Sequence motif

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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).

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

Overview[edit]

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 bioinformatics[edit]

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[edit]

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.

De novo computational discovery of motifs[edit]

There are software programs which, given multiple input sequences, attempt to identify one or more candidate motifs. One example is MEME, which generates statistical information for each candidate. Other algorithms include AlignAce, Amadeus, CisModule, FIRE, Gibbs Motif Sampler, PhyloGibbs, SeSiMCMC, ChIPMunk and Weeder. SCOPE, MotifVoter, and MProfiler [1] are ensemble motif finders that uses several algorithms simultaneously. The planted motif search is another motif discovery method that is based on combinatorial approach. There currently exist more than 100 publications with similar algorithms; Weirauch et al. evaluated many related algorithms in a 2013 benchmark.[2]

Discovery through evolutionary conservation[edit]

Motifs have been discovered by 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[3] and others discovered a pattern which they called the GCM motif. 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[4][5] and the Gibbs Motif Sampler[6][7] are motif discovery algorithms that consider phylogenetic conservation.

Pattern description notations[edit]

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[edit]

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[edit]

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.

Another scheme[edit]

The following example comes from the paper by Matsuda, et al. 1997.[8]

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.[8] 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.

See also[edit]

References[edit]

  1. ^ Doaa Altarawy, M. A. Ismail, and Sahar Ghanem (2009). "MProfiler: A Profile-Based Method for DNA Motif Discovery". Pattern Recognition in Bioinformatics 5780: 13–23. doi:10.1007/978-3-642-04031-3_2. 
  2. ^ Weirauch et al. (2009). "Evaluation of methods for modeling transcription factor sequence specificity". Nature biotechnology 31: 126–134. doi:10.1038/nbt.2486. 
  3. ^ 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 26236. PMID 8962155. 
  4. ^ Siddharthan R, van Nimwegen E, Siggia ED (2004). "PhyloGibbs: A Gibbs sampler incorporating phylogenetic information". In Eskin E, Workman C (eds), RECOMB 2004 Satellite Workshop on Regulatory Genomics, LNBI 3318, 3041 (Springer-Verlag Berlin Heidelberg 2005). 
  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 1309704. PMID 16477324. 
  6. ^ Lawrence, Charles E.; Altschul, Stephen F.; Boguski, Mark S.; Liu, Jun S.; Neuwald, Andrew F.; Wootton, John C. (8 October 1993). "Detecting subtle sequence signals: a Gibbs sampling strategy for multiple alignment". Science 262 (5131): 208–214. doi:10.1126/science.8211139. PMID 8211139. 
  7. ^ Newberg, Lee A.; Thompson, William A.; Conlan, Sean; Smith, Thomas M.; McCue, Lee Ann; Lawrence, Charles E. (15 July 2007). "A phylogenetic Gibbs sampler that yields centroid solutions for cis regulatory site prediction". Bioinformatics 23 (14): 1718–1727. doi:10.1093/bioinformatics/btm241. PMC 2268014. PMID 17488758. 
  8. ^ a b Matsuda H, Taniguchi F, Hashimoto A (1997). "An approach to detection of protein structural motifs using an encoding scheme of backbone conformations". Proc. of 2nd Pacific Symposium on Biocomputing: 280–291. 

Further reading[edit]

External links[edit]

Motif-finding methods and databases[edit]

Motif-finding Web applications[edit]

  • BLOCK-maker — finds conserved blocks in a group of two or more unaligned protein sequences
  • ChIPMunk — is a fast heuristic DNA motif digger based on greedy approach accompanied by bootstrapping
  • ELM — functional site prediction of short linear motifs
  • FIRE — finds DNA and RNA motifs from expression data using the mutual information
  • Gibbs Motif Sampler — discovers overrepresented conserved motifs in an aligned set of orthologous sequences
  • GIMSAN — motif-finder with biologically realistic and reliable statistical significance analysis
  • Improbizer — searches for motifs in DNA or RNA sequences that occur with improbable frequency
  • MEME Suite — discover motifs (highly conserved regions) in groups of related DNA or protein sequences
  • Minimotif Miner — public interface to the minimotif miner database which correlates short sequence amino acids to their biological function
  • ModuleMaster — allows to search for motifs by pre-defined or custom PWMs
  • MotifVoter — variance based ensemble method for discovery of binding sites
  • PhyloGibbs — discovers overrepresented conserved motifs in an aligned set of orthologous sequences
  • PLACE — database of plant cis-acting regulatory DNA elements
  • PMS or [2] — free online motif discovery tools for searching DNA and RNA overrepresented conserved motifs
  • RSATde novo detection of regulatory signals in non-coding sequences
  • SCOPE — an ensemble of programs aimed at identifying novel cis-regulatory elements from groups of upstream sequences
  • SeSiMCMC — algorithm finds DNA motifs of unknown length and complicated structure, such as direct repeats or palindromes with variable spacers in the middle in a set of unaligned DNA sequences
  • TEIRESIS — search for short sequence motifs in Proteins
  • WebMotifs — use different programs to search for DNA-sequence motifs, and to easily combine and evaluate the results
  • XXmotif web server for eXhaustive, weight matriX-based motif discovery in nucleotide sequences

Motif visualization and browsing[edit]

  • MochiView — a genome browser supporting import of motif libraries and containing tools for motif discovery, visualization, and analysis
  • Seq2Logo — a sequence logo generator for construction and visualization of amino acid binding motifs and sequence profiles, including features for sequence weighting, pseudo counts and two-sided representation of amino acid enrichment and depletion