DNA structural and physical properties reveal peculiarities in promoter sequences of the bacterium Escherichia coli K-12

Martinez, Gustavo Sganzerla; Silva, Scheila de Ávila e; Kumar, Aditya; Pérez‐Rueda, Ernesto

doi:10.1007/s42452-021-04713-2

Cited by 5 publications

(8 citation statements)

References 34 publications

(71 reference statements)

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“…Many factors such as the diversity of archaea and their relatively recent discovery creates the need for high quality genome annotation. This is the moment when in-silico approaches provide help to experimental biology by curating data [ 20 ]. The boxplots portrayed in Fig.…”

Section: Resultsmentioning

confidence: 99%

Machine learning and statistics shape a novel path in archaeal promoter annotation

et al. 2022

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Background Archaea are a vast and unexplored domain. Bioinformatic techniques might enlighten the path to a higher quality genome annotation in varied organisms. Promoter sequences of archaea have the action of a plethora of proteins upon it. The conservation found in a structural level of the binding site of proteins such as TBP, TFB, and TFE aids RNAP-DNA stabilization and makes the archaeal promoter prone to be explored by statistical and machine learning techniques. Results and discussions In this study, experimentally verified promoter sequences of the organisms Haloferax volcanii, Sulfolobus solfataricus, and Thermococcus kodakarensis were converted into DNA duplex stability attributes (i.e. numerical variables) and were classified through Artificial Neural Networks and an in-house statistical method of classification, being tested with three forms of controls. The recognition of these promoters enabled its use to validate unannotated promoter sequences in other organisms. As a result, the binding site of basal transcription factors was located through a DNA duplex stability codification. Additionally, the classification presented satisfactory results (above 90%) among varied levels of control. Concluding remarks The classification models were employed to perform genomic annotation into the archaea Aciduliprofundum boonei and Thermofilum pendens, from which potential promoters have been identified and uploaded into public repositories.

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Section: Resultsmentioning

confidence: 99%

Machine learning and statistics shape a novel path in archaeal promoter annotation

et al. 2022

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“…There are several physico-chemical conformities upon a DNA sequence that can be represented in numbers 26 . Some of these features were found to converge, conveying the level of contained information 3 , 27 . In this work, we have selected DNA Duplex Stability (DDS), which has widely been used as a way of representing genetic information 8 , 13 , 17 , 19 , 28 as it is dependent on the primary sequence reflected by the number of hydrogen bonds keeping them linked.…”

Section: Methodsmentioning

confidence: 99%

Explainable artificial intelligence as a reliable annotator of archaeal promoter regions

Martinez

Pérez‐Rueda

Kumar

et al. 2023

Sci Rep

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Archaea are a vast and unexplored cellular domain that thrive in a high diversity of environments, having central roles in processes mediating global carbon and nutrient fluxes. For these organisms to balance their metabolism, the appropriate regulation of their gene expression is essential. A key momentum in regulating genes responsible for the life maintenance of archaea is when transcription factor proteins bind to the promoter element. This DNA segment is conserved, which enables its exploration by machine learning techniques. Here, we trained and tested a support vector machine with 3935 known archaeal promoter sequences. All promoter sequences were coded into DNA Duplex Stability. After, we performed a model interpretation task to map the decision pattern of the classification procedure. We also used a dataset of known-promoter sequences for validation. Our results showed that an AT rich region around position − 27 upstream (relative to the start TSS) is the most conserved in the analyzed organisms. In addition, we were able to identify the BRE element (− 33), the PPE (at − 10) and a position at + 3, that provides a more understandable picture of how promoters are organized in all the archaeal organisms. Finally, we used the interpreted model to identify potential promoter sequences of 135 unannotated organisms, delivering regulatory regions annotation of archaea in a scale never accomplished before (https://pcyt.unam.mx/gene-regulation/). We consider that this approach will be useful to understand how gene regulation is achieved in other organisms apart from the already established transcription factor binding sites.

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“…Promoter sequences differ structurally from nonpromoter regions, and the parametrization of DNA information into structural attributes can reveal unique characteristics of promoters . DNA structural features, such as stability, bendability, curvature, and base stacking energy, can be used to predict different promoter sequences in transcriptomes with high accuracy. ,− DNA stability refers to the ability of the DNA double helix to maintain its structure and resist separation of its complementary strands. , It is influenced by factors like base pairing between complementary strands and stacking interactions between adjacent bases. , The base stacking energy refers to the energy associated with the interaction between adjacent bases in a DNA or RNA molecule, crucial for maintaining the helix stability and structure. , Base pairing drives the formation of double-stranded DNA, and the stacking stabilizes the structure .…”

Section: Introductionmentioning

confidence: 99%

“…8,9 Promoter sequences differ structurally from nonpromoter regions, and the parametrization of DNA information into structural attributes can reveal unique characteristics of promoters. 6 DNA structural features, such as stability, bendability, curvature, and base stacking energy, can be used to predict different promoter sequences in transcriptomes with high accuracy. 4,10−12 DNA stability refers to the ability of the DNA double helix to maintain its structure and resist separation of its complementary strands.…”

Section: ■ Introductionmentioning

confidence: 99%

“…The regulation of gene expression is an intricate process governed by multiple factors and mechanisms, including DNA-dependent RNA polymerase, sigma factors, transcription factors, and promoter sequences . Promoters are the crucial DNA sequences, that serve as docking sites for the transcriptional machinery initiating gene transcription. − Prokaryotic promoters show conserved sequences, such as the presence of consensus motif TATAAT, often referred to as the Pribnow box or −10 motifs, correspondingly associated with σ70, located upstream of the transcription start site (TSS). − They also act as transcription factor binding sites that play a role in DNA-TF recognition, and DNA structural features influence binding affinity. , …”

Section: Introductionmentioning

confidence: 99%

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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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DNA structural and physical properties reveal peculiarities in promoter sequences of the bacterium Escherichia coli K-12

Cited by 5 publications

References 34 publications

Machine learning and statistics shape a novel path in archaeal promoter annotation

Machine learning and statistics shape a novel path in archaeal promoter annotation

Explainable artificial intelligence as a reliable annotator of archaeal promoter regions

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

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