Sequence motif
In biology, a sequence motif is a nucleotide or amino-acid sequence pattern that is widespread and usually assumed to be related to biological function of the macromolecule. For example, an "N"-glycosylation site motif can be defined as "Asn, followed by anything but Pro, followed by either Ser or Thr, followed by anything but Pro residue". 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: This pattern may be written as codice_1 where codice_2 = Asn, codice_3 = Pro, codice_4 = Ser, codice_5 =  Thr; codice_6 means any amino acid except codice_7; and codice_8 means either codice_7 or codice_10. The notation codice_8 does not give any indication of the probability of codice_7 or codice_10 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 codice_14 means codice_7 or codice_10 or codice_17, 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: where codice_19 signifies any amino acid, and the square brackets indicate an alternative (see below for further details about notation). Usually, however, the first letter is codice_20, and both codice_21 choices resolve to codice_22. Since the last choice is so wide, the pattern codice_23 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 codice_28 matches the six amino acid sequences corresponding to codice_29, codice_30, codice_31, codice_32, codice_33, and codice_34. 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, 'codice_35', 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: Some examples: The signature of the C2H2-type "zinc finger" domain is: 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. Motif Discovery. Overview. The sequence motif discovery process has been well-developed since the 1990s. In particular, most of  the existing motif discovery research focuses on DNA motifs. With the advances in high-throughput sequencing, such motif discovery problems are challenged by both the sequence pattern degeneracy issues and the data-intensive computational scalability issues. Section::::"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. There are more than 100 publications detailing motif discovery algorithms; Weirauch "et al". evaluated many related algorithms in a 2013 benchmark. The planted motif search  is another motif discovery method that is based on combinatorial approach. Phylogenetic motif discovery. 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 in 1996. It spans about 150 amino acid residues, and begins as follows: Here each codice_69 signifies a single amino acid or a gap, and each codice_70 indicates one member of a closely related family  of amino acids. The authors were able to show that the motif has DNA binding activity. A similar approach is commonly used by modern protein domain databases such as Pfam: human curators would select a pool of sequences known to be related and use computer programs to align them and produce the motif profile, which can be used to identify other related proteins. A phylogenic approach can also be used to enhance the "de novo" MEME algorithm, with PhyloGibbs being an example. Section::::"De novo" motif pair discovery. In 2017, MotifHyades has been developed as a motif discovery tool that can  be directly applied to paired sequences. Section::::"De novo" motif recognition from protein. In 2018, a Markov random field approach has been proposed to infer DNA motifs from DNA-binding domains of proteins. Motif Cases. Three-dimensional chain codes. The "E. coli" lactose operon repressor LacI ( chain A) and "E. coli" catabolite gene activator ( 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. In 1997, Matsuda, "et al." devised a code they called the "three-dimensional chain code" for representing the protein structure as a  string of letters. This encoding scheme reveals the similarity between the proteins much more clearly than the amino acid sequence (example from article): The code encodes the torsion angles between alpha-carbons of the protein backbone. "W" always corresponds to an alpha helix.