Computational prediction and analysis of protein γ-carboxylation sites based on a random forest method.

Abstract:

:The glutamate γ-carboxylation plays a pivotal part in a number of important human diseases. However, traditional protein γ-carboxylation site detection by experimental approaches are often laborious and time-consuming. In this study, we initiated an attempt for the computational prediction of protein γ-carboxylation sites. We developed a new method for predicting the γ-carboxylation sites based on a Random Forest method. As a result, 90.44% accuracy and 0.7739 MCC value were obtained for the training dataset, and 89.83% accuracy and 0.7448 MCC value for the testing dataset. Our method considered several features including sequence conservation, residual disorder, secondary structures, solvent accessibility, physicochemical/biochemical properties and amino acid occurrence frequencies. By means of the feature selection algorithm, an optimal set of 327 features were selected; these features were considered as the ones that contributed significantly to the prediction of protein γ-carboxylation sites. Analysis of the optimal feature set indicated several important factors in determining the γ-carboxylation and a possible consensus sequence of the γ-carboxylation recognition site (γ-CRS) was suggested. These may shed some light on the in-depth understanding of the mechanisms of γ-carboxylation, providing guidelines for experimental validation.

journal_name

Mol Biosyst

journal_title

Molecular bioSystems

authors

Zhang N,Li BQ,Gao S,Ruan JS,Cai YD

doi

10.1039/c2mb25185j

subject

Has Abstract

pub_date

2012-11-01 00:00:00

pages

2946-55

issue

11

eissn

1742-206X

issn

1742-2051

journal_volume

8

pub_type

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