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PPLK+C: A Bioinformatics Tool for Predicting Peptide Ligands of Potassium Channels Based on Primary Structure Information

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Abstract

Potassium channels play a key role in regulating the flow of ions through the plasma membrane, orchestrating many cellular processes including cell volume regulation, hormone secretion and electrical impulse formation. Ligand peptides of potassium channels are molecules used in basic and applied research and are now considered promising alternatives in the treatment of many diseases, such as cardiovascular diseases and cancer. Currently, there are various bioinformatics tools focused on the prediction of peptides with different activities. However, none of the current tools can predict ligand peptides of potassium channels. In this work, we developed a tool called PPLK+C; this is the first tool that can predict peptide ligands of potassium channels. We also evaluated several amino acid molecular features and four machine-learning algorithms for the prediction of potassium channel ligand peptides: random forest, nearest neighbors, support vector machine and artificial neural network. All the biological data used in this study for training and validating models were obtained from peptides with experimentally verified activity. PPLK+C is a bioinformatics software written in the Python programming language, which showed a high predictive capacity with a model generated with the random forest algorithm: 0.77 sensitivity, 0.94 specificity, 0.91 accuracy and 0.70 Matthews correlation coefficient. PPLK+C is a novel tool with a friendly interface that can be used for the discovery of novel ligand peptides of potassium channels with high reliability, using only primary structure information.

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Acknowledgements

The authors would like to thank to DIUFRO DI19-2015, DIUFRO DI12-PEO1 and DIUFRO DIE14-0001 projects and the UFRO scholarship of the University of La Frontera, Chile.

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Correspondence to Jorge G. Farias.

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Lissabet, J.F.B., Belén, L.H. & Farias, J.G. PPLK+C: A Bioinformatics Tool for Predicting Peptide Ligands of Potassium Channels Based on Primary Structure Information. Interdiscip Sci Comput Life Sci (2020) doi:10.1007/s12539-019-00356-5

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Keywords

  • Peptide
  • Prediction
  • Bioinformatics
  • Potassium channel
  • Machine learning