Sequence motif

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A DNA sequence motif represented as a sequence logo for the LexA-binding motif. LexA gram positive bacteria sequence logo.png
A DNA sequence motif represented as a sequence logo for the LexA-binding 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.



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:


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:

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


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:


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


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.

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. [1] There are more than 100 publications detailing motif discovery algorithms; Weirauch et al. evaluated many related algorithms in a 2013 benchmark. [2] 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. [3] 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.

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. [4]

De novo motif pair discovery

In 2017, MotifHyades has been developed as a motif discovery tool that can be directly applied to paired sequences. [5]

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. [6]

Motif Cases

Three-dimensional chain codes

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. 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): [7] The code encodes the torsion angles between alpha-carbons of the protein backbone. "W" always corresponds to an alpha helix.

3D chain codeAmino acid sequence

See also

Related Research Articles

Base pair Unit consisting of two nucleobases bound to each other by hydrogen bonds

A base pair (bp) is a fundamental unit of double-stranded nucleic acids consisting of two nucleobases bound to each other by hydrogen bonds. They form the building blocks of the DNA double helix and contribute to the folded structure of both DNA and RNA. Dictated by specific hydrogen bonding patterns, "Watson–Crick" base pairs allow the DNA helix to maintain a regular helical structure that is subtly dependent on its nucleotide sequence. The complementary nature of this based-paired structure provides a redundant copy of the genetic information encoded within each strand of DNA. The regular structure and data redundancy provided by the DNA double helix make DNA well suited to the storage of genetic information, while base-pairing between DNA and incoming nucleotides provides the mechanism through which DNA polymerase replicates DNA and RNA polymerase transcribes DNA into RNA. Many DNA-binding proteins can recognize specific base-pairing patterns that identify particular regulatory regions of genes.

Zinc finger Small structural protein motif found mostly in transcriptional proteins

A zinc finger is a small protein structural motif that is characterized by the coordination of one or more zinc ions (Zn2+) in order to stabilize the fold. It was originally coined to describe the finger-like appearance of a hypothesized structure from the African clawed frog (Xenopus laevis) transcription factor IIIA. However, it has been found to encompass a wide variety of differing protein structures in eukaryotic cells. Xenopus laevis TFIIIA was originally demonstrated to contain zinc and require the metal for function in 1983, the first such reported zinc requirement for a gene regulatory protein followed soon thereafter by the Krüppel factor in Drosophila. It often appears as a metal-binding domain in multi-domain proteins.

Protein structure prediction Type of biological prediction

Protein structure prediction is the inference of the three-dimensional structure of a protein from its amino acid sequence—that is, the prediction of its secondary and tertiary structure from primary structure. Structure prediction is different from the inverse problem of protein design. Protein structure prediction is one of the most important goals pursued by computational biology; and it is important in medicine and biotechnology.

Nucleic acid sequence Succession of nucleotides in a nucleic acid

A nucleic acid sequence is a succession of bases signified by a series of a set of five different letters that indicate the order of nucleotides forming alleles within a DNA or RNA (GACU) molecule. By convention, sequences are usually presented from the 5' end to the 3' end. For DNA, the sense strand is used. Because nucleic acids are normally linear (unbranched) polymers, specifying the sequence is equivalent to defining the covalent structure of the entire molecule. For this reason, the nucleic acid sequence is also termed the primary structure.

In a chain-like biological molecule, such as a protein or nucleic acid, a structural motif is a common three-dimensional structure that appears in a variety of different, evolutionarily unrelated molecules. A structural motif does not have to be associated with a sequence motif; it can be represented by different and completely unrelated sequences in different proteins or RNA.

Structural bioinformatics Bioinformatics subfield

Structural bioinformatics is the branch of bioinformatics that is related to the analysis and prediction of the three-dimensional structure of biological macromolecules such as proteins, RNA, and DNA. It deals with generalizations about macromolecular 3D structures such as comparisons of overall folds and local motifs, principles of molecular folding, evolution, binding interactions, and structure/function relationships, working both from experimentally solved structures and from computational models. The term structural has the same meaning as in structural biology, and structural bioinformatics can be seen as a part of computational structural biology. The main objective of structural bioinformatics is the creation of new methods of analysing and manipulating biological macromolecular data in order to solve problems in biology and generate new knowledge.

In molecular biology and bioinformatics, the consensus sequence is the calculated order of most frequent residues, either nucleotide or amino acid, found at each position in a sequence alignment. It serves as a simplified representation of the viral population. It represents the results of multiple sequence alignments in which related sequences are compared to each other and similar sequence motifs are calculated. Such information is important when considering sequence-dependent enzymes such as RNA polymerase.

Sequence logo

In bioinformatics, a sequence logo is a graphical representation of the sequence conservation of nucleotides or amino acids . A sequence logo is created from a collection of aligned sequences and depicts the consensus sequence and diversity of the sequences. Sequence logos are frequently used to depict sequence characteristics such as protein-binding sites in DNA or functional units in proteins.

Helix-turn-helix Structural motif capable of binding DNA

In proteins, the helix-turn-helix (HTH) is a major structural motif capable of binding DNA. Each monomer incorporates two α helices, joined by a short strand of amino acids, that bind to the major groove of DNA. The HTH motif occurs in many proteins that regulate gene expression. It should not be confused with the helix–loop–helix motif.

Leucine zipper DNA-binding structural motif

A leucine zipper is a common three-dimensional structural motif in proteins. They were first described by Landschulz and collaborators in 1988 when they found that an enhancer binding protein had a very characteristic 30-amino acid segment and the display of these amino acid sequences on an idealized alpha helix revealed a periodic repetition of leucine residues at every seventh position over a distance covering eight helical turns. The polypeptide segments containing these periodic arrays of leucine residues were proposed to exist in an alpha-helical conformation and the leucine side chains from one alpha helix interdigitate with those from the alpha helix of a second polypeptide, facilitating dimerization.

A DNA-binding domain (DBD) is an independently folded protein domain that contains at least one structural motif that recognizes double- or single-stranded DNA. A DBD can recognize a specific DNA sequence or have a general affinity to DNA. Some DNA-binding domains may also include nucleic acids in their folded structure.

A position weight matrix (PWM), also known as a position-specific weight matrix (PSWM) or position-specific scoring matrix (PSSM), is a commonly used representation of motifs (patterns) in biological sequences.

SV40 large T antigen

SV40 large T antigen is a hexamer protein that is a dominant-acting oncoprotein derived from the polyomavirus SV40. TAg is capable of inducing malignant transformation of a variety of cell types. The transforming activity of TAg is due in large part to its perturbation of the retinoblastoma (pRb) and p53 tumor suppressor proteins. In addition, TAg binds to several other cellular factors, including the transcriptional co-activators p300 and CBP, which may contribute to its transformation function.


PROSITE is a protein database. It consists of entries describing the protein families, domains and functional sites as well as amino acid patterns and profiles in them. These are manually curated by a team of the Swiss Institute of Bioinformatics and tightly integrated into Swiss-Prot protein annotation. PROSITE was created in 1988 by Amos Bairoch, who directed the group for more than 20 years. Since July 2018, the director of PROSITE and Swiss-Prot is Alan Bridge.

The Walker A and Walker B motifs are protein sequence motifs, known to have highly conserved three-dimensional structures. These were first reported in ATP-binding proteins by Walker and co-workers in 1982.

GCM transcription factors Protein family

In molecular biology, the GCM transcription factors are a family of proteins which contain a GCM motif. The GCM motif is a domain that has been identified in proteins belonging to a family of transcriptional regulators involved in fundamental developmental processes which comprise Drosophila melanogaster GCM and its mammalian homologues. In GCM transcription factors the N-terminal moiety contains a DNA-binding domain of 150 amino acids. Sequence conservation is highest in this GCM domain. In contrast, the C-terminal moiety contains one or two transactivating regions and is only poorly conserved.

The MEME suite is a collection of tools for the discovery and analysis of sequence motifs.

Gary Stormo American geneticist (born 1950)

Gary Stormo is an American geneticist and currently Joseph Erlanger Professor in the Department of Genetics and the Center for Genome Sciences and Systems Biology at Washington University School of Medicine in St Louis. He is considered one of the pioneers of bioinformatics and genomics. His research combines experimental and computational approaches in order to identify and predict regulatory sequences in DNA and RNA, and their contributions to the regulatory networks that control gene expression.

PSI-blast based secondary structure PREDiction (PSIPRED) is a method used to investigate protein structure. It uses artificial neural network machine learning methods in its algorithm. It is a server-side program, featuring a website serving as a front-end interface, which can predict a protein's secondary structure from the primary sequence.

Protein tandem repeats

An array of protein tandem repeats is defined as several adjacent copies having the same or similar sequence motifs. These periodic sequences are generated by internal duplications in both coding and non-coding genomic sequences. Repetitive units of protein tandem repeats are considerably diverse, ranging from the repetition of a single amino acid to domains of 100 or more residues.


Secondary and tertiary sources

Primary sources

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  2. Weirauch MT, Cote A, Norel R, Annala M, Zhao Y, Riley TR, et al. (February 2013). "Evaluation of methods for modeling transcription factor sequence specificity". Nature Biotechnology. 31 (2): 126–34. doi:10.1038/nbt.2486. PMC   3687085 . PMID   23354101.
  3. Akiyama Y, Hosoya T, Poole AM, Hotta Y (December 1996). "The gcm-motif: a novel DNA-binding motif conserved in Drosophila and mammals". Proceedings of the National Academy of Sciences of the United States of America. 93 (25): 14912–6. Bibcode:1996PNAS...9314912A. doi: 10.1073/pnas.93.25.14912 . PMC   26236 . PMID   8962155.
  4. Siddharthan R, Siggia ED, van Nimwegen E (December 2005). "PhyloGibbs: a Gibbs sampling motif finder that incorporates phylogeny". PLOS Computational Biology. 1 (7): e67. Bibcode:2005PLSCB...1...67S. doi:10.1371/journal.pcbi.0010067. PMC   1309704 . PMID   16477324.
  5. Wong KC (October 2017). "MotifHyades: expectation maximization for de novo DNA motif pair discovery on paired sequences". Bioinformatics. 33 (19): 3028–3035. doi: 10.1093/bioinformatics/btx381 . PMID   28633280.
  6. Wong KC (September 2018). "DNA Motif Recognition Modeling from Protein Sequences". iScience. 7: 198–211. Bibcode:2018iSci....7..198W. doi:10.1016/j.isci.2018.09.003. PMC   6153143 . PMID   30267681.
  7. Matsuda H, Taniguchi F, Hashimoto A (1997). "An approach to detection of protein structural motifs using an encoding scheme of backbone conformations" (PDF). Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing: 280–91. PMID   9390299.

Further reading

Secondary and tertiary sources

Primary sources