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In biology, asequence motif is anucleotide oramino-acidsequencepattern that is widespread and usually assumed to be related tobiological function of the macromolecule. For example, anN-glycosylation site motif can be defined asAsn, followed by anything but Pro, followed by either Ser or Thr, followed by anything but Pro residue.
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When a sequence motif appears in theexon of agene, it mayencode the "structural motif" of aprotein; that is a stereotypical element of theoverall structure of the protein. Nevertheless, motifs need not be associated with a distinctivesecondary structure. "Noncoding" sequences are nottranslated into proteins, andnucleic acids with such motifs need not deviate from the typical shape (e.g. the "B-form"DNA double helix).
Outside of gene exons, there existregulatory sequence motifs and motifs within the "junk", such assatellite DNA. Some of these are believed to affect the shape of nucleic acids[1] (see for exampleRNA self-splicing), but this is only sometimes the case. For example, manyDNA binding proteins that have affinity for specificDNA 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 thatlabel proteins for delivery to particular parts of acell, or mark them forphosphorylation.
Within a sequence ordatabase of sequences, researchers search and find motifs using computer-based techniques ofsequence analysis, such asBLAST. Such techniques belong to the discipline ofbioinformatics. See alsoconsensus sequence.
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Consider theN-glycosylation site motif mentioned above:
This pattern may be written asN{P}[ST]{P} whereN = Asn,P = Pro,S = Ser,T = Thr;{X} means any amino acid exceptX; and[XY] means eitherX orY.
The notation[XY] does not give any indication of the probability ofX orY occurring in the pattern. Observed probabilities can be graphically represented usingsequence logos. Sometimes patterns are defined in terms of a probabilistic model such as ahidden Markov model.
The notation[XYZ] meansX orY orZ, 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 theIQ motif may be taken to be:
[FILV]Qxxx[RK]Gxxx[RK]xx[FILVWY]wherex signifies any amino acid, and the square brackets indicate an alternative (see below for further details about notation).
Usually, however, the first letter isI, and both[RK] choices resolve toR. Since the last choice is so wide, the patternIQxxxRGxxxR is sometimes equated with the IQ motif itself, but a more accurate description would be aconsensus sequence for the IQ motif.
Several notations for describing motifs are in use but most of them are variants of standard notations forregular expressions and use these conventions:
[abc] matches any of the amino acids represented bya orb orc.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 toACF,ADF,AEF,BCF,BDF, andBEF.
Different pattern description notations have other ways of forming pattern elements. One of these notations is the PROSITE notation, described in the following subsection.
ThePROSITE notation uses theIUPAC 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:
x' can be used as a pattern element to denote any amino acid.{ST} denotes any amino acid other thanS orT.<'.>'.>' can also occur inside a terminating square bracket pattern, so thatS[T>] matches both "ST" and "S>".e is a pattern element, andm andn are two decimal integers withm <=n, then:e(m) is equivalent to the repetition ofe exactlym times;e(m,n) is equivalent to the repetition ofe exactlyk times for any integerk satisfying:m <=k <=n.Some examples:
x(3) is equivalent tox-x-x.x(2,4) matches any sequence that matchesx-x orx-x-x orx-x-x-x.The signature of the C2H2-typezinc finger domain is:
C-x(2,4)-C-x(3)-[LIVMFYWC]-x(8)-H-x(3,5)-HA 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 theTRANSFAC 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.
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.
Process of discovery
Motif discovery happens in three major phases. A pre-processing stage where sequences are meticulously prepared in assembly and cleaning steps. Assembly involves selecting sequences that contain the desired motif in large quantities, and extraction of unwanted sequences using clustering. Cleaning then ensures the removal of any confounding elements. Next there is the discovery stage. In this phase sequences are represented using consensus strings orPosition-specific Weight Matrices (PWM). After motif representation, an objective function is chosen and a suitable search algorithm is applied to uncover the motifs. Finally the post-processing stage involves evaluating the discovered motifs.[2]
There are software programs which, given multiple input sequences, attempt to identify one or more candidate motifs. One example is theMultiple EM for Motif Elicitation (MEME) algorithm, which generates statistical information for each candidate.[3] There are more than 100 publications detailing motif discovery algorithms; Weirauchet al. evaluated many related algorithms in a 2013 benchmark.[4] Theplanted motif search is another motif discovery method that is based on combinatorial approach.
Motifs have also been discovered by taking aphylogenetic 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 andD. melanogaster, Akiyama and others discovered a pattern which they called theGCM motif in 1996.[5] It spans about 150 amino acid residues, and begins as follows:
WDIND*.*P..*...D.F.*W***.**.IYS**...A.*H*S*WAMRNTNNHNHere 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 modernprotein domain databases such asPfam: human curators would select a pool of sequences known to be related and use computer programs to align them and produce the motif profile (Pfam usesHMMs, which can be used to identify other related proteins.[6] A phylogenic approach can also be used to enhance thede novo MEME algorithm, with PhyloGibbs being an example.[7]
In 2017, MotifHyades has been developed as a motif discovery tool that can be directly applied to paired sequences.[8]
In 2018, aMarkov random field approach has been proposed to infer DNA motifs fromDNA-binding domains of proteins.[9]
Motif Discovery Algorithms
Motif discovery algorithms use diverse strategies to uncover patterns in DNA sequences. Integrating enumerative, probabilistic, and nature-inspired approaches, demonstrate their adaptability, with the use of multiple methods proving effective in enhancing identification accuracy.
Enumerative Approach:[2]
Initiating the motif discovery journey, the enumerative approach witnesses algorithms meticulously generating and evaluating potential motifs. Pioneering this domain are Simple Word Enumeration techniques, such as YMF and DREME, which systematically go through the sequence in search of short motifs. Complementing these, Clustering-Based Methods such as CisFinder employ nucleotide substitution matrices for motif clustering, effectively mitigating redundancy. Concurrently, Tree-Based Methods like Weeder and FMotif exploit tree structures, and Graph Theoretic-Based Methods (e.g., WINNOWER) employ graph representations, demonstrating the richness of enumeration strategies.
Probabilistic Approach:[2]
Diverging into the probabilistic realm, this approach capitalizes on probability models to discern motifs within sequences. MEME, a deterministic exemplar, employs Expectation-Maximization for optimizing Position Weight Matrices (PWMs) and unraveling conserved regions in unaligned DNA sequences. Contrasting this, stochastic methodologies like Gibbs Sampling initiate motif discovery with random motif position assignments, iteratively refining the predictions. This probabilistic framework adeptly captures the inherent uncertainty associated with motif discovery.
Advanced Approach:[2]
Evolving further, advanced motif discovery embraces sophisticated techniques, withBayesian modeling[10] taking center stage. LOGOS and BaMM, exemplifying this cohort, intricately weave Bayesian approaches andMarkov models into their fabric for motif identification. The incorporation of Bayesian clustering methods enhances the probabilistic foundation, providing a holistic framework for pattern recognition in DNA sequences.
Nature-Inspired and Heuristic Algorithms:[2]
A distinct category unfolds, wherein algorithms draw inspiration from the biological realm.Genetic Algorithms (GA), epitomized by FMGA and MDGA,[11] navigate motif search through genetic operators and specialized strategies. Harnessing swarm intelligence principles,Particle Swarm Optimization (PSO),Artificial Bee Colony (ABC) algorithms, andCuckoo Search (CS) algorithms, featured in GAEM, GARP, and MACS, venture into pheromone-based exploration. These algorithms, mirroring nature's adaptability and cooperative dynamics, serve as avant-garde strategies for motif identification. The synthesis of heuristic techniques in hybrid approaches underscores the adaptability of these algorithms in the intricate domain of motif discovery.
TheE. coli lactoseoperon repressor LacI (PDB:1lcc chain A) andE. coli catabolite gene activator (PDB:3gap chain A) both have ahelix-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):[12] The code encodes thetorsion angles between alpha-carbons of theprotein backbone. "W" always corresponds to an alpha helix.
| 3D chain code | Amino acid sequence | |
|---|---|---|
| 1lccA | TWWWWWWWKCLKWWWWWWG | LYDVAEYAGVSYQTVSRVV |
| 3gapA | KWWWWWWGKCFKWWWWWWW | RQEIGQIVGCSRETVGRIL |
