“Sequence motif”的意思、由来-开放百科全书

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.

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.

Motif Representation

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.

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:

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

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:

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.

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

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 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:

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:

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. ^¹{{cite journal |author1=Matsuda H |author2=Taniguchi F |author3=Hashimoto A |year=1997 |title=An approach to detection of protein structural motifs using an encoding scheme of backbone conformations |journal=Proc. Of 2nd Pacific Symposium on Biocomputing |pages=280–291 |url=http://helix-web.stanford.edu/psb97/matsuda.pdf}}
2. ^{{cite journal |vauthors=Bailey TL, Williams N, Misleh C, Li WW |title=MEME: discovering and analyzing DNA and protein sequence motifs |journal=Nucleic Acids Res |year=2006 |volume=34 |issue=Web Server issue |pages=W369–373 |pmid=16845028 |doi=10.1093/nar/gkl198 |pmc=1538909}}
3. ^{{cite journal |author=Weirauch|year=2013 |title=Evaluation of methods for modeling transcription factor sequence specificity |journal=Nature Biotechnology |volume=31 |pages=126–134 |doi=10.1038/nbt.2486 |pmid=23354101 |pmc=3687085 |issue=2|display-authors=etal}}
4. ^{{cite journal |author1=Akiyama Y |author2=Hosoya T |author3=Poole AM |author4=Hotta Y |year=1996 |title=The gcm-motif: a novel DNA-binding motif conserved in Drosophila and mammals |journal=Proc. Natl. Acad. Sci. U.S.A. |volume=93 |issue=25 |pages=14912–14916 |pmid=8962155 |doi=10.1073/pnas.93.25.14912 |pmc=26236}}
5. ^{{cite journal |author1=Siddharthan R |author2=Siggia ED |author3=van Nimwegen E |title=PhyloGibbs: A Gibbs sampling motif finder that incorporates phylogeny |journal=PLoS Comput Biol |volume=1 |issue=7 |pages=e67 |year=2005 |doi=10.1371/journal.pcbi.0010067 |pmid=16477324 |pmc=1309704}}
6. ^Ka-Chun Wong; [https://doi.org/10.1093/bioinformatics/btx381 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