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Scoring Card Method Bacterial tYrosine Kinase :
Identifying and characterizing bacterial tyrosine kinase using propensity scores of dipeptides

Home | Download | Release 0.1, Last update: May 12, 2016 

Please paste target sequence(s) in FASTA format. (one time one sequence)


Bacterial tyrosine-kinases (BY-kinases), which play an important role in numerous 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.


This study proposes a novel SCMBYK method, to predict and characterize BY-kinases. A dataset consisting of 798 BY-kinases and 776 non-BY-kinases was newly established to design the SCMBYK predictor, which achieved training and test accuracy of 96.73% 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% test accuracy. After analyzing SCMBYK-derived propensity scores, four characteristics of BY-kinases were determined: 1) BY-kinases prefer to be composed of α-helices; 2) the content of extracellular regions of BY-kinases is expected to be dominated by such residues, as Val, Ile, Phe and Tyr; 3) BY-kinases structurally resemble nuclear proteins; 4) role of different domains in triggering BY-kinase activity. Additionally, in potential antibiotics targeting to BY-kinases, Azathioprinewas found to suppress the virulence of M. tuberculosis and can be considered as a potential antibiotic for tuberculosis treatment.

The flowchart of system designs for predicting and characterizing bacterial tyrosine kinases (BYKs).

Scoring card of bacterial tyrosine kinases propensity scores.

Contact with:

Hui-Ling Huang, Shinn-Ying Ho

Related publications of SCM:

Huang HL, Charoenkwan P, Kao TF, Lee HC, Chang FL, Huang WL, Ho SJ, Shu LS, Chen WL, Ho SY: Prediction and analysis of protein solubility using a novel scoring card method with dipeptide composition. Bmc Bioinformatics 2012, 13.

Charoenkwan P, Shoombuatong W, Lee HC, Chaijaruwanich J, Huang HL, Ho SY: SCMCRYS: Predicting Protein Crystallization Using an Ensemble Scoring Card Method with Estimating Propensity Scores of P-Collocated Amino Acid Pairs. Plos One 2013, 8(9).

Tamara Vasylenko, Yi-Fan Liou, Hong-An Chen, Phasit Charoenkwan, Hui-Ling Huang* and Shinn-Ying Ho*, SCMPSP: Prediction and characterization of photosynthetic proteins based on a scoring card method, BMC Bioinformatics, 16 (Suppl 1):S8, 2015

Yi-Fan Liou, Phasit Charoenkwan, Yerukala Sathipati Srinivasulu, Tamala Vasylenko, Shih-Chung Lai, Hua-Chin Lee, Yi-Hsiung Chen, Hui-Ling Huang* and Shinn-Ying Ho*, "SCMHBP: Prediction and analysis of heme binding proteins using propensity scores of dipeptides," BMC Bioinformatics, 15 Suppl 16:S4, Dec. 2014.

Liou Y-F, Vasylenko T, Yeh C-L, Lin W-C, Chiu S-H, Charoenkwan P, et al: SCMMTP: identifying and characterizing membrane transport proteins using propensity scores of dipeptides. BMC Genomics. 2015, 16 (Suppl 12): S6-