Sequence motif
"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).
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.
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).
PROSITE allows the following pattern elements in addition to those described previously: Some examples: The signature of the C2H2-type zinc finger domain is: A matrix of numbers containing scores for each residue or nucleotide at each position of a fixed-length motif.
One example is the Multiple EM for Motif Elicitation (MEME) algorithm, which generates statistical information for each candidate.
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 (Pfam uses HMMs, which can be used to identify other related proteins.
[8] In 2018, a Markov random field approach has been proposed to infer DNA motifs from DNA-binding domains of proteins.
Integrating enumerative, probabilistic, and nature-inspired approaches, demonstrate their adaptability, with the use of multiple methods proving effective in enhancing identification accuracy.
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.
MEME, a deterministic exemplar, employs Expectation-Maximization for optimizing Position Weight Matrices (PWMs) and unraveling conserved regions in unaligned DNA sequences.
LOGOS and BaMM, exemplifying this cohort, intricately weave Bayesian approaches and Markov 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.
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.
