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.
Similar content being viewed by others
References
Blackiston DJ, McLaughlin KA, Levin M (2009) Bioelectric controls of cell proliferation: ion channels, membrane voltage and the cell cycle. Cell Cycle 8:3527–3536
Sanders KM (2008) Regulation of smooth muscle excitation and contraction. Neurogastroenterol Motil 20:39–53
Dutertre S, Lewis RJ (2010) Use of venom peptides to probe ion channel structure and function. J Biol Chem 285:13315–13320
González C, Baez-Nieto D, Valencia I et al (2012) K(+) channels: function–structural overview. Compr Physiol 2:2087–2149. https://doi.org/10.1002/cphy.c110047
Lewis RJ, Garcia ML (2003) Therapeutic potential of venom peptides. Nat Rev Drug Discov 2:790
Wulff H, Castle NA, Pardo LA (2009) Voltage-gated potassium channels as therapeutic targets. Nat Rev Drug Discov 8:982
Suarez-Kurtz G, Vianna-Jorge R, Pereira BF et al (1999) Peptidyl inhibitors of shaker-type Kv1 channels elicit twitches in guinea pig ileum by blocking kv1.1 at enteric nervous system and enhancing acetylcholine release. J Pharmacol Exp Ther 289:1517–1522
Koo GC, Blake JT, Talento A et al (1997) Blockade of the voltage-gated potassium channel Kv1.3 inhibits immune responses in vivo. J Immunol 158:5120–5128
Leonard RJ, Garcia ML, Slaughter RS, Reuben JP (2006) Selective blockers of voltage-gated K+ channels depolarize human T lymphocytes: mechanism of the antiproliferative effect of charybdotoxin. Proc Natl Acad Sci USA. https://doi.org/10.1073/pnas.89.21.10094
Price M, Lee SC, Deutsch C (1989) Charybdotoxin inhibits proliferation and interleukin 2 production in human peripheral blood lymphocytes. Proc Natl Acad Sci USA. https://doi.org/10.1073/pnas.86.24.10171
Kalman K, Pennington MW, Lanigan MD et al (1998) Shk-Dap22, a potent Kv1.3-specific immunosuppressive polypeptide. J Biol Chem. https://doi.org/10.1074/jbc.273.49.32697
Ding L, Hao J, Luo X et al (2018) The Kv1.3 channel-inhibitory toxin BF9 also displays anticoagulant activity via inhibition of factor XIa. Toxicon. https://doi.org/10.1016/j.toxicon.2018.07.014
Aissaoui D, Mlayah-Bellalouna S, Jebali J et al (2018) Functional role of Kv1.1 and Kv1.3 channels in the neoplastic progression steps of three cancer cell lines, elucidated by scorpion peptides. Int J Biol Macromol. https://doi.org/10.1016/j.ijbiomac.2018.01.144
Pennington MW, Czerwinski A, Norton RS (2018) Peptide therapeutics from venom: current status and potential. Bioorg Med Chem. https://doi.org/10.1016/j.bmc.2017.09.029
Gupta S, Kapoor P, Chaudhary K et al (2013) In silico approach for predicting toxicity of peptides and proteins. PLoS ONE. https://doi.org/10.1371/journal.pone.0073957
Kuzmenkov AI, Krylov NA, Chugunov AO et al (2016) Kalium: a database of potassium channel toxins from scorpion venom. Database. https://doi.org/10.1093/database/baw056
Otvos L, Wade JD (2014) Current challenges in peptide-based drug discovery. Front Chem. https://doi.org/10.3389/fchem.2014.00062
Bhadra P, Yan J, Li J et al (2018) AmPEP: sequence-based prediction of antimicrobial peptides using distribution patterns of amino acid properties and random forest. Sci Rep. https://doi.org/10.1038/s41598-018-19752-w
Beltrán Lissabet JF, Belén LH, Farias JG (2019) AntiVPP 1.0: a portable tool for prediction of antiviral peptides. Comput Biol Med. https://doi.org/10.1016/j.compbiomed.2019.02.011
Agrawal P, Bhalla S, Chaudhary K et al (2018) In silico approach for prediction of antifungal peptides. Front Microbiol. https://doi.org/10.3389/fmicb.2018.00323
Lata S, Sharma BK, Raghava GPS (2007) Analysis and prediction of antibacterial peptides. BMC Bioinform. https://doi.org/10.1186/1471-2105-8-263
Manavalan B, Basith S, Shin TH et al (2017) MLACP: machine-learning-based prediction of anticancer peptides. Oncotarget. https://doi.org/10.18632/oncotarget.20365
Jhong JH, Chi YH, Li WC et al (2019) DbAMP: an integrated resource for exploring antimicrobial peptides with functional activities and physicochemical properties on transcriptome and proteome data. Nucleic Acids Res. https://doi.org/10.1093/nar/gky1030
Kawashima S, Ogata H, Kanehisa M (1999) AAindex: amino acid index database. Nucleic Acids Res 28:374
Boughorbel S, Jarray F, El-Anbari M (2017) Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric. PLoS ONE. https://doi.org/10.1371/journal.pone.0177678
Choe S (2002) Ion channel structure: potassium channel structures. Nat Rev Neurosci. https://doi.org/10.1038/nrn727
Shieh CC, Coghlan M, Sullivan JP, Gopalakrishnan M (2000) Potassium channels: molecular defects, diseases, and therapeutic opportunities. Pharmacol Rev 52:557–594
Wulff H, Christophersen P, Colussi P et al (2019) Antibodies and venom peptides: new modalities for ion channels. Nat Rev Drug Discov 18:339–357
Ortiz E, Possani LD (2018) Scorpion toxins to unravel the conundrum of ion channel structure and functioning. Toxicon 150:17–27
Chang KY, Yang JR (2013) Analysis and prediction of highly effective antiviral peptides based on random forests. PLoS ONE. https://doi.org/10.1371/journal.pone.0070166
Mei J, Fu Y, Zhao J (2018) Analysis and prediction of ion channel inhibitors by using feature selection and Chou’s general pseudo amino acid composition. J Theor Biol 456:41–48. https://doi.org/10.1016/j.jtbi.2018.07.040
Fernández-Ballester G, Fernández-Carvajal A, González-Ros JM, Ferrer-Montiel A (2011) Ionic channels as targets for drug design: a review on computational methods. Pharmaceutics 3:932–953
Salmaso V, Moro S (2018) Bridging molecular docking to molecular dynamics in exploring ligand–protein recognition process: an overview. Front Pharmacol 9:923
De Vivo M, Masetti M, Bottegoni G, Cavalli A (2016) Role of molecular dynamics and related methods in drug discovery. J Med Chem 59:4035–4061
Larranaga P (2006) Machine learning in bioinformatics. Brief Bioinform. https://doi.org/10.1093/bib/bbk007
Jenssen H, Hamill P, Hancock REW (2006) Peptide antimicrobial agents. Clin Microbiol Rev 19:491–511
Spänig S, Heider D (2019) Encodings and models for antimicrobial peptide classification for multi-resistant pathogens. BioData Min 12:7
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.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no competing interests.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
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 12, 258–263 (2020). https://doi.org/10.1007/s12539-019-00356-5
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12539-019-00356-5