A tree-based conservation scoring method for short linear motifs in multiple alignments of protein sequences View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2008-05-06

AUTHORS

Claudia Chica, Alberto Labarga, Cathryn M Gould, Rodrigo López, Toby J Gibson

ABSTRACT

BackgroundThe structure of many eukaryotic cell regulatory proteins is highly modular. They are assembled from globular domains, segments of natively disordered polypeptides and short linear motifs. The latter are involved in protein interactions and formation of regulatory complexes. The function of such proteins, which may be difficult to define, is the aggregate of the subfunctions of the modules. It is therefore desirable to efficiently predict linear motifs with some degree of accuracy, yet sequence database searches return results that are not significant.ResultsWe have developed a method for scoring the conservation of linear motif instances. It requires only primary sequence-derived information (e.g. multiple alignment and sequence tree) and takes into account the degenerate nature of linear motif patterns. On our benchmarking, the method accurately scores 86% of the known positive instances, while distinguishing them from random matches in 78% of the cases. The conservation score is implemented as a real time application designed to be integrated into other tools. It is currently accessible via a Web Service or through a graphical interface.ConclusionThe conservation score improves the prediction of linear motifs, by discarding those matches that are unlikely to be functional because they have not been conserved during the evolution of the protein sequences. It is especially useful for instances in non-structured regions of the proteins, where a domain masking filtering strategy is not applicable. More... »

PAGES

229

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1471-2105-9-229

DOI

http://dx.doi.org/10.1186/1471-2105-9-229

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https://app.dimensions.ai/details/publication/pub.1028307827

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/18460207


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