Sigma70Pred: A highly accurate method for predicting sigma70 promoter in Escherichia coli K-12 strains

Patiyal, Sumeet; Singh, Nitindeep; Ali, Mohd. Zartab; Pundir, Dhawal Singh; Raghava, Gajendra P. S.

doi:10.3389/fmicb.2022.1042127

Cited by 8 publications

(7 citation statements)

References 36 publications

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“…A σ70 promoter should harbor two well-defined short DNA elements that are situated at about 10 bps and 35 bps upstream of the TSS (the −10 element and the −35 element, respectively) [19]. The consensus sequences are TTGACA (for −35) and TATAAT (for −10), and the distances between these two elements are usually 17 to 18 bps [20]. Based on these features, we proposed two short sequences, "TTGtgA" (−96 to −91 relative to the fucA start codon; consensus nucleotides are capitalized) and "aATtAa" (−73 to −68) upstream of the TSS, to be the −35 element and the −10 element, respectively (Figure 4B).…”

Section: Determination Of the Transcriptional Start Site (Tss) For Th...mentioning

confidence: 99%

Comprehensive Characterization of fucAO Operon Activation in Escherichia coli

Zhang,

Huo,

Velo

et al. 2024

IJMS

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Wildtype Escherichia coli cells cannot grow on L-1,2-propanediol, as the fucAO operon within the fucose (fuc) regulon is thought to be silent in the absence of L-fucose. Little information is available concerning the transcriptional regulation of this operon. Here, we first confirm that fucAO operon expression is highly inducible by fucose and is primarily attributable to the upstream operon promoter, while the fucO promoter within the 3′-end of fucA is weak and uninducible. Using 5′RACE, we identify the actual transcriptional start site (TSS) of the main fucAO operon promoter, refuting the originally proposed TSS. Several lines of evidence are provided showing that the fucAO locus is within a transcriptionally repressed region on the chromosome. Operon activation is dependent on FucR and Crp but not SrsR. Two Crp-cAMP binding sites previously found in the regulatory region are validated, where the upstream site plays a more critical role than the downstream site in operon activation. Furthermore, two FucR binding sites are identified, where the downstream site near the first Crp site is more important than the upstream site. Operon transcription relies on Crp-cAMP to a greater degree than on FucR. Our data strongly suggest that FucR mainly functions to facilitate the binding of Crp to its upstream site, which in turn activates the fucAO promoter by efficiently recruiting RNA polymerase.

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Section: Determination Of the Transcriptional Start Site (Tss) For Th...mentioning

confidence: 99%

Comprehensive Characterization of fucAO Operon Activation in Escherichia coli

Zhang,

Huo,

Velo

et al. 2024

IJMS

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“…An 81-nucleotide long sequence containing the end of SO_1320 and the first eight nucleotides of yadS were used as the query. (Lin et al, 2014;Patiyal et al, 2022). Amino acid sequences of WT and mutant versions of SO_3758 were aligned to the sodiumdependent bicarbonate transporter SbtA from cyanobacteria Synechocystis sp.…”

Section: Bioinformaticsmentioning

confidence: 99%

A small number of point mutations confer formate tolerance inShewanella oneidensis

Cross,

Aboulnaga,

TerAvest

2024

Preprint

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Microbial electrosynthesis (MES) is a sustainable approach to chemical production from CO2and clean electricity. However, limitations in electron transfer efficiency and gaps in understanding of electron transfer pathways in MES systems prevent full realization of this technology.Shewanella oneidensiscould serve as an MES biocatalyst because it has a well-studied, efficient transmembrane electron transfer pathway. A key first step in MES in this organism could be CO2reduction to formate. However, wild-typeS. oneidensisdoes not tolerate high levels of formate. In this work, we created and characterized formate-tolerant strains ofS. oneidensisfor further engineering and future use in MES systems through adaptive laboratory evolution. Two different point mutations in a sodium-dependent bicarbonate transporter and a DUF2721-contianing protein separately confer formate tolerance toS. oneidensis. The mutations were further evaluated to understand their role in improving formate tolerance. We also show that the wild-type and mutant versions of theS. oneidensissodium-dependent bicarbonate transporter improves formate tolerance ofZymomonas mobilis, indicating the potential for the transfer of this formate tolerance phenotype to other organisms.ImportanceShewanella oneidensisis a bacterium with a well-studied, efficient extracellular electron transfer pathway. This capability could make this organism a suitable host for microbial electrosynthesis using CO2or formate feedstocks. However, formate is toxic toS. oneidensis, limiting the potential for its use in these systems. In this work, we evolve several strains ofS. oneidensisthat have improved formate tolerance and we investigate some mutations that confer this phenotype. The phenotype is confirmed to be attributed to several single point mutations by transferring the wild- type and mutant versions of each gene to the unmutated strain. Finally, the formate tolerance mechanism of one mutation is studied using structural modeling and expression in another host. This study therefore presents a simple method for conferring formate tolerance to bacterial hosts.

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“…19,20 Multiple machine learning and deep learning methods have been developed for bacterial promoter prediction, including iPromoter-2L, 21 G4PromFinder, 22 iPro70-FMWin, 23 MUL-TiPLy, 24 iProEP, 25 SELECTOR, 26 CNNProm, 26 iPromoter-BnCNN, 27 Promotech, 28 iPro-GAN, 29 PromoterLCNN, 30 iPromoter-CLA, 31 iPro-WAEL, 32 and Sigma70Pred. 33 These methods employ various algorithms and feature encoding schemes to identify promoters and their specific types in different bacterial species. On the other side, bioinformatic tools that utilize the structural architecture of promoters, such as PromPredict, 34 BTSS Finder, 35 and BacPP, 36 leverage structural architecture of DNA and have shown great promise in distinguishing promoters from nonpromoter regions.…”

Section: ■ Introductionmentioning

confidence: 99%

“…However, alternative approaches that consider structural properties hold promise for this task, as they provide discernible biological signals in the context of transcription initiation mechanisms. Machine learning techniques utilizing DNA duplex stability and base stacking energy of promoter, intergenic and transcribed sequences can be compared to demonstrate the unique features of the core promoter providing a high-scale annotation of these essential genetic regions. , Multiple machine learning and deep learning methods have been developed for bacterial promoter prediction, including iPromoter-2L, G4PromFinder, iPro70-FMWin, MUL-TiPLy, iProEP, SELECTOR, CNNProm, iPromoter-BnCNN, Promotech, iPro-GAN, PromoterLCNN, iPromoter-CLA, iPro-WAEL, and Sigma70Pred . These methods employ various algorithms and feature encoding schemes to identify promoters and their specific types in different bacterial species.…”

Section: Introductionmentioning

confidence: 99%

MLDSPP: Bacterial Promoter Prediction Tool Using DNA Structural Properties with Machine Learning and Explainable AI

Paul,

Olymon,

Martinez

et al. 2024

J. Chem. Inf. Model.

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Bacterial promoters play a crucial role in gene expression by serving as docking sites for the transcription initiation machinery. However, accurately identifying promoter regions in bacterial genomes remains a challenge due to their diverse architecture and variations. In this study, we propose MLDSPP (Machine Learning and Duplex Stability based Promoter prediction in Prokaryotes), a machine learning-based promoter prediction tool, to comprehensively screen bacterial promoter regions in 12 diverse genomes. We leveraged biologically relevant and informative DNA structural properties, such as DNA duplex stability and base stacking, and state-of-the-art machine learning (ML) strategies to gain insights into promoter characteristics. We evaluated several machine learning models, including Support Vector Machines, Random Forests, and XGBoost, and assessed their performance using accuracy, precision, recall, specificity, F1 score, and MCC metrics. Our findings reveal that XGBoost outperformed other models and current state-of-the-art promoter prediction tools, namely Sigma70pred and iPromoter2L, achieving F1-scores >95% in most systems. Significantly, the use of one-hot encoding for representing nucleotide sequences complements these structural features, enhancing our XGBoost model's predictive capabilities. To address the challenge of model interpretability, we incorporated explainable AI techniques using Shapley values. This enhancement allows for a better understanding and interpretation of the predictions of our model. In conclusion, our study presents MLDSPP as a novel, generic tool for predicting promoter regions in bacteria, utilizing original downstream sequences as nonpromoter controls. This tool has the potential to significantly advance the field of bacterial genomics and contribute to our understanding of gene regulation in diverse bacterial systems.

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Sigma70Pred: A highly accurate method for predicting sigma70 promoter in Escherichia coli K-12 strains

Cited by 8 publications

References 36 publications

Comprehensive Characterization of fucAO Operon Activation in Escherichia coli

Comprehensive Characterization of fucAO Operon Activation in Escherichia coli

A small number of point mutations confer formate tolerance inShewanella oneidensis

MLDSPP: Bacterial Promoter Prediction Tool Using DNA Structural Properties with Machine Learning and Explainable AI

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