A Model for the Recognition of Protein Kinases Based on the Entropy of 3D van der Waals Interactions

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• 作者：Humberto Gonzá ; lez-Dí ; az ; Liane Saiz-Urra ; Reinaldo Molina ; Lourdes Santana ; Eugenio Uriarte
• 刊名：Journal of Proteome Research
• 出版年：2007
• 出版时间：February 2007
• 年：2007
• 卷：6
• 期：2
• 页码：904 - 908
• 全文大小：251K
• 年卷期：v.6,no.2(February 2007)
• ISSN：1535-3907

文摘

The study and prediction of kinase function (kinomics) is of major importance for proteome research dueto the widespread distribution of kinases. However, theprediction of protein function based on the similaritybetween a functionally annotated 3D template and a querystructure may fail, for instance, if a similar proteinstructure cannot be identified. Alternatively, function canbe assigned using 3D-structural empirical parameters. Inprevious studies, we introduced parameters based onelectrostatic entropy (Proteins 2004, 56, 715) and molecular vibration entropy (Bioinformatics 2003, 19, 2079) butignored other important factors such as van der Waals(vdw) interactions. In the work described here, we define3D-vdw entropies (_k) and use them for the first time toderive a classifier for protein kinases. The model classifiescorrectly 88.0% of proteins in training and more than85.0% of proteins in validation studies. Principal components analysis of heterogeneous proteins demonstratedthat _k codify information that is different to that described by other bulk or folding parameters. In additionalvalidation experiments, the model recognized 129 out of142 kinases (90.8%) and 592 out of 677 non-kinases(87.4%) not used above. This study provides a basis forfurther consideration of _k as parameters for the empirical search for structure-function relationships.