Symphony of the DNA flexibility and sequence environment orchestrates p53 binding to its responsive elements

Vanaja, Akkinepally; Mallick, Sarada Prasanna; Kulandaivelu, Umasankar; Kumar, Aditya; Yella, Venkata Rajesh

doi:10.1016/j.gene.2021.145892

Cited by 4 publications

(4 citation statements)

References 72 publications

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“…Understanding the stability and base stacking energies of DNA is vital because they play a crucial role in maintaining the structural integrity and stability of the DNA molecule, influencing various biological processes such as DNA replication, transcription, and repair. ,,− ,− Figure indicates the DNA duplex stability and base stacking signatures of promoter sequences extracted from 12 diverse organisms. The promoter sequences of all 12 organisms were translated into energy values for seven parameters (Figure S1).…”

Section: Resultsmentioning

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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Section: Resultsmentioning

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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“…31 The pathophysiological studies of neurodegenerative diseases affecting the brain in higher primates like humans can be done by using G-quadruplex motifs and aids in designing new diagnostic ways. 32 Although previous research utilized several non-B DNA in eukaryotes, i-motif has not been considered exclusively [32][33][34] . We speculate that research on motifs could have several implications in future research.…”

Section: L-112mentioning

confidence: 99%

Dissecting G-Quadruplexes and I-Motifs in Cis-Regulatory Regions of Mammals and Plants

Renuka

Yella

2022

Int J Life Sci Pharm Res

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The evolutionary constraints of a gene consist of cis-gene regulatory regions, such as promoters and enhancers, which contribute to the regulation of gene expression. Several genomic and computational studies stated the roles of G-quadruplex and i-motif structures in vital cellular processes like transcription, translation, gene regulation, etc. The formation of these non-B DNA structures is supported by the occurrence of unique repeated sequences. However, many studies lean toward understanding the role of the G-quadruplex, and only recent studies indicated i-motif significance. In this study, we attempted to dissect the enrichment of G-quadruplex and i-motifs in promoter regions of mammals and plants. To this end, we employed the genomic sequences encompassing -500 to +500 region relative to the gene start positions in mammals and plants retrieved from the UCSC browser and Plant Genome database (PlantGDB). We computed the putative G-quadruplexes and i-motifs with well-recognized regular expression sequence patterns. We observed that G-quadruplex motifs showed preponderance in mammals, algal species, namely, green algae, and Chlamydomonas when compared to plants. Contrastingly i-motifs are enriched in both monocot and dicot plants compared to G-quadruplex motifs. The comparative examinations in this study revamp our understanding of the two quadruplex structures and their emerging functional roles in complex eukaryotes.

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“…DNA structural features were seen to correlate with functional noncoding regions of the human genome . The importance of DNA shape in protein DNA recognition has been highlighted in the literature. ,,, Several TFs and other DNA binding proteins have been shown to use shape readout for recognizing their target sequence. − ,,− Shapes of sequences flanking the core motif are also important for TF–DNA affinity. − Structural features of sequences farther than immediate flanks also contribute to the motif environment and influence DNA target search by TFs …”

Section: Introductionmentioning

confidence: 99%

Discerning the Role of DNA Sequence, Shape, and Flexibility in Recognition by Drosophila Transcription Factors

Murthy,

Dey,

Olymon

et al. 2024

ACS Chem. Biol.

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The precise spatial and temporal orchestration of gene expression is crucial for the ontogeny of an organism and is mainly governed by transcription factors (TFs). The mechanism of recognition of cognate sites amid millions of base pairs in the genome by TFs is still incompletely understood. In this study, we focus on DNA sequence composition, shape, and flexibility preferences of 28 quintessential TFs from Drosophila melanogaster that are critical to development and body patterning mechanisms. Our study finds that TFs exhibit distinct predilections for DNA shape, flexibility, and sequence compositions in the proximity of transcription factor binding sites (TFBSs). Notably, certain zinc finger proteins prefer GC-rich areas with less negative propeller twist, while homeodomains mainly seek AT-rich regions with a more negative propeller twist at their sites. Intriguingly, while numerous cofactors share similar binding site preferences and bind closer to each other in the genome, some cofactors that have different preferences bind farther apart. These findings shed light on TF DNA recognition and provide novel insights into possible cofactor binding and transcriptional regulation mechanisms.

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Symphony of the DNA flexibility and sequence environment orchestrates p53 binding to its responsive elements

Cited by 4 publications

References 72 publications

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

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

Dissecting G-Quadruplexes and I-Motifs in Cis-Regulatory Regions of Mammals and Plants

Discerning the Role of DNA Sequence, Shape, and Flexibility in Recognition by Drosophila Transcription Factors

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