Please use this identifier to cite or link to this item: https://doi.org/10.3390/ijms20020302
Title: KSIMC: Predicting kinase–Substrate interactions based on matrix completion
Authors: Gan, J
Qiu, J
Deng, C
Lan, W
Chen, Q 
Hu, Y
Keywords: algorithm
amino acid sequence
Article
bioinformatics
computer analysis
enzyme substrate complex
false positive result
gene expression
human
kinase substrate interaction
measurement accuracy
molecular docking
protein interaction
protein phosphorylation
receiver operating characteristic
sequence alignment
signal transduction
validation process
antagonists and inhibitors
biology
biophysics
chemistry
enzyme specificity
factual database
phosphorylation
protein analysis
theoretical model
phosphotransferase
Algorithms
Biophysical Phenomena
Computational Biology
Databases, Factual
Humans
Models, Theoretical
Phosphorylation
Phosphotransferases
Protein Interaction Mapping
Sequence Alignment
Substrate Specificity
Issue Date: 2019
Citation: Gan, J, Qiu, J, Deng, C, Lan, W, Chen, Q, Hu, Y (2019). KSIMC: Predicting kinase–Substrate interactions based on matrix completion. International Journal of Molecular Sciences 20 (2) : 302. ScholarBank@NUS Repository. https://doi.org/10.3390/ijms20020302
Rights: Attribution 4.0 International
Abstract: Protein phosphorylation is an important chemical modification catalyzed by kinases. It plays important roles in many cellular processes. Predicting kinase–substrate interactions is vital to understanding the mechanism of many diseases. Many computational methods have been proposed to identify kinase–substrate interactions. However, the prediction accuracy still needs to be improved. Therefore, it is necessary to develop an efficient computational method to predict kinase–substrate interactions. In this paper, we propose a novel computational approach, KSIMC, to identify kinase–substrate interactions based on matrix completion. Firstly, the kinase similarity and substrate similarity are calculated by aligning sequence of kinase–kinase and substrate–substrate, respectively. Then, the original association network is adjusted based on the similarities. Finally, the matrix completion is used to predict potential kinase–substrate interactions. The experiment results show that our method outperforms other state-of-the-art algorithms in performance. Furthermore, the relevant databases and scientific literature verify the effectiveness of our algorithm for new kinase–substrate interaction identification. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.
Source Title: International Journal of Molecular Sciences
URI: https://scholarbank.nus.edu.sg/handle/10635/183294
ISSN: 16616596
DOI: 10.3390/ijms20020302
Rights: Attribution 4.0 International
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