Support-vector-machine classification of linear functional motifs in proteins View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2006-03

AUTHORS

Dariusz Plewczynski, Adrian Tkacz, Lucjan Stanisław Wyrwicz, Adam Godzik, Andrzej Kloczkowski, Leszek Rychlewski

ABSTRACT

Our algorithm predicts short linear functional motifs in proteins using only sequence information. Statistical models for short linear functional motifs in proteins are built using the database of short sequence fragments taken from proteins in the current release of the Swiss-Prot database. Those segments are confirmed by experiments to have single-residue post-translational modification. The sensitivities of the classification for various types of short linear motifs are in the range of 70%. The query protein sequence is dissected into short overlapping fragments. All segments are represented as vectors. Each vector is then classified by a machine learning algorithm (Support Vector Machine) as potentially modifiable or not. The resulting list of plausible post-translational sites in the query protein is returned to the user. We also present a study of the human protein kinase C family as a biological application of our method. More... »

PAGES

453-461

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00894-005-0070-2

DOI

http://dx.doi.org/10.1007/s00894-005-0070-2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1038239500

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/16341901


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