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• TITLE: MetalloPred: A tool for hierarchical prediction of metal ion binding proteins using cluster of neural networks and sequence derived features

  AUTHORS: Pradeep Kumar Naik, Piyush Ranjan, Pooja Kesari, Sankalp Jain

  KEYWORDS: Metalloprotein; Classification; Sequence Derived Parameters; Neural Networks

  JOURNAL NAME: Journal of Biophysical Chemistry, Vol.2 No.2, June 1, 2011

  ABSTRACT: Given a protein sequence, how can we identify whether it is a metalloprotein or not? If it is, which main functional class and subclasses it belongs to? This is an important biological question because they are closely related to the biological function of an uncharacterized protein. Particularly, with the avalanche of protein sequences generated in the post genomic era and since conventional techniques are time consuming and expensive, it is highly desirable to develop an automated method by which one can get a fast and accurate answer to these questions. Here, a top-down predictor, called MetalloPred, is developed which consists of 3 level of hierarchical classification using cascade of neural networks from sequence derived features. The 1st layer of the prediction engine is for identifying a query protein as metalloprotein or not; the 2nd layer for the main functional class; and the 3rd layer for the sub-functional class. The overall success rates for all the three layers are higher than 60% that were obtained through rigorous cross-validation tests on the very stringent benchmark datasets in which none of the proteins has 30% sequence identity with any other in the same class or subclass. MetalloPred achieved good prediction accuracies and could nicely complement experimental approaches for identification of metal binding proteins. MetalloPred is freely available to be used in-house as a standalone and is accessible at http://www.juit.ac.in/assets/Metallopred/.