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Motif: CRC_binding (RM00004)

Description: CRC binding motif

Summary

Wikipedia annotation Edit Wikipedia article

The Rfam group coordinates the annotation of Rfam data in Wikipedia. This motif is described by a Wikipedia entry entitled Sequence motif. More...

A DNA sequence motif represented as a sequence logo for the LexA-binding motif.

In genetics, a sequence motif is a nucleotide or amino-acid sequence pattern that is widespread and has, or is conjectured to have, a biological significance. For proteins, a sequence motif is distinguished from a structural motif, a motif formed by the three-dimensional arrangement of amino acids which may not be adjacent.

An example is the N-glycosylation site motif:

Asn, followed by anything but Pro, followed by either Ser or Thr, followed by anything but Pro

where the three-letter abbreviations are the conventional designations for amino acids (see genetic code).

Overview

When a sequence motif appears in the exon of a gene, it may encode the "structural motif" of a protein; that is a stereotypical element of the overall structure of the protein. Nevertheless, motifs need not be associated with a distinctive secondary structure. "Noncoding" sequences are not translated into proteins, and nucleic acids with such motifs need not deviate from the typical shape (e.g. the "B-form" DNA double helix).

Outside of gene exons, there exist regulatory sequence motifs and motifs within the "junk", such as satellite DNA. Some of these are believed to affect the shape of nucleic acids (see for example RNA self-splicing), but this is only sometimes the case. For example, many DNA binding proteins that have affinity for specific DNA binding sites bind DNA in only its double-helical form. They are able to recognize motifs through contact with the double helix's major or minor groove.

Short coding motifs, which appear to lack secondary structure, include those that label proteins for delivery to particular parts of a cell, or mark them for phosphorylation.

Within a sequence or database of sequences, researchers search and find motifs using computer-based techniques of sequence analysis, such as BLAST. Such techniques belong to the discipline of bioinformatics.

See also consensus sequence.

Motif Representation

Consider the N-glycosylation site motif mentioned above:

Asn, followed by anything but Pro, followed by either Ser or Thr, followed by anything but Pro

This pattern may be written as N{P}[ST]{P} where N = Asn, P = Pro, S = Ser, T = Thr; {X} means any amino acid except X; and [XY] means either X or Y.

The notation [XY] does not give any indication of the probability of X or Y occurring in the pattern. Observed probabilities can be graphically represented using sequence logos. Sometimes patterns are defined in terms of a probabilistic model such as a hidden Markov model.

Motifs and consensus sequences

The notation [XYZ] means X or Y or Z, but does not indicate the likelihood of any particular match. For this reason, two or more patterns are often associated with a single motif: the defining pattern, and various typical patterns.

For example, the defining sequence for the IQ motif may be taken to be:

[FILV]Qxxx[RK]Gxxx[RK]xx[FILVWY]

where x signifies any amino acid, and the square brackets indicate an alternative (see below for further details about notation).

Usually, however, the first letter is I, and both [RK] choices resolve to R. Since the last choice is so wide, the pattern IQxxxRGxxxR is sometimes equated with the IQ motif itself, but a more accurate description would be a consensus sequence for the IQ motif.

Pattern description notations

Several notations for describing motifs are in use but most of them are variants of standard notations for regular expressions and use these conventions:

  • there is an alphabet of single characters, each denoting a specific amino acid or a set of amino acids;
  • a string of characters drawn from the alphabet denotes a sequence of the corresponding amino acids;
  • any string of characters drawn from the alphabet enclosed in square brackets matches any one of the corresponding amino acids; e.g. [abc] matches any of the amino acids represented by a or b or c.

The fundamental idea behind all these notations is the matching principle, which assigns a meaning to a sequence of elements of the pattern notation:

a sequence of elements of the pattern notation matches a sequence of amino acids if and only if the latter sequence can be partitioned into subsequences in such a way that each pattern element matches the corresponding subsequence in turn.

Thus the pattern [AB] [CDE] F matches the six amino acid sequences corresponding to ACF, ADF, AEF, BCF, BDF, and BEF.

Different pattern description notations have other ways of forming pattern elements. One of these notations is the PROSITE notation, described in the following subsection.

PROSITE pattern notation

The PROSITE notation uses the IUPAC one-letter codes and conforms to the above description with the exception that a concatenation symbol, '-', is used between pattern elements, but it is often dropped between letters of the pattern alphabet.

PROSITE allows the following pattern elements in addition to those described previously:

  • The lower case letter 'x' can be used as a pattern element to denote any amino acid.
  • A string of characters drawn from the alphabet and enclosed in braces (curly brackets) denotes any amino acid except for those in the string. For example, {ST} denotes any amino acid other than S or T.
  • If a pattern is restricted to the N-terminal of a sequence, the pattern is prefixed with '<'.
  • If a pattern is restricted to the C-terminal of a sequence, the pattern is suffixed with '>'.
  • The character '>' can also occur inside a terminating square bracket pattern, so that S[T>] matches both "ST" and "S>".
  • If e is a pattern element, and m and n are two decimal integers with m <= n, then:
    • e(m) is equivalent to the repetition of e exactly m times;
    • e(m,n) is equivalent to the repetition of e exactly k times for any integer k satisfying: m <= k <= n.

Some examples:

  • x(3) is equivalent to x-x-x.
  • x(2,4) matches any sequence that matches x-x or x-x-x or x-x-x-x.

The signature of the C2H2-type zinc finger domain is:

  • C-x(2,4)-C-x(3)-[LIVMFYWC]-x(8)-H-x(3,5)-H

Matrices

A matrix of numbers containing scores for each residue or nucleotide at each position of a fixed-length motif. There are two types of weight matrices.

  • A position frequency matrix (PFM) records the position-dependent frequency of each residue or nucleotide. PFMs can be experimentally determined from SELEX experiments or computationally discovered by tools such as MEME using hidden Markov models.
  • A position weight matrix (PWM) contains log odds weights for computing a match score. A cutoff is needed to specify whether an input sequence matches the motif or not. PWMs are calculated from PFMs.

An example of a PFM from the TRANSFAC database for the transcription factor AP-1:

Pos A C G T IUPAC
01 6 2 8 1 R
02 3 5 9 0 S
03 0 0 0 17 T
04 0 0 17 0 G
05 17 0 0 0 A
06 0 16 0 1 C
07 3 2 3 9 T
08 4 7 2 4 N
09 9 6 1 1 M
10 4 3 7 3 N
11 6 3 1 7 W

The first column specifies the position, the second column contains the number of occurrences of A at that position, the third column contains the number of occurrences of C at that position, the fourth column contains the number of occurrences of G at that position, the fifth column contains the number of occurrences of T at that position, and the last column contains the IUPAC notation for that position. Note that the sums of occurrences for A, C, G, and T for each row should be equal because the PFM is derived from aggregating several consensus sequences.

Encoding scheme

The following example comes from the paper by Matsuda, et al. 1997.[1]

The E. coli lactose operon repressor LacI (PDB: 1lcc​ chain A) and E. coli catabolite gene activator (PDB: 3gap​ chain A) both have a helix-turn-helix motif, but their amino acid sequences do not show much similarity, as shown in the table below.

Matsuda, et al.[1] devised a code they called the "three-dimensional chain code" for representing a protein structure as a string of letters. This encoding scheme reveals the similarity between the proteins much more clearly than the amino acid sequence:

3D chain code Amino acid sequence
1lccA TWWWWWWWKCLKWWWWWWG LYDVAEYAGVSYQTVSRVV
3gapA KWWWWWWGKCFKWWWWWWW RQEIGQIVGCSRETVGRIL

where "W" corresponds to an α-helix, and "E" and "D" correspond to a β-strand.

Computational discovery of motifs

De novo motif discovery

There are software programs which, given multiple input sequences, attempt to identify one or more candidate motifs. One example is the Multiple EM for Motif Elicitation (MEME) algorithm, which generates statistical information for each candidate.[2] There are more than 100 publications detailing motif discovery algorithms; Weirauch et al. evaluated many related algorithms in a 2013 benchmark.[3] The planted motif search is another motif discovery method that is based on combinatorial approach.

Discovery through evolutionary conservation

Motifs have also been discovered by taking a phylogenetic approach and studying similar genes in different species. For example, by aligning the amino acid sequences specified by the GCM (glial cells missing) gene in man, mouse and D. melanogaster, Akiyama and others discovered a pattern which they called the GCM motif.[4] It spans about 150 amino acid residues, and begins as follows:

WDIND*.*P..*...D.F.*W***.**.IYS**...A.*H*S*WAMRNTNNHN

Here each . signifies a single amino acid or a gap, and each * indicates one member of a closely related family of amino acids.

The authors were able to show that the motif has DNA binding activity. PhyloGibbs[5] is an example of a motif discovery algorithm that considers phylogenetic conservation.

De novo motif pair discovery

In 2017, MotifHyades has been developed as a motif discovery tool that can be directly applied to paired sequences [6].

See also

References

  1. ^ a b Matsuda H; Taniguchi F; Hashimoto A (1997). "An approach to detection of protein structural motifs using an encoding scheme of backbone conformations" (PDF). Proc. of 2nd Pacific Symposium on Biocomputing: 280–291. 
  2. ^ Bailey TL, Williams N, Misleh C, Li WW (2006). "MEME: discovering and analyzing DNA and protein sequence motifs". Nucleic Acids Res. 34 (Web Server issue): W369–373. doi:10.1093/nar/gkl198. PMC 1538909Freely accessible. PMID 16845028. 
  3. ^ Weirauch; et al. (2013). "Evaluation of methods for modeling transcription factor sequence specificity". Nature Biotechnology. 31 (2): 126–134. doi:10.1038/nbt.2486. 
  4. ^ Akiyama Y; Hosoya T; Poole AM; Hotta Y (1996). "The gcm-motif: a novel DNA-binding motif conserved in Drosophila and mammals". Proc. Natl. Acad. Sci. U.S.A. 93 (25): 14912–14916. doi:10.1073/pnas.93.25.14912. PMC 26236Freely accessible. PMID 8962155. 
  5. ^ Siddharthan R; Siggia ED; van Nimwegen E (2005). "PhyloGibbs: A Gibbs sampling motif finder that incorporates phylogeny". PLoS Comput Biol. 1 (7): e67. doi:10.1371/journal.pcbi.0010067. PMC 1309704Freely accessible. PMID 16477324. 
  6. ^ Ka-Chun Wong; MotifHyades: expectation maximization for de novo DNA motif pair discovery on paired sequences, Bioinformatics, Volume 33, Issue 19, 1 October 2017, Pages 3028–3035, https://doi.org/10.1093/bioinformatics/btx381

Further reading

This page is based on a wikipedia article. The text is available under the Creative Commons Attribution/Share-Alike License.

Alignments

You can either download the motif alignment or view it directly in your browser window. More...

Formatting options

You can view or download motif alignments in several formats. Check either the "download" button, to save the formatted alignment, or "view", to see it in your browser window, and click "Generate".

Alignment format:
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Family matches

There are 115 Rfam families which match this motif.

This section shows the families which have been annotated with this motif. Users should be aware that the motifs are structural constructs and do not necessarily conform to taxonomic boundaries in the way that Rfam families do. More...

Original order Family Accession Family Description Number of Hits Fraction of Hits Sum of Bits Image
3 RF00011 Bacterial RNase P class B 12 0.105 109.2 Match Image
3 RF00023 transfer-messenger RNA 59 0.124 630.6 Match Image
3 RF00024 Vertebrate telomerase RNA 5 0.135 48.2 Match Image
3 RF00028 Group I catalytic intron 3 0.250 28.1 Match Image
3 RF00053 mir-7 microRNA precursor 10 0.175 98.8 Match Image
3 RF00082 SraG RNA 2 0.286 25.4 Match Image
3 RF00114 Ribosomal S15 leader 7 0.090 54.2 Match Image
3 RF00125 IS128 RNA 5 1.000 46.3 Match Image
3 RF00150 Small nucleolar RNA SNORD42 3 0.429 24.1 Match Image
3 RF00151 Small nucleolar RNA SNORD58 2 0.061 23.8 Match Image
3 RF00168 Lysine riboswitch 4 0.085 39.2 Match Image
3 RF00199 SL2 RNA 8 0.267 68.8 Match Image
3 RF00225 Tobamovirus internal ribosome entry site (IRES) 4 0.571 52.0 Match Image
3 RF00230 T-box leader 6 0.125 60.3 Match Image
3 RF00234 glmS glucosamine-6-phosphate activated ribozyme 2 0.111 21.0 Match Image
3 RF00278 Small nucleolar RNA SNORD50 3 0.115 28.3 Match Image
3 RF00303 Small nucleolar RNA snoR86 2 0.182 19.9 Match Image
3 RF00368 sroB RNA 5 0.312 50.4 Match Image
3 RF00623 Pseudomonas sRNA P1 3 0.214 23.4 Match Image
3 RF00659 microRNA mir-365 4 0.444 36.4 Match Image
3 RF00677 microRNA MIR168 2 0.200 19.0 Match Image
3 RF00736 microRNA mir-320 7 0.127 65.0 Match Image
3 RF00749 microRNA mir-208 11 0.647 173.1 Match Image
3 RF00782 microRNA MIR480 8 0.116 79.4 Match Image
3 RF00859 microRNA mir-234 2 0.500 20.8 Match Image
3 RF00937 microRNA mir-653 5 0.417 49.0 Match Image
3 RF01036 microRNA mir-567 3 0.333 23.1 Match Image
3 RF01050 Saccharomyces telomerase 9 0.692 87.3 Match Image
3 RF01181 Small nucleolar RNA snR77 15 1.000 211.3 Match Image
3 RF01203 Small nucleolar RNA snR47 10 0.588 150.2 Match Image
3 RF01214 Small nucleolar RNA snR51 3 0.176 25.6 Match Image
3 RF01241 Small nucleolar RNA SNORA81 18 0.643 189.9 Match Image
3 RF01265 Small nucleolar RNA snR42 2 0.400 23.0 Match Image
3 RF01272 Small nucleolar RNA snR86 2 0.400 21.4 Match Image
3 RF01296 Small nucleolar RNA U85 10 0.476 100.4 Match Image
3 RF01387 isrC Hfq binding RNA 3 0.188 29.6 Match Image
3 RF01402 STnc150 Hfq binding RNA 9 1.000 207.3 Match Image
3 RF01408 sraL Hfq binding RNA 2 0.333 18.4 Match Image
3 RF01410 BsrC 2 0.069 27.0 Match Image
3 RF01412 BsrG 2 0.333 27.6 Match Image
3 RF01419 Antisense RNA which regulates isiA expression 31 0.101 318.4 Match Image
3 RF01459 Listeria sRNA rliE 3 0.750 26.8 Match Image
3 RF01461 Listeria sRNA rli22 6 1.000 58.5 Match Image
3 RF01471 Listeria sRNA rliB 2 0.500 28.2 Match Image
3 RF01473 Listeria sRNA rli41 6 1.000 65.5 Match Image
3 RF01477 Listeria sRNA rli43 8 2.000 72.7 Match Image
3 RF01496 A. fumigatus sRNA Afu_182 2 0.105 16.6 Match Image
3 RF01577 Plasmodium RNase_P 6 3.000 56.4 Match Image
3 RF01592 small nucleolar RNA snoR17 3 0.600 32.7 Match Image
3 RF01603 small nucleolar RNA snoR29 4 1.333 30.4 Match Image
3 RF01606 small nucleolar RNA snoR31 3 1.000 27.9 Match Image
3 RF01675 Pseudomonas sRNA CrcZ 19 1.000 1922.9 Match Image
3 RF01692 Bacteroidete tryptophan peptide leader RNA 13 0.371 163.3 Match Image
3 RF01699 Clostridiales-1 RNA 31 0.160 259.3 Match Image
3 RF01742 lactis-plasmid RNA 14 1.000 228.8 Match Image
3 RF01766 cspA thermoregulator 2 0.133 17.5 Match Image
3 RF01808 MicX Vibrio cholerae sRNA 6 0.600 97.2 Match Image
3 RF01824 RNA of unknown function 20 5 0.833 77.2 Match Image
3 RF01825 RNA of unknown function 21 5 1.000 74.6 Match Image
3 RF01830 Salmonella enterica Typhi npcRNA 44 4 0.118 38.8 Match Image
3 RF01859 Phenylalanine leader peptide 38 0.535 517.0 Match Image
3 RF01865 Manganese dependent ribozyme in Vg1 mRNA 2 0.500 19.7 Match Image
3 RF01871 Metastasis associated lung adenocarcinoma transcript 1 5 0.294 61.1 Match Image
3 RF01873 Polled intersex syndrome regulated transcript 1 12 0.667 99.6 Match Image
3 RF01888 Embryonic ventral forebrain RNA 1 conserved region 2 14 0.737 144.3 Match Image
3 RF01931 Bithoraxoid conserved region 3 4 1.000 49.8 Match Image
3 RF01960 Eukaryotic small subunit ribosomal RNA 37 0.407 468.6 Match Image
3 RF01997 microRNA mir-969 4 0.500 31.4 Match Image
3 RF02029 sraA 6 0.300 44.8 Match Image
3 RF02032 Giant, ornate, lake- and Lactobacillales-derived (GOLLD) RNA 4 0.114 36.6 Match Image
3 RF02036 IMES-3 RNA motif 31 0.969 266.2 Match Image
3 RF02039 SPRY4-IT1 conserved region 2 10 0.417 100.3 Match Image
3 RF02042 HOXA transcript at the distal tip, conserved region 3 15 0.652 140.9 Match Image
3 RF02045 CDKN2B antisense RNA 1 convserved region 3 4 0.222 38.1 Match Image
3 RF02057 Salmonella enterica sRNA STnc40 6 0.353 66.9 Match Image
3 RF02075 Enterobacterial sRNA STnc230 6 0.545 73.4 Match Image
3 RF02076 Gammaproteobacterial sRNA STnc100 3 0.125 27.9 Match Image
3 RF02080 Salmonella enterica sRNA STnc170 5 1.667 54.7 Match Image
3 RF02081 Enterobacterial sRNA STnc550 6 0.545 57.9 Match Image
3 RF02101 Highly up-regulated in liver cancer conserved region 7 0.368 82.3 Match Image
3 RF02103 Deleted in lymphocytic leukemia 1 conserved region 1 3 0.115 29.2 Match Image
3 RF02112 DLG2 antisense RNA 1 conserved region 1 5 0.238 47.8 Match Image
3 RF02132 HOXB13 antisense RNA 1 conserved region 1 7 0.583 73.2 Match Image
3 RF02142 HOXA11 antisense RNA 1 conserved region 6 21 1.000 312.5 Match Image
3 RF02154 Non-protein coding RNA, upstream of F2R/PAR1 conserved region 1 5 0.556 42.2 Match Image
3 RF02157 NPPA antisense RNA 1 conserved region 2 12 0.750 123.6 Match Image
3 RF02158 NPPA antisense RNA 1 conserved region 3 4 0.167 36.5 Match Image
3 RF02159 Prostate androgen-regulated transcript 1 conserved region 1 6 0.273 62.7 Match Image
3 RF02160 Prostate androgen-regulated transcript 1 conserved region 2 8 0.276 88.6 Match Image
3 RF02170 Pvt1 oncogene conserved region 7 3 0.375 23.3 Match Image
3 RF02179 ST7 antisense RNA 1 conserved region 1 2 0.080 24.1 Match Image
3 RF02183 ST7 overlapping transcript 3 conserved region 1 3 0.107 25.7 Match Image
3 RF02187 ST7 overlapping transcript 4 conserved region 1 2 0.083 21.0 Match Image
3 RF02192 T-cell leukemia/lymphoma 6 conserved region 2 7 0.583 96.7 Match Image
3 RF02194 Bacterial antisense RNA HPnc0260 8 0.258 71.8 Match Image
3 RF02199 TTC28 antisense RNA 1 conserved region 2 3 0.375 25.0 Match Image
3 RF02212 ZFAT antisense RNA 1 conserved region 2 6 0.545 79.4 Match Image
3 RF02376 SR1 sRNA 2 0.333 26.2 Match Image
3 RF02415 Listeria sRNA rliG 3 0.600 38.6 Match Image
3 RF02422 Burkholderia sRNA Bp1_Cand738_SIPHT 9 0.429 75.6 Match Image
3 RF02446 Streptococcus sRNA SpR18 18 0.783 149.2 Match Image
3 RF02447 Streptococcus sRNA SpR19 2 0.087 21.0 Match Image
3 RF02449 Bacillus sRNA ncr1015 3 0.188 28.5 Match Image
3 RF02530 Ure2 internal ribosome entry site (IRES) 5 1.000 91.6 Match Image
3 RF02540 Archaeal large subunit ribosomal RNA 61 0.670 525.0 Match Image
3 RF02541 Bacterial large subunit ribosomal RNA 31 0.304 385.9 Match Image
3 RF02542 Microsporidia small subunit ribosomal RNA 8 0.174 86.0 Match Image
3 RF02543 Eukaryotic large subunit ribosomal RNA 25 0.284 401.9 Match Image
3 RF02544 Mitochondrion-encoded tmRNA 4 0.364 36.8 Match Image
3 RF02545 Trypanosomatid mitochondrial small subunit ribosomal RNA 4 1.000 79.9 Match Image
3 RF02575 Drosophila melanogaster stable intronic sequence RNA 1 3 1.000 28.8 Match Image
3 RF02737 Soft rot Enterobacteriaceae Rev 13 asRNA 3 1.000 53.5 Match Image
3 RF02738 Soft rot Enterobacteriaceae Rev 24 asRNA 2 0.500 22.7 Match Image
3 RF02742 Soft rot Enterobacteriaceae Rev 72 asRNA 3 1.000 34.7 Match Image
3 RF02745 Soft rot Enterobacteriaceae Rev 42 5'UTR 3 1.000 37.5 Match Image

References

This section shows the database cross-references that we have for this Rfam motif.

Literature references

  1. Sonnleitner E, Abdou L, Haas D Proc Natl Acad Sci U S A. 2009;106:21866-21871. Small RNA as global regulator of carbon catabolite repression in Pseudomonas aeruginosa. PUBMED:20080802

External database links

Curation and motif details

This section shows the detailed information about the Rfam motif. We're happy to receive updated or improved alignments for new or existing families. Submit your new alignment and we'll take a look.

Curation

Seed source Gardner PP
Structure source N/A
Type Specific Recognition Motif
Author Gardner PP
Alignment details
Alignment Number of
sequences
Average length Sequence
identity (%)
seed 85 14.79 73

Model information

Build commands
cmbuild -F CM SEED
cmcalibrate --mpi --seed 1 CM
Gathering cutoff 12.0
Covariance model Download the Infernal CM for the motif here