Advances in the study of protein–DNA interaction

Cai, Yuhang; Huang, He

doi:10.1007/s00726-012-1377-9

Cited by 38 publications

(20 citation statements)

References 58 publications

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“…While great strides have been made toward systematic mapping of physical protein-protein and protein-DNA interaction networks (Cai & Huang, 2012;Syafrizayanti et al, 2014;Myers et al, 2015;Smits & Vermeulen, 2016), systematic mapping of protein-metabolite interactions is lagging behind. One challenge is the generally low affinity (mM range) of protein-metabolite interactions (Reznik et al, 2017) and their fleeting nature.…”

Section: Introductionmentioning

confidence: 99%

Systematic mapping of protein‐metabolite interactions in central metabolism of Escherichia coli

Diether

Nikolaev

Allain

et al. 2019

Molecular Systems Biology

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Metabolite binding to proteins regulates nearly all cellular processes, but our knowledge of these interactions originates primarily from empirical in vitro studies. Here, we report the first systematic study of interactions between water‐soluble proteins and polar metabolites in an entire biological subnetwork. To test the depth of our current knowledge, we chose to investigate the well‐characterized Escherichia coli central metabolism. Using ligand‐detected NMR , we assayed 29 enzymes towards binding events with 55 intracellular metabolites. Focusing on high‐confidence interactions at a false‐positive rate of 5%, we detected 98 interactions, among which purine nucleotides accounted for one‐third, while 50% of all metabolites did not interact with any enzyme. In contrast, only five enzymes did not exhibit any metabolite binding and some interacted with up to 11 metabolites. About 40% of the interacting metabolites were predicted to be allosteric effectors based on low chemical similarity to their target's reactants. For five of the eight tested interactions, in vitro assays confirmed novel regulatory functions, including ATP and GTP inhibition of the first pentose phosphate pathway enzyme. With 76 new candidate regulatory interactions that have not been reported previously, we essentially doubled the number of known interactions, indicating that the presently available information about protein–metabolite interactions may only be the tip of the iceberg.

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Section: Introductionmentioning

confidence: 99%

Systematic mapping of protein‐metabolite interactions in central metabolism of Escherichia coli

Diether

Nikolaev

Allain

et al. 2019

Molecular Systems Biology

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“…Position weight matrices (PWMs) use the log-likelihood scoring function for computing a match score for potential binding sites and therefore have been reported to be better measure than the consensus sequence [89], [90]. However, it is still challenging for PWM based predictive methods to distinguish functional TFBS from non-functional predictions without applying additional refinements such as cross-species conservation [91], [92], [93]. Functional studies on understanding the role of conserved genomic regions from species to species have shown positional conservation to be one of the key biological characteristics of the DNA-motifs in a regulatory context [94], [95], [96].…”

Section: Introductionmentioning

confidence: 99%

Genome-Wide Computational Prediction and Analysis of Core Promoter Elements across Plant Monocots and Dicots

Kumari

Ware

2013

PLoS ONE

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Transcription initiation, essential to gene expression regulation, involves recruitment of basal transcription factors to the core promoter elements (CPEs). The distribution of currently known CPEs across plant genomes is largely unknown. This is the first large scale genome-wide report on the computational prediction of CPEs across eight plant genomes to help better understand the transcription initiation complex assembly. The distribution of thirteen known CPEs across four monocots (Brachypodium distachyon, Oryza sativa ssp. japonica, Sorghum bicolor, Zea mays) and four dicots (Arabidopsis thaliana, Populus trichocarpa, Vitis vinifera, Glycine max) reveals the structural organization of the core promoter in relation to the TATA-box as well as with respect to other CPEs. The distribution of known CPE motifs with respect to transcription start site (TSS) exhibited positional conservation within monocots and dicots with slight differences across all eight genomes. Further, a more refined subset of annotated genes based on orthologs of the model monocot (O. sativa ssp. japonica) and dicot (A. thaliana) genomes supported the positional distribution of these thirteen known CPEs. DNA free energy profiles provided evidence that the structural properties of promoter regions are distinctly different from that of the non-regulatory genome sequence. It also showed that monocot core promoters have lower DNA free energy than dicot core promoters. The comparison of monocot and dicot promoter sequences highlights both the similarities and differences in the core promoter architecture irrespective of the species-specific nucleotide bias. This study will be useful for future work related to genome annotation projects and can inspire research efforts aimed to better understand regulatory mechanisms of transcription.

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“…Nucleic acids are polyanions and can bind theoretically to any positively charged proteins at an acidic pH through electrostatic interactions. Interaction of nucleic acids with proteins can influence their stability and function both for RNAs and DNAs . For example, the well‐known DNA‐binding proteins, or transcription factors bind to DNA complexes at regulatory loci to regulate expression of a target gene .…”

Section: Drug Substance Manufacturingmentioning

confidence: 99%