Molecular modeling of phosphorylation sites in proteins using a database of local structure segments View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2005-11

AUTHORS

Dariusz Plewczynski, Lukasz Jaroszewski, Adam Godzik, Andrzej Kloczkowski, Leszek Rychlewski

ABSTRACT

A new bioinformatics tool for molecular modeling of the local structure around phosphorylation sites in proteins has been developed. Our method is based on a library of short sequence and structure motifs. The basic structural elements to be predicted are local structure segments (LSSs). This enables us to avoid the problem of non-exact local description of structures, caused by either diversity in the structural context, or uncertainties in prediction methods. We have developed a library of LSSs and a profile--profile-matching algorithm that predicts local structures of proteins from their sequence information. Our fragment library prediction method is publicly available on a server (FRAGlib), at http://ffas.ljcrf.edu/Servers/frag.html . The algorithm has been applied successfully to the characterization of local structure around phosphorylation sites in proteins. Our computational predictions of sequence and structure preferences around phosphorylated residues have been confirmed by phosphorylation experiments for PKA and PKC kinases. The quality of predictions has been evaluated with several independent statistical tests. We have observed a significant improvement in the accuracy of predictions by incorporating structural information into the description of the neighborhood of the phosphorylated site. Our results strongly suggest that sequence information ought to be supplemented with additional structural context information (predicted with our segment similarity method) for more successful predictions of phosphorylation sites in proteins. More... »

PAGES

431-438

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00894-005-0235-z

DOI

http://dx.doi.org/10.1007/s00894-005-0235-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1018182705

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/16094535


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