ArticleBMC bioinformatics2016
SCMBYK: prediction and characterization of bacterial tyrosine-kinases based on propensity scores of dipeptides.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 16 citations in OpenAlex.
- PSR-MAPMS: A new approach for the interpretable prediction of myelin autoantigenic peptides in multiple sclerosis using multi-source propensity scores.Protein science : a publication of the Protein Society · 2025Article
- TROLLOPE: A novel sequence-based stacked approach for the accelerated discovery of linear T-cell epitopes of hepatitis C virus.PloS one · 2023Article
- iAMAP-SCM: A Novel Computational Tool for Large-Scale Identification of Antimalarial Peptides Using Estimated Propensity Scores of Dipeptides.ACS omega · 2022Article
- SCMRSA: a New Approach for Identifying and Analyzing Anti-MRSA Peptides Using Estimated Propensity Scores of Dipeptides.ACS omega · 2022Article
- SCMTHP: A New Approach for Identifying and Characterizing of Tumor-Homing Peptides Using Estimated Propensity Scores of Amino Acids.Pharmaceutics · 2022Article
- Meta-iPVP: a sequence-based meta-predictor for improving the prediction of phage virion proteins using effective feature representation.Journal of computer-aided molecular design · 2020Article
- PVPred-SCM: Improved Prediction and Analysis of Phage Virion Proteins Using a Scoring Card Method.Cells · 2020Article
- Bioinformatics and systems biology research update from the 15BMC bioinformatics · 2016Article
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8 authors at 1 institution in 1 country.
Funding
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Abstract
backgroundBacterial 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.
resultsA 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.
conclusionsThe 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.
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