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

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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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  1. 1.

    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

  2. 2.

    Sanders KM (2008) Regulation of smooth muscle excitation and contraction. Neurogastroenterol Motil 20:39–53

  3. 3.

    Dutertre S, Lewis RJ (2010) Use of venom peptides to probe ion channel structure and function. J Biol Chem 285:13315–13320

  4. 4.

    González C, Baez-Nieto D, Valencia I et al (2012) K(+) channels: function–structural overview. Compr Physiol 2:2087–2149.

  5. 5.

    Lewis RJ, Garcia ML (2003) Therapeutic potential of venom peptides. Nat Rev Drug Discov 2:790

  6. 6.

    Wulff H, Castle NA, Pardo LA (2009) Voltage-gated potassium channels as therapeutic targets. Nat Rev Drug Discov 8:982

  7. 7.

    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

  8. 8.

    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

  9. 9.

    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.

  10. 10.

    Price M, Lee SC, Deutsch C (1989) Charybdotoxin inhibits proliferation and interleukin 2 production in human peripheral blood lymphocytes. Proc Natl Acad Sci USA.

  11. 11.

    Kalman K, Pennington MW, Lanigan MD et al (1998) Shk-Dap22, a potent Kv1.3-specific immunosuppressive polypeptide. J Biol Chem.

  12. 12.

    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.

  13. 13.

    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.

  14. 14.

    Pennington MW, Czerwinski A, Norton RS (2018) Peptide therapeutics from venom: current status and potential. Bioorg Med Chem.

  15. 15.

    Gupta S, Kapoor P, Chaudhary K et al (2013) In silico approach for predicting toxicity of peptides and proteins. PLoS ONE.

  16. 16.

    Kuzmenkov AI, Krylov NA, Chugunov AO et al (2016) Kalium: a database of potassium channel toxins from scorpion venom. Database.

  17. 17.

    Otvos L, Wade JD (2014) Current challenges in peptide-based drug discovery. Front Chem.

  18. 18.

    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.

  19. 19.

    Beltrán Lissabet JF, Belén LH, Farias JG (2019) AntiVPP 1.0: a portable tool for prediction of antiviral peptides. Comput Biol Med.

  20. 20.

    Agrawal P, Bhalla S, Chaudhary K et al (2018) In silico approach for prediction of antifungal peptides. Front Microbiol.

  21. 21.

    Lata S, Sharma BK, Raghava GPS (2007) Analysis and prediction of antibacterial peptides. BMC Bioinform.

  22. 22.

    Manavalan B, Basith S, Shin TH et al (2017) MLACP: machine-learning-based prediction of anticancer peptides. Oncotarget.

  23. 23.

    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.

  24. 24.

    Kawashima S, Ogata H, Kanehisa M (1999) AAindex: amino acid index database. Nucleic Acids Res 28:374

  25. 25.

    Boughorbel S, Jarray F, El-Anbari M (2017) Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric. PLoS ONE.

  26. 26.

    Choe S (2002) Ion channel structure: potassium channel structures. Nat Rev Neurosci.

  27. 27.

    Shieh CC, Coghlan M, Sullivan JP, Gopalakrishnan M (2000) Potassium channels: molecular defects, diseases, and therapeutic opportunities. Pharmacol Rev 52:557–594

  28. 28.

    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

  29. 29.

    Ortiz E, Possani LD (2018) Scorpion toxins to unravel the conundrum of ion channel structure and functioning. Toxicon 150:17–27

  30. 30.

    Chang KY, Yang JR (2013) Analysis and prediction of highly effective antiviral peptides based on random forests. PLoS ONE.

  31. 31.

    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.

  32. 32.

    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

  33. 33.

    Salmaso V, Moro S (2018) Bridging molecular docking to molecular dynamics in exploring ligand–protein recognition process: an overview. Front Pharmacol 9:923

  34. 34.

    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

  35. 35.

    Larranaga P (2006) Machine learning in bioinformatics. Brief Bioinform.

  36. 36.

    Jenssen H, Hamill P, Hancock REW (2006) Peptide antimicrobial agents. Clin Microbiol Rev 19:491–511

  37. 37.

    Spänig S, Heider D (2019) Encodings and models for antimicrobial peptide classification for multi-resistant pathogens. BioData Min 12:7

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