Prediction of substrate-enzyme-product interaction based on molecular descriptors and physicochemical properties.
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.
Shanghai Key Laboratory of Bio-Energy Crops, School of Life Science, Shanghai University, 333 Nancheng Road, Shanghai 200444, China.
Bing
B
Niu
Institute of Systems Biology, Shanghai University, Shanghai, China ; Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Shanghai 200444, China.
Guohua
G
Huang
Department of Radiology, First People's Hospital, Shanghai Jiaotong University, Shanghai 200080, China.
Linfeng
L
Zheng
Shanghai Key Laboratory of Bio-Energy Crops, School of Life Science, Shanghai University, 333 Nancheng Road, Shanghai 200444, China.
Xueyuan
X
Wang
Shanghai Key Laboratory of Bio-Energy Crops, School of Life Science, Shanghai University, 333 Nancheng Road, Shanghai 200444, China.
Fuxue
F
Chen
Department of Neurosurgery, Changhai Hospital, Second Military Medical University, Shanghai 200433, China.
Yuhui
Y
Zhang
Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Tao
T
Huang
D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry*; D004798:Enzymes / Q000737:chemistry* / Q000235:genetics; D053858:Metabolic Networks and Pathways / Q000235:genetics*; D008962:Models, Theoretical; D017433:Protein Structure, Secondary; D020539:Sequence Analysis, Protein; D013379:Substrate Specificity
D000596:Amino Acids; D004798:Enzymes