Automatic Discovery of Protein Motifs
نویسندگان
چکیده
We have developed a novel representation of protein motifs that permits the rapid discovery of structural features in sets of protein sequences with a common structure or function. Many popular methods for representing protein motifs (consensus sequences, weight matrices, profiles, etc.) emphasize conservation of amino acids at specific sites in the sequence. Our method looks for correlations between amino acid variations at distinct sites. Correlations between the residues represent side-chain side-chain interactions and give insight into the structural properties of the motifs. Structural correlations can be used in database search to discover other proteins bearing similar relationships. This database search is significantly more sensitive than methods depending only upon conserved residues.
منابع مشابه
Improving Activity Discovery with Automatic Neighborhood Estimation
A fundamental problem for artificial intelligence is identifying perceptual primitives from raw sensory signals that are useful for higher-level reasoning. We equate these primitives with initially unknown recurring patterns called motifs. Autonomously learning the motifs is difficult because their number, location, length, and shape are all unknown. Furthermore, nonlinear temporal warping may ...
متن کاملAutomatic Discovery of Technology Networks for Industrial-Scale R&D IT Projects via Data Mining
Industrial-Scale R&D IT Projects depend on many sub-technologies which need to be understood and have their risks analysed before the project can begin for their success. When planning such an industrial-scale project, the list of technologies and the associations of these technologies with each other is often complex and form a network. Discovery of this network of technologies is time consumi...
متن کاملA study of statistical methods for function prediction of protein motifs.
Automatic discovery of new protein motifs (i.e. amino acid patterns) is one of the major challenges in bioinformatics. Several algorithms have been proposed that can extract statistically significant motif patterns from any set of protein sequences. With these methods, one can generate a large set of candidate motifs that may be biologically meaningful. This article examines methods to predict ...
متن کاملAutomatic Discovery Using Genetic Programming of an Unknown-Sized Detector of Protein Motifs Containing Repeatedly-Used Subexpressions
Automated methods of machine learning may be useful in discovering biologically meaningful patterns that are hidden in the rapidly growing databases of genomic and protein sequences. However, almost all existing methods of automated discovery require that the user specify, in advance, the size and shape of the pattern that is to be discovered. Moreover, existing methods do not have a workable a...
متن کاملExploiting Publicly Available Biological and Biochemical Information for the Discovery of Novel Short Linear Motifs
The function of proteins is often mediated by short linear segments of their amino acid sequence, called Short Linear Motifs or SLiMs, the identification of which can provide important information about a protein function. However, the short length of the motifs and their variable degree of conservation makes their identification hard since it is difficult to correctly estimate the statistical ...
متن کامل