A global characterization and identification of multifunctional enzymes.
Multi-functional enzymes are enzymes that perform multiple physiological functions. Characterization and identification of multi-functional enzymes are critical for communication and cooperation between different functions and pathways within a complex cellular system or between cells. In present study, we collected literature-reported 6,799 multi-functional enzymes and systematically characterized them in structural, functional, and evolutionary aspects. It was found that four physiochemical properties, that is, charge, polarizability, hydrophobicity, and solvent accessibility, are important for characterization of multi-functional enzymes. Accordingly, a combinational model of support vector machine and random forest model was constructed, based on which 6,956 potential novel multi-functional enzymes were successfully identified from the ENZYME database. Moreover, it was observed that multi-functional enzymes are non-evenly distributed in species, and that Bacteria have relatively more multi-functional enzymes than Archaebacteria and Eukaryota. Comparative analysis indicated that the multi-functional enzymes experienced a fluctuation of gene gain and loss during the evolution from S. cerevisiae to H. sapiens. Further pathway analyses indicated that a majority of multi-functional enzymes were well preserved in catalyzing several essential cellular processes, for example, metabolisms of carbohydrates, nucleotides, and amino acids. What's more, a database of known multi-functional enzymes and a server for novel multi-functional enzyme prediction were also constructed for free access at http://bioinf.xmu.edu.cn/databases/MFEs/index.htm.
State Key Laboratory of Stress Cell Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, People's Republic of China.
Xian-Ying
XY
Cheng
Wei-Juan
WJ
Huang
Shi-Chang
SC
Hu
Hai-Lei
HL
Zhang
Hao
H
Wang
Jing-Xian
JX
Zhang
Hong-Huang
HH
Lin
Yu-Zong
YZ
Chen
Quan
Q
Zou
Zhi-Liang
ZL
Ji
D030562:Databases, Protein; D004798:Enzymes / Q000737:chemistry / Q000378:metabolism*; D008954:Models, Biological; D060388:Support Vector Machine
D004798:Enzymes