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BioMed Research International
Volume 2013, Article ID 674215, 7 pages
http://dx.doi.org/10.1155/2013/674215
Research Article

Prediction of Substrate-Enzyme-Product Interaction Based on Molecular Descriptors and Physicochemical Properties

1Shanghai Key Laboratory of Bio-Energy Crops, School of Life Science, Shanghai University, 333 Nancheng Road, Shanghai 200444, China
2Institute of Systems Biology, Shanghai University, Shanghai, China
3Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Shanghai 200444, China
4Department of Radiology, First People’s Hospital, Shanghai Jiaotong University, Shanghai 200080, China
5Department of Neurosurgery, Changhai Hospital, Second Military Medical University, Shanghai 200433, China
6Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA

Received 2 November 2013; Accepted 30 November 2013

Academic Editor: Yudong Cai

Copyright © 2013 Bing Niu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

It is important to correctly and efficiently predict the interaction of substrate-enzyme and to predict their product in metabolic pathway. In this work, a novel approach was introduced to encode substrate/product and enzyme molecules with molecular descriptors and physicochemical properties, respectively. Based on this encoding method, KNN was adopted to build the substrate-enzyme-product interaction network. After selecting the optimal features that are able to represent the main factors of substrate-enzyme-product interaction in our prediction, totally 160 features out of 290 features were attained which can be clustered into ten categories: elemental analysis, geometry, chemistry, amino acid composition, predicted secondary structure, hydrophobicity, polarizability, solvent accessibility, normalized van der Waals volume, and polarity. As a result, our predicting model achieved an MCC of 0.423 and an overall prediction accuracy of 89.1% for 10-fold cross-validation test.