platform demonst ...[more]

PMID: 38213883

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Project description:BackgroundCucumber (Cucumis sativus) belongs to the Cucurbitaceae family that includes more than 800 species. The cucumber genome has been recently sequenced and annotated. Transcriptomics and genome sequencing of many plant genomes are providing information on candidate genes potentially related to agronomically important traits. To accelerate functional characterization of these genes in cucumber we have generated an EMS mutant population that can be used as a TILLinG platform for reverse genetics.Principal findingsA population of 3,331 M2 mutant seed families was generated using two EMS concentrations (0.5% and 0.75%). Genomic DNA was extracted from M2 families and eight-fold pooled for mutation detection by ENDO1 nuclease. To assess the quality of the mutant collection, we screened for induced mutations in five genes and identified 26 mutations. The average mutation rate was calculated as 1/1147 Kb giving rise to approximately 320 mutations per genome. We focused our characterization on three missense mutations, G33C, S238F and S249F identified in the CsACS2 sex determination gene. Protein modeling and crystallography studies predicted that mutation at G33 may affect the protein function, whereas mutations at S238 and S249 may not impair the protein function. As predicted, detailed phenotypic evaluation showed that the S238F and the S249F mutant lines had no sexual phenotype. In contrast, plants homozygous for the G33C mutation showed a complete sexual transition from monoecy to andromonoecy. This result demonstrates that TILLinG is a valuable tool for functional validation of gene function in crops recalcitrant to transgenic transformation.ConclusionsWe have developed a cucumber mutant population that can be used as an efficient reverse genetics tool. The cucumber TILLinG collection as well as the previously described melon TILLinG collection will prove to be a valuable resource for both fundamental research and the identification of agronomically-important genes for crop improvement in cucurbits in general.

Dataset Information

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

Publications

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

Similar Datasets

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