MetalExplorer, a Bioinformatics Tool for the Improved Prediction of Eight Types of Metal-binding Sites Using a Random Forest Algorithm with Two-step Feature Selection

(E-pub Ahead of Print)

Author(s): Jiangning Song, Chen Li, Cheng Zheng, Jerico Revote, Ziding Zhang, Geoffrey I Webb.

Journal Name:Current Bioinformatics

Volume 11 , 2016

Abstract:

Metalloproteins are highly involved in many biological processes, including catalysis, recognition, transport, transcription, and signal transduction. The metal ions they bind usually play enzymatic or structural roles in mediating these diverse functional roles. Thus, the systematic analysis and prediction of metal-binding sites using sequence and/or structural information are crucial for understanding their sequence-structure-function relationships. In this study, we propose MetalExplorer (http://metalexplorer.erc.monash.edu.au/), a new machine learning-based method for predicting eight different types of metal-binding sites (Ca, Co, Cu, Fe, Ni, Mg, Mn, and Zn) in proteins. Our approach combines heterogeneous sequence-, structure-, and residue contact network-based features. The predictive performance of MetalExplorer was tested by cross-validation and independent tests using non-redundant datasets of known structures. This method applies a two-step feature selection approach based on the maximum relevance minimum redundancy and forward feature selection to identify the most informative features that contribute to the prediction performance. With a precision of 60%, MetalExplorer achieved high recall values, which ranged from 59% to 88% for the eight metal ion types in fivefold cross-validation tests. Moreover, the common and type-specific features in the optimal subsets of all metal ions were characterized in terms of their contributions to the overall performance. In terms of both benchmark and independent datasets at the 60% precision control level, MetalExplorer compared favorably with an existing metalloprotein prediction tool, SitePredict. Thus, MetalExplorer is expected to be a powerful tool for the accurate prediction of potential metal-binding sites and it should facilitate the functional analysis and rational design of novel metalloproteins.

Keywords: metal-binding site prediction; random forest; feature selection.metal-binding site prediction; random forest; feature selection.metal-binding site prediction; random forest; feature selection.

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Article Details

VOLUME: 11
Year: 2016
(E-pub Ahead of Print)
DOI: 10.2174/2468422806666160618091522
Price: $95