SCMBYK: prediction and characterization of bacterial tyrosine-kinases based on propensity scores of dipeptides.
BACKGROUND
Bacterial tyrosine-kinases (BY-kinases), which play an important role in numerous cellular processes, are characterized as a separate class of enzymes and share no structural similarity with their eukaryotic counterparts. However, in silico methods for predicting BY-kinases have not been developed yet. Since these enzymes are involved in key regulatory processes, and are promising targets for anti-bacterial drug design, it is desirable to develop a simple and easily interpretable predictor to gain new insights into bacterial tyrosine phosphorylation. This study proposes a novel SCMBYK method for predicting and characterizing BY-kinases.


RESULTS
A dataset consisting of 797 BY-kinases and 783 non-BY-kinases was established to design the SCMBYK predictor, which achieved training and test accuracies of 97.55 and 96.73%, respectively. Furthermore, the leave-one-phylum-out method was used to predict specific bacterial phyla hosts of target sequences, gaining 97.39% average test accuracy. After analyzing SCMBYK-derived propensity scores, four characteristics of BY-kinases were determined: 1) BY-kinases tend to be composed of α-helices; 2) the amino-acid content of extracellular regions of BY-kinases is expected to be dominated by residues such as Val, Ile, Phe and Tyr; 3) BY-kinases structurally resemble nuclear proteins; 4) different domains play different roles in triggering BY-kinase activity.


CONCLUSIONS
The SCMBYK predictor is an effective method for identification of possible BY-kinases. Furthermore, it can be used as a part of a novel drug repurposing method, which recognizes putative BY-kinases and matches them to approved drugs. Among other results, our analysis revealed that azathioprine could suppress the virulence of M. tuberculosis, and thus be considered as a potential antibiotic for tuberculosis treatment.
Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan.
Tamara
T
Vasylenko
Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan.
Yi-Fan
YF
Liou
Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan.
Po-Chin
PC
Chiou
Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan.
Hsiao-Wei
HW
Chu
Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan.
Yung-Sung
YS
Lai
Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan.
Yu-Ling
YL
Chou
Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan. hlhuang.nctu@gmail.com.|College of Biological Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan. hlhuang.nctu@gmail.com.|Center for Bioinformatics Research, National Chiao Tung University, Hsinchu, Taiwan. hlhuang.nctu@gmail.com.
Hui-Ling
HL
Huang
Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan. syho@mail.nctu.edu.tw.|College of Biological Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan. syho@mail.nctu.edu.tw.|Center for Bioinformatics Research, National Chiao Tung University, Hsinchu, Taiwan. syho@mail.nctu.edu.tw.
Shinn-Ying
SY
Ho
D001419:Bacteria / Q000201:enzymology*; D001426:Bacterial Proteins / Q000737:chemistry*; D030562:Databases, Protein; D004151:Dipeptides / Q000737:chemistry*; D057216:Propensity Score; D011505:Protein-Tyrosine Kinases / Q000737:chemistry*; D012984:Software; D014443:Tyrosine / Q000737:chemistry*
D001426:Bacterial Proteins; D004151:Dipeptides; D014443:Tyrosine; D011505:Protein-Tyrosine Kinases
BY-kinase; Dipeptide; Drug repurposing; Propensity scores; Scoring card method