DNA binding site

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DNA contacts of different types of DNA-binding domains

DNA binding sites are a type of

restriction enzymes, site-specific recombinases (see site-specific recombination) and methyltransferases.[1]

DNA binding sites can be thus defined as short DNA sequences (typically 4 to 30 base pairs long, but up to 200 bp for recombination sites) that are specifically bound by one or more DNA-binding proteins or protein complexes. It has been reported that some binding sites have potential to undergo fast evolutionary change.[2]

Types of DNA binding sites

DNA binding sites can be categorized according to their biological function. Thus, we can distinguish between transcription factor-binding sites, restriction sites and recombination sites. Some authors have proposed that binding sites could also be classified according to their most convenient mode of representation.

consensus sequences, and they are typically represented using position specific frequency matrices (PSFM), which are often graphically depicted using sequence logos. This argument, however, is partly arbitrary. Restriction enzymes, like transcription factors, yield a gradual, though sharp, range of affinities for different sites [4] and are thus also best represented by PSFM. Likewise, site-specific recombinases also show a varied range of affinities for different target sites.[5][6]

History and main experimental techniques

The existence of something akin to DNA binding sites was suspected from the experiments on the biology of the

Microscale Thermophoresis[14]
is used.

Databases

Due to the diverse nature of the experimental techniques used in determining binding sites and to the patchy coverage of most organisms and transcription factors, there is no central database (akin to

false positive
rates are often associated with in-silico motif discovery / site search methods), there has been no systematic effort to computationally annotate these features in sequenced genomes.

There are, however, several private and public databases devoted to compilation of experimentally reported, and sometimes computationally predicted, binding sites for different transcription factors in different organisms. Below is a non-exhaustive table of available databases:

Name Organisms Source Access URL
PlantRegMap 165 plant species (e.g., Arabidopsis thaliana, Oryza sativa, Zea mays, etc.) Expert curation and projection Public [1]
JASPAR Vertebrates, Plants, Fungi, Flies, and Worms Expert curation with literature support Public [2]
CIS-BP All Eukaryotes Experimentally derived motifs and predictions Public [3]
CollecTF Prokaryotes Literature curation Public [4]
RegPrecise Prokaryotes Expert curation Public [5]
RegTransBase Prokaryotes Expert/literature curation Public [6]
RegulonDB Escherichia coli Expert curation Public [7] Archived 2017-05-07 at the Wayback Machine
PRODORIC Prokaryotes Expert curation Public [8] Archived 2007-05-16 at the Wayback Machine
TRANSFAC Mammals Expert/literature curation Public/Private [9] Archived 2008-10-23 at the Wayback Machine
TRED Human, Mouse, Rat Computer predictions, manual curation Public [10]
DBSD Drosophila species Literature/Expert curation Public [11]
HOCOMOCO Human, Mouse Literature/Expert curation Public [12],[13]
MethMotif Human, Mouse Expert curation Public [14] Archived 2019-10-29 at the Wayback Machine

Representation of DNA binding sites

A collection of DNA binding sites, typically referred to as a DNA binding motif, can be represented by a

Information Theory,[17] leading to its graphical representation as a sequence logo
.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
A 1 0 1 5 32 5 35 23 34 14 43 13 34 4 52 3
C 50 1 0 1 5 6 0 4 4 13 3 8 17 51 2 0
G 0 0 54 15 5 5 12 2 7 1 1 3 1 0 1 52
T 5 55 1 35 14 40 9 27 11 28 9 32 4 1 1 1
Sum 56 56 56 56 56 56 56 56 56 56 56 56 56 56 56 56

PSFM for the transcriptional repressor

LexA
as derived from 56 LexA-binding sites stored in Prodoric. Relative frequencies are obtained by dividing the counts in each cell by the total count (56)

Computational search and discovery of binding sites

In

artificial neural networks.[3][19][20] A plethora of algorithms is also available for sequence motif discovery. These methods rely on the hypothesis that a set of sequences share a binding motif for functional reasons. Binding motif discovery methods can be divided roughly into enumerative, deterministic and stochastic.[21] MEME[22] and Consensus [23] are classical examples of deterministic optimization, while the Gibbs sampler[24] is the conventional implementation of a purely stochastic method for DNA binding motif discovery. Another instance of this class of methods is SeSiMCMC[25] that is focused of weak TFBS sites with symmetry. While enumerative methods often resort to regular expression representation of binding sites, PSFM and their formal treatment under Information Theory methods are the representation of choice for both deterministic and stochastic methods. Hybrid methods, e.g. ChIPMunk[26] that combines greedy optimization with subsampling, also use PSFM. Recent advances in sequencing have led to the introduction of comparative genomics approaches to DNA binding motif discovery, as exemplified by PhyloGibbs.[27][28]

More complex methods for binding site search and motif discovery rely on the base stacking and other interactions between DNA bases, but due to the small sample sizes typically available for binding sites in DNA, their efficiency is still not completely harnessed. An example of such tool is the ULPB[29]

See also

References

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