Interpretation of the consequences of mutations in protein kinases: combined use of bioinformatics and text mining.

Jose Maria Gonzalez-Izarzugaza, Martin Krallinger, Alfonso Valencia

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Protein kinases play a crucial role in a plethora of significant physiological functions and a number of mutations in this superfamily have been reported in the literature to disrupt protein structure and/or function. Computational and experimental research aims to discover the mechanistic connection between mutations in protein kinases and disease with the final aim of predicting the consequences of mutations on protein function and the subsequent phenotypic alterations. In this article, we will review the possibilities and limitations of current computational methods for the prediction of the pathogenicity of mutations in the protein kinase superfamily. In particular we will focus on the problem of benchmarking the predictions with independent gold standard datasets. We will propose a pipeline for the curation of mutations automatically extracted from the literature. Since many of these mutations are not included in the databases that are commonly used to train the computational methods to predict the pathogenicity of protein kinase mutations we propose them to build a valuable gold standard dataset in the benchmarking of a number of these predictors. Finally, we will discuss how text mining approaches constitute a powerful tool for the interpretation of the consequences of mutations in the context of disease genome analysis with particular focus on cancer.
Original languageEnglish
JournalFrontiers in Physiology
Volume3
Pages (from-to)323
ISSN1664-042X
DOIs
Publication statusPublished - 2012
Externally publishedYes

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