Jiangning Song, Chen Li, Cheng Zheng, Jerico Revote, Ziding Zhang* and Geoffrey I. Webb* Pages 480 - 489 ( 10 )
Background: 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.
Objective: The objective of this work is to develop a new computational algorithm for improved prediction of major types of metal-binding sites.
Method: 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 in a random forest machine-learning framework.
Results: 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.
Conclusion: In terms of both benchmark and independent datasets at the 60% precision control level, MetalExplorer compared favorably with an existing metalloprotein prediction tool, SitePredict. 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.
Metal-binding site prediction, random forest, feature selection, functional annotation, machine learning, sequence analysis.
Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, National Engineering Laboratory of Industrial Enzymes and Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, Monash Bioinformatics Platform, Faculty of Medicine, Monash University, Melbourne, VIC 3800, State Key laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC 3800