Dataset Information

SCMBYK: prediction and characterization of bacterial tyrosine-kinases based on propensity scores of dipeptides.

ABSTRACT: 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.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 ?-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.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.

SUBMITTER: Vasylenko T

PROVIDER: S-EPMC5260027 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

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Publications

SCMBYK: prediction and characterization of bacterial tyrosine-kinases based on propensity scores of dipeptides.

Vasylenko Tamara T Liou Yi-Fan YF Chiou Po-Chin PC Chu Hsiao-Wei HW Lai Yung-Sung YS Chou Yu-Ling YL Huang Hui-Ling HL Ho Shinn-Ying SY

BMC bioinformatics 20161222 Suppl 19

<h4>Background</h4>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 interpreta ...[more]

PMID: 28155663

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Project description:Bioluminescent proteins (BLPs) are a class of proteins with various mechanisms of light emission such as bioluminescence and fluorescence from luminous organisms. While valuable for commercial and medical applications, identification of BLPs, including luciferases and fluorescent proteins (FPs), is rather challenging, owing to their high variety of protein sequences. Moreover, characterization of BLPs facilitates mutagenesis analysis to enhance bioluminescence and fluorescence. Therefore, this study proposes a novel methodological approach to estimating the propensity scores of 400 dipeptides and 20 amino acids in order to design two prediction methods and characterize BLPs based on a scoring card method (SCM). The SCMBLP method for predicting BLPs achieves an accuracy of 90.83% for 10-fold cross-validation higher than existing support vector machine based methods and a test accuracy of 82.85%. A dataset consisting of 269 luciferases and 216 FPs is also established to design the SCMLFP prediction method, which achieves training and test accuracies of 97.10% and 96.28%, respectively. Additionally, four informative physicochemical properties of 20 amino acids are identified using the estimated propensity scores to characterize BLPs as follows: 1) high transfer free energy from inside to the protein surface, 2) high occurrence frequency of residues in the transmembrane regions of the protein, 3) large hydrophobicity scale from the native protein structure, and 4) high correlation coefficient (R = 0.921) between the amino acid compositions of BLPs and integral membrane proteins. Further analyzing BLPs reveals that luciferases have a larger value of R (0.937) than FPs (0.635), suggesting that luciferases tend to locate near the cell membrane location rather than FPs for convenient receipt of extracellular ions. Importantly, the propensity scores of dipeptides and amino acids and the identified properties facilitate efforts to predict, characterize, and apply BLPs, including luciferases, photoproteins, and FPs. The web server is available at http://iclab.life.nctu.edu.tw/SCMBLP/index.html.

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Dataset Information

SCMBYK: prediction and characterization of bacterial tyrosine-kinases based on propensity scores of dipeptides.

Publications

SCMBYK: prediction and characterization of bacterial tyrosine-kinases based on propensity scores of dipeptides.

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